Spatial data for modelling building stock energy needs : annex to the proceedings of the workshop : participants' contributions.
Date: 2016-02-16
Institutions: Joint Research Centre
Subjects: building industry, data collection, energy consumption, energy demand, energy efficiency, energy policy, research report

Spatial  Data  for  Modelling  Building 
Stock Energy Needs 

Annex to the Proceedings 
of the Workshop 

Participants' contributions 

Edited by  
Hans BLOEM, Ray BOGUSLAWSKI, Maria 
Teresa BORZACCHIELLO, Albana KONA, 
Giacomo MARTIRANO, Isabella MASCHIO, 
Francesco PIGNATELLI 

2015 

 EUR 27747

 
This publication is a Conference and Workshop report by the Joint Research Centre, the 
European Commission’s in-house science service. It aims to provide evidence-based scientific 
support to the European policy-making process. The scientific output expressed does not imply 
a policy position of the European Commission. Neither the European Commission nor any 
person acting on behalf of the Commission is responsible for the use which might be made of 
this publication.

JRC Science Hub
https://ec.europa.eu/jrc

© European Union 2015

JRC 99902

PDF 

ISBN 978-92-79-55082-9 

ISSN 1831-9424 

doi:10.2790/89997

Luxembourg: Publications Office of the European Union, 2016

© European Union, 2016

Reproduction is authorised provided the source is acknowledged.

How to cite this report: Bloem, H., Bogulawski, R., Borzachiello, M.T., Kona, A., Martirano, G., 
Maschio, I., Pignatelli, F.; Spatial data for modelling building stock energy needs: Annex to the 
Proceedings of the Workshop: Participants’ contributions; EUR 27747; 10.2790/89997.

All images © European Union 2015

The following report is an annex to the report entitled Spatial data for modelling building stock 
energy needs: Proceedings of the Workshop; ISBN 978-92-79-55083-6; ISSN 1831-9424; 
10.2790/331094.

 
 
 
The role of INSPIRE in the EULF Energy Pilot 

Giacomo Martirano – JRC, Unit H06 (External Consultant) 

Introduction 

This  paper,  prepared  for  the  workshop  “Spatial  data  for  modelling  building  stock  energy  needs”, 
focuses on the role of INSPIRE in the EULF Energy Pilot. It has been prepared in the form of questions 
and answers, in order to facilitate the discussions that will take place during the workshop with the 
invited experts. Some of the questions are still not answered, waiting to collect feedback during the 
workshop. 

What is INSPIRE? 

INSPIRE is the acronym of the Directive 2007/2/EC of the European Parliament and of the Council 
entered into force on the of May 15th 2007, establishing an Infrastructure for Spatial Information in 
the European Community. 

INSPIRE aims to support EU environmental policies, and policies or activities which may have an impact 
on the environment. It defines binding Implementing Rules (Commission Regulations) and non-binding 
Technical  Guidelines  for  the  interchange  of  spatial  data  through  interoperable  data  models  and 
services. 

INSPIRE is based on the infrastructures for spatial information established and  operated by the 28 
Member States of the European Union and it addresses 34 spatial data themes1. 

What is EULF? 

EULF  is  the  acronym  of  European  Union  Location  Framework2,  which  is  a  project  led  by  the  Joint 
Research Centre of the European Commission and is part of the Interoperability Solutions for Public 
Administrations  (ISA)  Programme3,  run  by  DG  Informatics  (DIGIT).  The  EULF  is  a  framework  of 
recommendations, guidance and actions to improve the way location information is used in all public 
services across Europe, targeting benefits for businesses, citizens and government in key areas of EU 
activity, such as Transport, Marine and Energy policy. The EULF builds on the spatial data infrastructure 
for Europe being implemented by INSPIRE. 

What is EULF Energy Pilot? 

The Energy Pilot is one of the three pilots (the other two dealing with Transport and Marine sectors, 
respectively) to test  the  concept  of  the  EULF. It started in the  last quarter of 2015,  based on the 
outcomes of a “pre-pilot”  feasibility study which are documented in the "Location data for energy 

1 http://inspire.ec.europa.eu/index.cfm/pageid/2/list/7 
2 Information about the EULF, including links to publications and key events, can be found at 

http://ec.europa.eu/isa/actions/02-interoperability-architecture/2-13action_en.htm 

3 Information about ISA is available at http://ec.europa.eu/isa/ and at http://ec.europa.eu/isa/library/isa-work-

programme/index_en.htm

efficiency policies" JRC Technical Report4. The feasibility study aimed to verify the potential for an 
effective application of spatial data to support the monitoring requirements of the different EU energy 
efficiency policies and initiatives, which include data from different sources and at different scales 
(building, district and national). 

Q.1 

At what extent can INSPIRE fill-in the data gap highlighted in the JRC Technical Report of the Energy 
Pilot Feasibility Study? Which kind of data potentially relevant for the Energy Pilot will be made 
available by INSPIRE and when? 

A.1 

It is important to focus on two aspects related to INSPIRE in the context of the Energy Pilot, in order 
to do not overemphasize INSPIRE expectations and benefits which are still not easily measurable, but 
at the same time to highlight the potentialities of current and future INSPIRE implementations in the 
Member States: 

•
•

fit for purpose of INSPIRE harmonized datasets with the Energy Pilot scope and objectives;
timing of availability of INSPIRE harmonized datasets.

Fit for purpose aspect 

In the above mentioned JRC Technical Report a number of candidate INSPIRE data themes have been 
identified as fitting for purpose with the data flows to be analysed/implemented in the Pilot itself, 
namely  Buildings,  Addresses,  Cadastral  parcels,  Utility  and  governmental  services,  Production  and 
industrial  facilities,  Energy  resources,  Atmospheric  conditions,  Land  Cover,  Statistical  Units, 
Population Distribution. 

The most relevant INSPIRE data theme is undoubtedly Buildings (BU), but some of the attributes of 
the  its  data  model  that  are  relevant  for  the  Energy  Pilot  scope  and  objectives  have  the  following 
characteristics: 

•

•

they  are  “voidable”5  and/or  optional  in  the  binding-by-law  BU  core  data  model  (e.g.
dateOfConstruction and currentUse in Figure 1);
they  are  voidable  and/or  optional 
in  the  draft  BU  extended  data  model  (e.g.
energyPerformance,  heatingSource,  heatingSystem,  materialOfFacade,  materialOfRoof,
materialOfStructure in Figure 2).

Therefore, it can’t be expected that all data providers will deliver datasets containing this information. 

4 http://publications.jrc.ec.europa.eu/repository/handle/JRC96946 
5 The «voidable» stereotype is used to characterise those properties of a spatial object that may not be 
present in some spatial data sets, even though they may be present or applicable in the real world. 

Figure 1: BU Core 2D data model 

Figure 2: BU Extended Base data model 

An  initial  mapping  exercise  has  been  carried  out  (in  the  Feasiblity  Study  and  documented  in  the 
Technical Report) between the building related elements of the data models required by EPBD6 and 

6 Energy Performance of Buildings Directive - http://eur-lex.europa.eu/legal-
content/EN/ALL/;ELX_SESSIONID=FZMjThLLzfxmmMCQGp2Y1s2d3TjwtD8QS3pqdkhXZbwqGwlgY9KN!2064651
424?uri=CELEX:32010L0031 

 
 
                                                             
the corresponding elements present in the INSPIRE data specification on Buildings7. The main outcome 
of  this  mapping  exercise  is  that  the  building-related  data  models  required  by  EPBD  and  CoM8  are 
semantically  richer  than  the  data  models  under  INSPIRE.  For  instance,  Figure  3  shows  the  UML 
representation  of  the  element  of  the  INSPIRE  Buildings  Base  Extended  data  model  (Feature  Type 
BuildingAndBuildingUnitInfo) which deals with energy related aspects. 

Figure 3: INSPIRE Feature Type BuildingAndBuildingUnitInfo 

From this figure it is evident that: 

• 

• 

• 

• 

there are 4 boolean attributes which can be used just to store the information if the building 
(or  building  unit)  is  connected  or  not  to  the  different  networks  (electricity,  gas,  sewage, 
water)9; 
there is one attribute (energyPerformance) which can be used to store the information related 
to the energy label; 
there is one attribute (heatingSource) which can be used to store the information related to 
the source of energy used for heating (i.e. electricity, naturalGas, etc.); 
there is one attribute (heatingSystem) which can be used to store the information related to 
the system of heating (i.e. stove, central heating, heat pump, etc.). 

Conversely, there are additional attributes required by the EN 15603 standard (which the EPBD data 
modelling  requirements  are  based  on),  like  “Energy  need”  for  heating,  cooling,  ventilation,  air 
conditioning, domestic hot water, lighting, appliances, which do not match with any attribute existing 
in the current INSPIRE data model for buildings. 

In  general,  because  not  all  the  elements  of  the  data  models  required  by  EPBD  and  CoM  have  a 
corresponding  element  in  the  relevant  INSPIRE  data  model,  these  elements  with  a  missing 
correspondence with INSPIRE need to be further analysed in order to proper address an extension of 

7 http://inspire.jrc.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_BU_v3.0.pdf 
8 Covenant of Mayors – http://www.covenantofmayors.eu/  
9 It is worth noting the absence of a similar boolean attribute to identify buildings connected to district heating 
network  

 
                                                             
the existing relevant INSPIRE data models. It is also worth highlighting that the extension of an existing 
INSPIRE data model is subject to precise rules and conditions, set to ensure that it does not break the 
interoperability of harmonised data and services10. 

Citygml Energy ADE11 initiative and GeoSmartCity12 project are producing results whose reuse in the 
Energy Pilot is encouraged/recommended. 

Timing 

According to the INSPIRE Implementation Roadmap shown in Figure 4, INSPIRE conformant datasets 
which are newly collected or heavily restructured will have to be available by October 21st 2015, whilst 
all BU datasets (i.e. also the existing datasets to be transformed) will have to be available by October 
21st 2020. 

Therefore, because most of the BU related datasets to be made conformant to INSPIRE are existing, 
and consequently fall in the second deadline, only few INSPIRE conformant BU datasets are expected 
to be available before 2020 (mainly those coming from pilot projects/initiatives). 

10 INSPIRE Generic Conceptual Model - 
http://inspire.ec.europa.eu/documents/Data_Specifications/D2.5_v3.4.pdf 
11 http://en.wiki.energy.sig3d.org/index.php/Main_Page 
12 https://themes.jrc.ec.europa.eu/discussion/view/61352/extended-bu-data-model-for-energy-efficiency 

 
 
 
                                                             
Figure 4: INSPIRE Implementation Roadmap 

Despite  the  two  aspects  above  described  highlight  that  INSPIRE  benefits  supporting  the  Energy 
Efficiency policies lifecycle are not yet tangible, it’s important to highlight the potentialities of current 
and future INSPIRE implementations in the Member States, assuming that some recommendations 
are followed. 

Q.2 

Is  it  possible  to  formulate  recommendations  to  better  address,  in  general,  on-going  and  future 
INSPIRE implementations potentially useful to support Energy Efficiency policies lifecycle? 

 
 
 
 
 
A.2 

Yes. A first draft list of recommendations is provided below: 

• 

• 

• 

to properly extend existing INSPIRE BU data models in order to take into account the data 
modelling  requirements  coming  from  Energy  Efficiency  policies,  considering  both  existing 
similar activities (e.g. citygml Energy ADE initiative and GeoSmartCity project) and the rules 
for INSPIRE Data Specifications extension13; 

to  use  these  extended  data  models  as  target  data  models  in  as  many  as  possible  data 
harmonization  processes  related  to  Energy  Efficiency  policies,  in  order  to  improve  data 
interoperability at EU level; 

to  carefully  compile  relevant  metadata  elements,  such  as  lineage  and  resource  locator,  in 
order to document and share relevant data processing activities and therefore facilitate their 
reuse. 

Q.3 

Are  3D  data  of  buildings  really  needed  or  the  semantic  enrichment  of  2D  data  models  will  be 
sufficient to satisfy the data modelling requirements contained in the Energy Efficiency policies? 

A.3 

A detailed cost-benefits analysis should be made. 

Q.4 

Is  it  worth  to  design  a  common  data  model  to  harmonise  the  collection  of  data  coming  from 
heterogeneous devices providing energy consumption dynamic measurements? 

Q.5 

How to overcome the barrier related to the non-disclosure of energy consumption data classified as 
commercial sensitive by Utility Companies? 

Q.6 

At what extent the use of INSPIRE can be beneficial to implement the data-flows relevant to the 

13 INSPIRE Generic Conceptual Model - 
http://inspire.ec.europa.eu/documents/Data_Specifications/D2.5_v3.4.pdf 

 
 
 
 
 
 
 
 
                                                             
data Energy Efficiency policies lifecycle? 

A.6 

Possible alternative scenarios are shown in the Figure 5, in which the following acronyms are used: 

BU = Buildings 

EP = Energy Performance 

BEI = Baseline Emission Inventory 

NEEAP = National Energy Efficiency Action Plan 

A  draft  list  of  possible  alternative  scenarios  depending  on  different  combinations  of  data-flows  is 
provided below: 

1.  A vs. B (where A = A1+A2+A3) 

2.  D vs. (A+F) 

3.  (A+F) vs. (A1+A2+A4) 

4.  E vs. C (where C=C1+C2+C3) 

5. 

I vs. (C1+C2+C4) 

6.  J vs. (A+G) 

7.  (C+H) vs. (C1+C2+C4) 

 
 
 
BU related 
datasets
(non INSPIRE 
compliant)

Non-BU related 
datasets
(non INSPIRE 
compliant)

C1

A1

B

I

D

J

INSPIRE data 
harmonization

A2

C2

A3

BU related 
datasets
(INSPIRE compliant)

Non-BU related 
datasets
(INSPIRE compliant)

E

EPBD
(EP of public BU, new BU, 
BU sold/rent)

G

EED
(NEEAP: BU + non-BU)

A4

C3

F

CoM
(BEI of all BU + other non-BU 
sources of emission + sources of 
energy production)

H

C4

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Workshop: Spatial data for modelling building stock energy needs, JRC Ispra 

Contributed text: A win-win strategic approach in support of energy-related spatial data 
collection 

Contributor: Alexander Deliyannis, Sympraxis Team 

INTRODUCTION 
The JRC Feasibility Study "Location data for buildings related energy efficiency policies", 
undertaken under the European Union Location Framework (EULF) Project, concludes, among 
others, that 

—  it is possible to apply a generalised methodology to support different energy efficiency 

policies using location data as an integrating factor and combining both real and 
extrapolated data to indicate progress in meeting efficiency targets and help in planning 
relevant actions; 

—  geospatial technologies in general and accurate location data in particular can play an 

important role in the energy efficiency field, significantly increasing: 
(cid:131) 

the efficiency of data collection, elaboration and communication processes in all phases 
of the life cycle of energy efficiency policies; 
the effectiveness of decisions taken by different stakeholders (policy-makers, 
technicians, citizens) 

(cid:131) 

The Study proposes a methodological approach, hierarchically structured with relevant data 
collection levels 

—  Building level: Energy Performance of Buildings 
—  District level: Energy Efficiency Directive 
—  Urban level: Covenant of Mayors 
—  National level: Energy Efficiency Directive 

and a corresponding Conceptual Architecture to support the methodology. 

The present contribution focuses on a complementary approach supporting the proposed 
methodology, by suggesting additional incentives for data collection at the various levels. 

Furthermore, brief reference is made to two other complementary approaches, namely: 

—  Integrating iterative processes at the various levels, allowing for the use of mixed data 

(measured, calculated, default values or estimates) and the gradual improvement of data 
quality and precision as the systems are utilised. 

—  A possible role for big data techniques to compensate for the lack of available data in certain 

cases, as well as to recognise patterns of energy consumption and savings potential. 

 
 
 
 
 
 
THE 'WHYs' and 'WHOs' OF BUILDING ENERGY DATA COLLECTION 
Motivations and structures for data gathering may differ significantly at the various locational 
levels, i.e. building / district / urban / national. The following paragraphs look at the various 
levels and potential corresponding key actors. The latter may differ from the main actors 
identified in the Feasibility Study; in such cases the intention is to highlight the potential for, 
among others: 

—  alternative sources of data; 
—  stakeholders 'lobbying' the actors identified in the Feasibility Study, in order to collect and 

provide the relevant data. 

Building level: citizens / consumers / building managers, owners and tenants 
Citizens / consumers / building managers, owners and tenants may have several motives and 
opportunities to provide building energy data. Examples include the issuance of Energy 
Performance Certificates (EPCs) for buildings or dwellings, and private initiatives to reduce 
financial costs by improving their homes' energy performance. In practice, these two activities 
are often combined, with the EPC being accompanied by customisable suggestions for energy 
improvements. 

A multitude of tools are available nationally or EU-wide to facilitate such activities. Many of these 
are desktop applications, but web-based tools, such as the Greek easykenak (www.easykenak.gr) 
are of particular interest. Similarly, the UK Energy Saving Trust's Home Energy Check 
(http://hec.est.org.uk/) provides users with a report on their home's energy use and the savings 
that are possible by following specific steps, such as improving the insulation and installing new 
heating equipment. 

The use of online tools for EPC calculation, combined with related activities such as inspections, 
allows the gradual aggregation of relevant building stock data into centralised databases. The 
German CO2-online heating atlas (www.buildup.eu/tools/8972) illustrates the possibilities: it 
aggregates data from 50.000 buildings, presents it in a graphical interface, and allows users to 
access data from selected federal states and regions. It is worth noting that CO2-online has data 
on one million buildings, but has only published the data for which it was explicitly granted the 
right by owners/tenants. 

Another approach encouraging voluntary data contribution by users is demonstrated by the 
social/gamified Social Electricity application (www.social-electricity.com) available for the EU 
and Singapore. This uses social networks and gamification to encourage energy information 
sharing and improvements. Similar applications have been developed by national energy agencies 
in various Member States, e.g. ControlaEnergia (http://app.energythatmatters.info) allows users 
to register their gas and electricity consumption and receive personalised energy advice, as well 
as comparison with their past consumption and that of other users under similar conditions. 

All of these tools empower the end users and building occupants who constitute the 'managers' for 
the majority of residential buildings. Similar tools are available for non-residential buildings, e.g. 
NL Agency's Energy Explorer Utility (www.buildup.eu/tools/28424) which focuses on 
refurbishment and covers non-residential buildings, including offices, warehouses and various 
shops. Correspondingly, offering such tools to users can open a gateway for aggregation of real 
building energy data. This is particularly powerful where the collection itself is automated, i.e. 
via a Building Energy Management System; see the example of Intelen further below. 

Several other building energy-related data tools available to the various locational levels are 
presented in the BUILD UP overview article “Data tools supporting improved energy performance 
of buildings: Evolution and perspectives” (pt.1 www.buildup.eu/news/45030, pt.2 
www.buildup.eu/news/45869) 

Intermediate level: Neighbourhoods 
Neighbourhoods have not been included in the Feasibility Study as a separate level, yet they 
represent a significant potential for synergistic energy-related improvements whose benefits can 
be immediately apparent to citizens. As such, neighbourhoods can provide strong incentives for 
energy data contribution to central databases. 

Neighbourhood energy data can be aggregated through tools aimed for buildings and support 
energy planning at a level where joint procurement can be implemented with major benefits. A 
case in point is the initiative for Energy Positive Neighbourhoods explored by the FP7 IDEAS 
project. A recent BUILD UP webinar (www.buildup.eu/news/46501) explains the concept and 
potential. 

There is also a benefit in adding a level of data input (and complexity) in respect to centralised 
databases: information granularity. As noted in the Feasibility Study, “EED reporting 'Districts' are 
not related to standard administrative units (NUTS)”. Neighbourhood-level data can help 
compensate for this disharmony. 

District level: local councils 
Local councils and other local (sub-municipal) formal or informal administration structures, 
including among others local citizens' associations, can play a significant role in energy data 
collection. Their practical incentive: the resolution of energy-related local issues at their own 
level. 

Here again, tools such as those described above are readily available and can easily be used, e.g. 
to share and compare energy data between dwellings, buildings and building units in the same 
area, leading to valuable insights on potential improvements. Most importantly, such 
improvements may include district-level initiatives, including the management of open spaces, 
tree coverage, cool materials, traffic, and even district heating and cooling, etc. 

Urban level: Municipalities and regions 
At the urban level, cross-correlation of energy and (external) conditions data among similar 
districts can support the establishment of relevant benchmarks, highlighting potential issues as 
well as the potential urban-level solutions. 

A case in point is the mapping of urban heat islands, which constitute an energy intensity vicious 
circle, i.e.: the heat island undermines the efficiency of air conditioning systems, systems are 
required to operate at higher intensities, in turn further contributing to the heat island effect 
outside of buildings. Targeted municipal/regional action such as cool materials, cool roofs, 
increased tree coverage and traffic management can significantly reduce energy consumption and 
improve citizens' well-being. 

National level: Member States 
The Member State represents the higher direct interest level where collected data can readily be 
used to support decision making and interventions leading to higher energy efficiency. The 
potential has been extensively explored, among others, by the Concerted Action EPBD. 

Catalysts: corporations and SMEs 
Operational collection of data at the distinct levels can be supported to a great extent by 
commercial offerings, directly delivering the benefits back to consumers, local administrations 
etc. Energy retailers constitute the most evident player in this regard, but a much broader range 
of energy data inputs can be exploited with the contribution of other companies, including SMEs, 
focused on more specific markets. 

Intelen's DiG platform (http://intelen.com/us/solutions/dig.html) is an example of a 
local/central approach exploiting this potential: it allows energy users to monitor their own 
consumption –in full detail if they have installed smart plugs– and energy utilities to gain insights 
into their clients' consumption patterns. 

POTENTIAL ALLIES 
The following organisations and initiatives are noted for possible consideration within the context 
of the pilot implementation, as well as the long term promotion of the collection and processing 
of INSPIRE-compliant energy-related locational data. 

European Environment Agency (EEA) 
In respect to the overall EULF approach, an organisation with substantial experience in 
integrating (location and non-location) data is the European Environment Agency 
(www.eea.europa.eu/data-and-maps). Such experience ranges from integrated pan-European 
work such as the monitoring of bathing water quality, to voluntary citizen-science initiatives such 
as LitterWatch. 

Concerted Action EPBD (CA EPBD) and Concerted Action EED (CA EED) 
The Concerted Actions for the EPBD and EED bring together the national focal organisations 
responsible for the respective directions and can provide valuable fora for the promotion of the 
EULF priorities and good practices. 

COMPLEMENTARY SUGGESTIONS 
Iterative approach 
It is quite reasonable to assume that ‘data gaps’ will be found in the implementation of various 
steps/stages of the location data methodology, possibly already during the pilot phase, but most 
probably within the context of wider applications. It is proposed to adopt a flexible approach, 
whereby mixed data (measured, calculated, default values or estimates) may be used, with non-
measured values compensating for the lack of measured data, at least temporarily. Results can 
then support, among others, iterative applications (e.g. modelling), whereby newly measured 
data can then be used to improve the accuracy of further estimations.  

A role for big data 
So-called ‘big data’ techniques have recently uncovered interesting correlations between 
seemingly unrelated data, which can support useful estimation of missing parameters. A 
popularised example is the prediction of income level from Twitter postings 
(www.upenn.edu/pennnews/twitter-behavior-can-predict-users-income-level); similar work exists 
for location data, and could be applied for energy-related parameters. 

 
 
Data availability on energy 
consumption in buildings  
Overcoming gaps for the European Building Observatory 

November 2015 
Buildings Performance Institute Europe 
Maarten De Groote 
Francesco Mariottini 
Aleksandra Arcipowska 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

1 

2 

3 

4 

Introduction on the Building Observatory ..................................................................................................... 3 

Data collection methodology ......................................................................................................................... 4 

Results of data availability exercise................................................................................................................ 5 

The importance of data collection: BPIE response to the EPBD survey ......................................................... 7 

Need for harmonised methodologies................................................................................................................. 7 

Monitoring of building renovations.................................................................................................................... 7 

Enforcement of EPC databases........................................................................................................................... 8 

Strengthening of Energy Performance Certificates (EPC)................................................................................... 8 

Annex ...................................................................................................................................................................... 9 

Data availability from the Building Observatory and energy consumption in buildings 

page 2 

 
 
 
 
 
 
•   

1  INTRODUCTION ON THE BUILDING OBSERVATORY 

The  Building  Performance  Institute  Europe  coordinates  the development  of  the  “Observatory  of  the  EU 
Building Stock” and related key policies, contracted by the European Commission. Other consortium partners 
are  the Energy  Research  Centre  of  the  Netherlands,  Ecofys,  ENERDATA  and  SEVEn.  In  addition,  there  are  
18 subcontractors in the project representing all 28 EU MS. 

The objective of the assignment is to set the methodology for the EU Building Stock Observatory and to gather 
good quality data on energy efficiency of the building stock across EU‐28.. The work involves consultation with 
Member States’ authorities and agencies responsible for the building sector, including the statistics offices in 
charge of collecting data from the national building stock. 

The template for data collection consists of about 250 indicators. The list of key indicators for the Observatory, 
based on a clearly defined methodology, includes (and is not limited to) the following areas: 

‐ 

‐ 
‐ 
‐ 

‐ 

Characteristics of the building stock and energy consumption (e.g. floor area, number of buildings per 
type, use, age,etc.). 
Characterisation of buildings’ energy performance (e.g. U‐values, , technical building systems, etc.). 
Certification (e.g. number of energy certified buildings, other certification schemes, etc.) . 
Financing (e.g. level of investment associated with deep retrofits and with major renovation, financial 
schemes targeting deep renovation, etc.). 
Social issues and fuel poverty 

The public domain of the EU Building Observatory, will be launched at the beginning of 2016. It will provide a 
comprehensive  knowledge  resource  for  policy  makers,  investors,  industry  stakeholders,  energy  utilities,  local 
and  national  authorities  and  researchers  to  underpin  decision  making,  financial  and  long‐term  strategic 
support. 

The Building Stock Observatory will support monitoring of the EPBD implementation, as well of relevant articles 
of the EED (such as art. 4 and 5) and of the RED (such as art. 13 and 14) at national and regional levels.  

Data availability from the Building Observatory and energy consumption in buildings 

page 3 

 
 
 
 
 
 
 
 
2  DATA COLLECTION METHODOLOGY 

In  the  scope  of  the  project,  the  list  of  the  indictors  (i.e.  definitions  and  data  needs)  has  been  proposed, 
discussed with various stakeholders and finally agreed with the European Commission. 

Step 1: 
Horizontal 
data sources 
(projects, 
databases and 
literature) 

Task 2.2 

Step 2: Cross-
country data 
experts 
(REHVA, VITO, 
industry 
associations) 

Step 3: 
National 
sources 
(country 
experts) 

Task 2: Country data templates 

Task 1.3: Data 
collection 
methodology 
and 
framework  

Task 2.1: 
Template and 
format for 
data 

Task 3: 
Preparation 
of 
approaches 
and policy 
evaluation 
templates for 
Task 4  

Task 4: 
Building 
Stock 
Observatory 
web tool  

Step 4: Data 
verification and quality 
control 

Task 2.3: Identification 
and filling data gaps 

Task 3.3: Action plan 
to fill data gaps 

Figure 1 ‐ Methodology of data collection. 

Following the project methodology, the data‐collection process is conducted in four phases:   

‐ 

STEP 1: Data collection for indicators based on horizontal sources. The consortium focused on: 

o  Existing horizontal sources with EU coverage.  These are sources like Eurostat, IEA, Odyssee‐
Mure, Entranze, Episcope, iServe, BPIE Data Hub, EU Comm’s Market Observatory and others.  
o  Existing  literature,  projects  and  periodic  reporting  of  the  EU  Member  States  (MS)  such  as 
CA‐EPBD  reports,  studies,  EUR’Observer  periodic  reports,  NEEAPs,  NREAPs,  reporting  under 
the  EED,  Ecodesign  preparatory  studies,  EU  Commission  initiatives  and  portals  such as  RES‐
Legal, etc.  

‐ 

‐ 

‐ 

STEP 2:  Cross‐country data provided  by  experts  and stakeholders   The  two  main  subcontractors  of 
the  project,  i.e.  REHVA  and  VITO,  have  filled  in  cross‐country  EU  data  regarding  HVAC  equipment, 
lighting, renewable energy sources and indoor air quality. 
In addition, 15 stakeholders active in the field of energy efficiency in the built environment have been 
contacted.  
STEP 3: Data collection based on national data sources. There are 18 subcontractors in the project, 
whose role is to provide data available from the national sources.   
STEP  4:  Data  verification,  quality  control  and  selection  of  data.  Data  sets  will  be  rejected  if  the 
reliability verification does not provide satisfying outcomes. 

Data availability from the Building Observatory and energy consumption in buildings 

page 4 

 
 
 
 
 
 
 
 
 
3  RESULTS OF DATA AVAILABILITY EXERCISE 

Prior  to  data  collection  process,  the  consortium  conducted  an  in‐depth  analysis  of  data  availability  both  on 
national  and  European  level.  The  results  of  the  data  availability  for  the  indicators  related  to  building  energy 
performance (only) are the presented as follows:  

Building  stock  characteristics. While  data on   building  stock characteristics  are  relatively  easy traceable  (e.g. 
IEE ODYSSEE‐MURE project, BPIE Data Hub, INSPIRE project), data for indicators on (energy) renovation are the 
most  challenging  to  collect  mainly  because  of  lack  of  harmonisation  on  the  definitions.  Some  information  is 
provided by IEE EU projects such as ZEBRA2020 and EPISCOPE.   

Energy    consumption.  Data  coverage  on  total  final  energy  consumption  is  rather  good  although  less 
information is available for non‐residential buildings. The new initiative by Eurostat (Commission Regulation No 
431/2014), will provide in the future a good source of data on energy consumption in the residential sectors; 
data for some countries is already available in the source. 

Technical  data  on  energy  performance,  disaggregated  into  building  envelope,  on‐site  renewable  energy 
generation, technical systems and embodied energy: 

•  Building envelope. In majority of MS, there are no official statistics on U‐values; some data is available 
from  European  (e.g.  ENTRANZE  and  INSPIRE  projects)  or  national  sources.  Gaps  concern  also 
airtightness. 

• 

• 
• 

•  On‐site renewable energy generation.  Data of biomass and heat pumps is to some extend available 
and  collected  annually  by  Eurostat  and  EurObser’ER  database,  while  information  on  PV  is  only 
available on national level and is not specified into residential and non‐residential installations. 
Technical systems – Space heating. There are large gaps on this indicator and information on average 
efficiency and age of systems is difficult to obtain. High‐level data is only available in some countries 
(provided  by  ENTRANZE  and  TABULA  projects).  In  order  to  fill  the  identified  gaps  contacts  with 
industry (associations) are being set up. Stakeholder, such as REHVA EHI, EHPA are supporting  data 
collection for the technical systems; 
Technical systems – Space cooling.  There is little data to be found on space cooling.  
Technical systems – Domestic Hot water.  Compared to data on space heating and cooling, horizontal 
data  on  domestic  hot  water  is  more  scarce.  However,  on  national  level  about  half  of  the  countries 
have data on the number of building (units) with boilers. 
Technical  systems  –  Ventilation.  For  data  on  Ventilation,  the  preparatory  studies  for  Lot  10  of  the 
Ecodesign can be consulted. On the national level, data on ventilation is not often available. 
Technical systems – Lighting. Data on lighting is available in the Melisa database, developed by Van 
Holsteijn & Kemna on EU level. 
Technical systems – Cooking and electric appliances.  Horizontal data for Cooking Appliances is not 
easily  available;  Data  on  appliances  are  available  in  most  of  the  countries  in  the  ODYSSEE‐  MURE 
database. 
Technical systems – Metering.  There is little data to be found on metering systems   
Embodied  energy.    Embodied  energy  is  a  topic  for  which  hardly  any  monitoring  is  done  and  it  is 
unlikely that empirical datasets can be found on this topic. 

• 
• 

• 

• 

• 

Certification  –  Distribution  of  EPCs.  National  and  regional  EPC  databases  are  the  most  important  source  of 
data  on  the  EPC  distribution,  and  some  of  them  have  been  analysed  within  ZEBRA2020  project.  Regarding 

Data availability from the Building Observatory and energy consumption in buildings 

page 5 

 
 
 
 
 
 
 
 
quality and compliances of EPC schemes, important sources are Concerted Action EPBD, the national reports 
on Energy Performance Certificates and the inspections of heating and air conditioning systems (requested by 
the European Commission).  

The results of the data availability for the indicators related to building energy performance are presented in 
the annex. 

Data availability from the Building Observatory and energy consumption in buildings 

page 6 

 
 
 
 
 
4  THE  IMPORTANCE  OF  DATA  COLLECTION: BPIE  RESPONSE  TO 

THE EPBD SURVEY 

The availability and reliability of the building data vary across Member States; data collected by the Eurostat in 
this regards are rather limited. 
As stated in the previous chapter, many gaps have been identified during the data collection process of the “EU 
Buildings Stock Observatory project”. Thus, the potential to change and improve the status quo relies also on 
the  launching  of  the  mandatory  data  collection  on  energy  consumption  in  housing  sector  by  Eurostat  and 
additional targeted European and national policies. 
There  is  a  need  to  consistently  improve  the  enforcement  of  the  EPBD  provisions  in  Member  States  and 
strengthen  the  monitoring  of  the  compliance  both  at  Member  State  and  European  levels.  For  instance,  only 
50% of the MS have a view on the compliance rates of new buildings with energy performance requirements, 
which is highly problematic. It is a major barrier for policy making (on European, National and Regional level) 
and it is harmful for the credibility to citizens. 

The European Commission organised a public consultation on the EPB directive, running from June till October 
2015.  The  public  consultation  launched  the  review  into  the  directive,  which  is  due  by  the  end  of  2016  as 
required under article 19 of the directive. The online survey consists of 79 questions, covering various topics 
(such as renovation existing buildings, smart buildings, energy poverty, etc.). BPIE responded to this questions 
and emphasized in some answers the need for public and harmonised data availability to support European, 
National and local policy making. 

Need for harmonised methodologies 

The findings of the EPISCOPE project highlight a strong need for better harmonisation of national and official 
statistics  related  to  building  performance.  While  some  types  of  data  (i.e.  surface)  are  easier  to  harmonise, 
public information on systems and technologies are usually too peculiar and country specific. 
EPCs require a number of input data that depend on the application (e.g. residential versus non‐ residential, 
new  versus  existing  building)  and  on  the  country  or  region1.  This  is  also  due  to  the  fact  that  the  majority  of 
Member  States  use  their  own  energy  performance  calculation  methodologies  and  not  the  available  CEN 
standard. 
There is a need to secure sufficient resources (i.e. financial & human) for the data collection, verification and 
quality control,  both  on  the European and national  level.  European  Commission  should  provide guidance  for 
the Member States  in  regards  of  the  type and  format  of the data  for  monitoring  of the  improvement  of the 
energy efficient in buildings, including non‐residential sector.  The links between EPBD and CEN standardisation 
should also be further strengthen. 
More  convergence  on  methodologies  and  targets  would  trigger  more  innovation  in  the  field  and  support  a 
technology leadership of Europe in the field. 

Monitoring of building renovations 

To date, information on the type and level of the renovation activities is very limited. A valuable learning in this 
regards could be taken from the ongoing EU projects, such as EPISCOPE, Renovalue etc., or national initiatives, 
such as the English Housing Survey. Adequate monitoring measures need to be applied on the EU and MS level, 
especially in case of public financing (including Cohesion Funds). 

1 Even within the same country (e.g. Italy, Austria and Belgium), different approaches are observed. 

 
                                                                 
Serious  consideration  should  be given  to oblige  MS  the  obligation to  report  on  renovation  market activity  in 
their NEEAPs.   
In addition there is the need for stricter and more harmonised definitions on the rate and depth of renovation 
(i.e. deep renovation, light renovation, rate of renovation and more). A distinction needs to be made between 
general renovation for aesthetic purposes and energy related renovations, since the first does not necessarily 
contribute towards the aims of the EPBD and has the potential to distort the reality of reported progress. 

Enforcement of EPC databases 

EPC  databases  are  the  central  source  of  information  on  the  energy  performance  of  the  EU  building  stock.  
Unfortunately, some MS don’t have a central database and in case they do, databases are mostly not public 
accessible. 
The  EC  should  impose  the  Member  States  to  gather  all  information  on  EPCs  in  national  databases,  with  the 
purpose  of  ensuring  monitoring  and  compliance,  and  make  (a  selection)  of  data  available  on  a  central  EU 
database. Therefore, there is an important role for the EC to support the Member States in the development 
and  strengthening  of  central  EPC  registers,  especially  in  the  context  of  a  solution  to  tackle  the  private  data 
issues,  and  tools  for  data  analysis.  The  experience  of  different  Member  States  regarding  the  systems  of 
monitoring, compliance and quality check has to be examined and standardised methodologies formats of data 
gathering, according to best practices should be promoted. 
There  is  need  for  guidance  on  the  development  of  centralised  EPC  registries,  not  only  to  support  the 
independent  control  system,  but  as  a  tool  to  map  and  monitor  the  national  building  stock.  Therefore,  the 
European  Commission  should  provide  further  recommendations  and  enable  the  exchange  of  best  practices 
towards functional EPC databases (i.e. methods for data collection and analysis and protocols for data sharing). 
Embedding  the  role  of  EPC  and  EPC  registers  into  national  refurbishment  policies  will  be  the  best  driver  to 
improve and sustain the EPC system over time. 

Strengthening of Energy Performance Certificates (EPC) 

In  order  to  ensure  compliance,  every  energy  efficiency  upgrade  should  be  logged  through  an  EPC  or  at  the 
building’s passport by an independent assessor, especially if the works are supported by public (both national 
and European) funds for buildings refurbishment. Standard formats to document the input data and to report 
the results of energy calculations make the EPC input data and results documentation transparent (e.g. Estonia, 
Belgium, France), and thereby facilitate compliance checks. 
EPCs have also the potential to become “building passports” accompanying a building through its life cycle and 
include individual renovation roadmap (e.g. improvement proposals and energy renovation activities in a step‐
by‐step  approach)  plus  additional  relevant  information  such  as  maintenance  and  reparations,  involved 
(qualified) building professionals, energy consumption, thermal comfort and indoor air quality aspects. 
Indeed,  in  order  to  become  useful  in  individual  buildings’  improvement  plans,  EPCs  should  evolve  towards 
more  comprehensive,  dynamic  tools  accompanying  a  building  over  its  lifetime.  Given  the  current  and  future 
ICT‐prospects, this ‘building passport’ should be digitalised, which would facilitate the further development of 
available centralised databases. 

Data availability from the Building Observatory and energy consumption in buildings 

page 8 

 
 
 
ANNEX 

The following tables summarise the data availability for some of the indicators and the key data sources that 
have been identified for 26 countries within the EU Buildings Stock Observatory project, with a special focus on 
energy performance of buildings. 2 

Legend: 

rather good access to data

data is available only partially

low data availability/expect difficulties

data not available

2  For  example,  data  on  number  of  buildings  and  dwellings  (available  from  EUROSTAT)  have  been  collected 
within the project but are not showed in the following tables.  

Data availability from the Building Observatory and energy consumption in buildings 

page 9 

 
 
 
 
 
 
                                                                 
 
 
AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

T.1.1 Building stock statistics

Indicators

Number of buildings/building stock units

Number of dwellings

k

k

Building floor area (and average floor area)

km²

HORIZONTAL DATA 
AVAILABILITY

ODYSSEE

ODYSSEE

ODYSSEE

Building stock decomposition by building or 
dwelling type

Building stock decomposition by construction 
period

k,Mm²

k,Mm²

ODYSSEE, DATA HUB; 
EUROSTAT

ENTRANZE, BPIE DATA 
HUB;

Building stock decomposition by size

k,Mm²

Building stock decomposition by location

Building stock decomposition by climatic zone

Building stock decomposition by occupancy 
level

Building stock decomposition by ownership 
status

k

k

k

k

New construction 

Average energy performance of new 
construction

Renovation

Energy renovation

Deep energy renovation

Major renovation (EPBD definition)

Index, Mm²

kWh/m²

def.

k,Mm²

k,Mm²

k,Mm²

Wall insulation improved from the original state %

Improvements to at least thermal protection 
double glazing

%

Roof insulation improved from the original state %

Average performance level reached after 
renovation

kWh/m²

Costs of energy building renovation

€/m²

Demolished buildings

Rented out last year

Transactions last year

Stock of nZEBs

K

K

K

K

Average energy performance of nZEBs

kWh/m²

EUROSTAT

EUROSTAT

EUROSTAT

EUROSTAT

ODYSSEE/ZEBRA

ZEBRA2020

ZEBRA2020

EPISCOPE

EPISCOPE

EPISCOPE

ZEBRA2020

ZEBRA2020

ZEBRA2020

Table 1 ‐ Results of data collection on energy performance of buildings within the topic “Building stock characteristics” 

Data availability from the Building Observatory and energy consumption in buildings 

page 10 

 
 
 
 
AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

T.1.2 Energy Needs
Indicators

Final/primary energy consumption

Energy consumption for space heating

Energy consumption for space cooling

ktoe

ktoe

ktoe

Energy consumption for domestic hot water

ktoe

Water consumption 

Energy consumption for lighting

Energy consumption for appliances (including 
cooking)

Energy consumption for ventilation

Final energy consumption per end use per 
carrier (including for the space heating and 
domestic hot water)

Theoretical energy use

Mm3

ktoe

ktoe

ktoe

ktoe

ktoe

HORIZONTAL DATA 
AVAILABILITY

EUROSTAT

ODYSSEE (some) 
EUROSTAT (some)

ODYSSEE (some) 
EUROSTAT (some)

ODYSSEE (some) 
EUROSTAT (some)

EUROSTAT

ODYSSEE (some) 
EUROSTAT (some)

ODYSSEE (some) 
EUROSTAT (some)

ODYSSEE, mainly 
residentail

EPISCOPE

Table 2 ‐ Results of data collection on energy performance of buildings within the topic “Energy needs” 

AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

T.2.1 Envelope performance

Indicators

Air tightness

dm3/s/m2

HORIZONTAL DATA 
AVAILABILITY

Average U-value for overall building envelope

W/m2°C

EPISCOPE INSIRE

Average U-value of doors

Average U-value of external walls

Average U-value of floors

Average U-value of roofs

Average U-value of skylight

Average U-value of windows

W/m2°C

W/m2°C

W/m2°C

W/m2°C

W/m2°C

W/m2°C

EPISCOPE INSIRE/BPIE

EPISCOPE INSIRE/BPIE

EPISCOPE INSIRE/BPIE

EPISCOPE INSIRE/BPIE

EPISCOPE INSIRE/BPIE

Disaggregation of type of glazing

No. of 

Disaggregation of type of window frame

No. of 

Table 3 ‐ Results of data collection on energy performance of buildings within the topic “Envelope performance” 

AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

T.2.2 On-site renewable energy generation

Indicators

HORIZONTAL DATA 
AVAILABILITY

On-site renewable energy sources generation

Mtoe

Renewable electricity generation by PV panels

Mtoe

Renewable heat generation by biomass

Mtoe

Renewable heat generation by heat pumps

Mtoe

Renewable heat generation by solar

Renewable electricity generation by wind (small 
size turbines)

Conversion efficiency of the renewable energy 
technology used

Mtoe

Mtoe

%

Eurostat, Shares Tool

Eurostat, Energy 
Balances

Eurostat, Shares Tool

Eurostat, Shares Tool

2011/877/EU 

Table  4  ‐  Results  of  data  collection  on  energy  performance  of  buildings  within  the  topic  “On‐site  renewable  energy 
generation” 

 
 
 
 
 
 
T.2.3 Technical systems A) Space heating

Indicators

HORIZONTAL DATA 
AVAILABILITY

AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

ENTRANZE

BPIE 

TABULA / EC

TABULA / EC

TABULA / EC

EC

EC

EHPA

EHPA

ESTIF

Disaggregation of buildings according to heating 
device coverage to main and supplementary

Disaggregation of buildings according to heating 
device capacity

Disaggregation of buildings according to heating 
system level

Average efficiency rate for space heating

Buildings/building units with central steam/hot water 
space heating system

No.of

No.of

No.of

%

No.of

Buildings/building units with condensing boilers

No.of

Average efficiency rate of condensing boilers

Efficiency rate of the BAT condensing boilers

%

%

Buildings/building units with conventional boilers

No.of

Average efficiency rate of conventional boilers

%

Buildings/building units with combi boilers

No.of

Average efficiency rate of combi boilers

Efficiency rate of the BAT combi boilers

%

%

Buildings/building units with a built-in electric system

No.of

Buildings/building units with central air space heating

No.of

Buildings/building units with heat pumps

No.of

Buildings/building units with reversible heat pumps

No.of

Average efficiency rate of heat pumps

Efficiency rate of the BAT heat pumps

%

%

Buildings/building units with solar heating system

No.of

Buildings/building units with a stove

Buildings/building units with a fireplace

No.of

No.of

Buildings/building units with an electric storage heater

No.of

Buildings/building units with a portable kerosene / 
liquefied petroleum gas heater

Buildings/building units with a cooking stove

Disaggregation of space heating devices according to 
the energy source

No.of

No.of

No.of

Buildings/building units with heating on biomass – self 
produced

[No.of}

Buildings/building units with heating on biomass - 
purchased

Buildings/building units supplied with energy from heat 
and cold storage

Buildings/building units supplied with energy from on-
site CHP

Buildings/building units supplied with energy 
generated by OTHER technologies.

[No.of]

No.of

No.of

No.of

Disaggregation of heating system according to the age 
of the space heating equipment

years

Table 5 ‐ Results of data collection on energy performance of buildings within the topic “Technical systems ‐ Space heating” 

Data availability from the Building Observatory and energy consumption in buildings 

page 12 

 
 
 
 
 
NATIONAL DATA AVAILABILITY

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

HORIZONTAL 
DATA 
AVAILABILITY

ENTRANZE

EC

T.2.3 Technical systems B) Space cooling
Indicators 

Disaggregation according to space cooling equipment 
coverage 

No. of 

Buildings/building units with reversible heat pumps

No.of

Average efficiency rate of space cooling equipment

Efficiency rate of the BAT space cooling equipment

Age of the cooling system

%

%

years

Table 6 ‐ Results of data collection on energy performance of buildings within the topic “Technical systems – Cooling” 

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

T.2.3 Technical systems C) DHW
Indicators 

Buildings/building units with a water heater/boiler

Tank size 

Age of the tank

Buildings/building units with a combi boiler

Disaggregation of the water heating devices according 
to the main energy source

Buildings/building units with electric boiler

Buildings/building units with electric heaters (not heat-
pump)

Buildings/building units with other water heaters

Average efficiency rate of water heating equipment

Efficiency rate of BAT water heating equipment

No.of

litres

years

No.of

No.of

No.of

No.of

No.of

%

%

HORIZONTAL 
DATA 
AVAILABILITY

EC

EC

EC

EC

EC

EC

EC

Table 7 ‐ Results of data collection on energy performance of buildings within the topic “Technical systems – Domestic Hot 
Water” 

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

T.2.3 Technical systems D) Ventilation
Indicators 

Buildings/building units with mechanical ventilation (no 
heat recovery)

No.of

Buildings/building units with mechanical ventilation 
(with heat recovery)

No.of

Buildings/building units with natural ventilation

No.of

Average efficiency rate of ventilation equipment

Efficiency rate of BAT ventilation equipment

%

%

HORIZONTAL 
DATA 
AVAILABILITY

EC

EC

EC

Table 8 ‐ Results of data collection on energy performance of buildings within the topic “Technical systems – Ventilation” 

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

T.2.3 Technical systems D) Lighting
Indicators 

Share of incandescent lamps

Number of incandescent lamps in a dwelling

Share of TL

Number of TL

Share of CFL

Number of CFL lamps in a dwelling

Share of LED

Number of LED lamps in a dwelling

Share of halogen lamps

Number of halogen lamps in a dwelling

Share of Others

Number of other lamps in a dwelling

%

No.of

%

No. of

%

No.of

%

No.of

%

No.of

%

No.of

HORIZONTAL 
DATA 
AVAILABILITY

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

MELISA

Table 9 ‐ Results of data collection on energy performance of buildings within the topic “Technical systems – Lighting” 

Data availability from the Building Observatory and energy consumption in buildings 

page 13 

 
 
 
 
 
 
 
 
 
 
T.2.3 Technical systems F) Cooking
Indicators 

HORIZONTAL 
DATA 
AVAILABILITY

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

Disaggregation according to the cooking equipment No.of
Disaggregation of the cooking devices according to 
the energy source
T.2.3 Technical systems F) Electrical Appliances
Number of refrigerators in a dwelling

No.of

No.of

Number of freezers in a dwelling

Number of washing machines in a dwelling

Number of dryers in a dwelling

Number of dishwashers in a dwelling

Number of televisions in a dwelling

Number of computers in a dwelling

No.of

No.of

No.of

No.of

No.of

No.of

ODYSSEE

ODYSSEE

ODYSSEE

ODYSSEE

ODYSSEE

ODYSSEE

ODYSSEE

Table 10 ‐ Results of data collection on energy performance of buildings within the topics “Technical systems – Cooking” and 
Technical systems – Electrical Appliances” 

AT

BE

BG HR

CY CZ

DE

DK

EE

ES

FI

FR GR HU

IE

IT

LV

LT

NL

PL

PT

RO SK

SI

SE

UK

NATIONAL DATA AVAILABILITY

T.2.3 Technical systems H) Metering

Indicators

Individual or collective metering

Availability of a thermostat

Thermostat types

Share of buildings with smart metering systems

Feedback system for smart meters

T.2.3 Technical systems I. Others
Dwellings/buildings with shading devices

Dwellings/buildings with PV-panels

HORIZONTAL DATA 
AVAILABILITY

Eurostat

Eurostat

No.of

No.of

No.of

No. of 

No. of 

No.of

No.of

Eurostat

Table 11 ‐ Results of data collection on energy performance of buildings within the topics “Technical systems – Metering” 
and “Technical systems – Other” 

HORIZONTAL 
DATA 
AVAILABILITY

T.2.4  Embodied energy
Indicators 

Estimated embodied energy of new construction

Estimated embodied energy of deep retrofits

Estimated embodied energy of  major 
renovations

toe/dwelling or 
toe/building

toe/dwelling or 
toe/building

toe/dwelling or 
toe/building

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

Table  12  ‐  Results  of  data  collection  on  energy  performance  of  buildings  within  the  topic  “Technical  systems  –  Embodied 
energy” 

AT BE BG HR CY CZ

DE DK EE ES FI

FR GR HU IE

IT

LV LT NL PL PT RO SK SI SE UK

NATIONAL DATA AVAILABILITY

T.3.1. EPC distribution
T.3.1. EPC distribution

Stock with EPC

Distribution of EPC levels for existing 
buildings 

No of

No of

Distribution of EPC levels for new buildings

No of

Distribution of EPC level per building size

No of

HORIZONTAL DATA 
AVAILABILITY

ZEBRA 2020

ZEBRA 2020

ZEBRA 2020

Value of the buildings

Rent value

EPCs issued last year

EPCs displayed publicly

Energy label impact on property market

Energy label in commercial 
advertisements

Quality control of the EPCs

Penalties for non-compliance

Euro/m2

Euro/m2

No of

No of

%

%

-

-

ZEBRA 2020

EC

EC/CA EPBD

CA EPBD

Table 13 ‐ Results of data collection on energy performance of buildings within the topic “EPC Distribution” 

Data availability from the Building Observatory and energy consumption in buildings 

page 14 

 
 
 
 
 
 
 
 
 
 
Spatial data for modelling building stock energy needs
A UK perspective for the European Commission Joint Research Centre.

Steve Evans, Rob Liddiard. Philip Steadman 

Energy Institute. Central House, 14 Upper Woburn Place London, WC1H 0NN

steve.evans@ucl.ac.uk

Abstract

This paper describes the development of a 3D model of the non-domestic building stock of England 
and Wales. The model’s purpose is to assess energy use in the stock, and study conservation 
options. The model combines digital maps and property taxation data to build a 3D representation 
in which separate premises are located within buildings, with floor areas on each level. For part of 
London, energy data at the individual meter level has also been coupled with this spatial model, 
allowing us to analyse energy use at the individual building level and energy use intensities per unit 
of floor area.

Acknowledgements 

Funding for this research was provided by the Research Councils UK (RCUK) Centre for Energy Epidemiology, grant 
reference EP/K011839/1. Gas and electricity meter data was supplied by the Department of Energy and Climate Change 
(DECC).

Overview

Over the past 25 years spatial data and the use of Geographical Information Systems (GIS) has 
advanced from a little known research tool to a well known technology. More recently, with the 
arrival in car navigation and mobile phones equipped with Global Positioning Systems (GPS), the 
technology of GIS has gone way beyond the boundaries of what the early pioneers could have 
envisioned. In short, there is an abundance of spatial data compared to 25 years ago and the 
computing power available to analyse this data is no longer a barrier to these types of queries.

Despite this abundance of spatial data, when we try to model the activity within buildings the 
experience in the UK is that surprisingly little information is recorded consistently about the 
national building stock. This is despite the fact that some buildings remain in use for hundreds of 
years and if taken as a whole, the building stock is a high value national 'asset'.

If we combine the paucity of consistent,  detailed information about the building stock with what we 
know about how much energy is used at this same scale (i.e. building by building) then there are 
large gaps in this information.

Research at the UCL Energy Institute is attempting to resolve this. Some members of the team have 
a relatively long history of work in this specific area, dating back to the 1990s (Steadman et al 
2000). Significant breakthroughs have been achieved in the past 3 years with the full integration of a 
model of non-domestic floorspace with fine scale 3D GIS (1:1,250 scale) and the subsequent 
coupling of this stock model with energy meter data for individual properties and premises. We have 
called this model '3DStock'.

‘Premises’ and ‘buildings’

One of the first hurdles that is of fundamental importance when modelling any non-domestic 
building stock is the fact that 'buildings' may not equal 'premises' and 'premises' may not equal 
'buildings'. Being able to model this relationship between premises and buildings is a critical step 
towards creating a detailed representation of the building stock. A premises is an extent of floor 
space with a single owner or occupant. A building is more difficult to define – indeed in what 
follows we will use a somewhat different unit, as we will explain. In everyday language of course a 
‘building’ tends to mean a single structure, erected all at one time, in a homogeneous style and form 
of construction. In these terms, there can be different kinds of relationship of premises to buildings. 
Several premises can occupy a single building, as in the case of many tenants sharing one office 
building. One premises can coincide with one building, as is typically the case with churches. Or 
one premises can consist of many detached buildings on a shared site, as for example a secondary 
school, a university campus, or a large factory. With the domestic stock the situation is generally 
simpler. One ‘premises’ (the household) occupies one building (the house). In a block of flats on the 
other hand, many ‘premises’ (the flats) share a single building. There is a similarity with a multi-
tenant office block. In other cases single buildings can be shared by domestic and non-domestic 
premises, as with flats over shops.

Data from the Ordnance Survey and the Valuation Office Agency

The main structure of the 3DStock model is assembled by bringing together existing data from two 
national sources. The first source is the Ordnance Survey – the UK’s national mapping agency – 
specifically the Ordnance Survey Address Base (OSAB) and Mastermap digital map products 
(Ordnance Survey 2015a, 2015b). The second source is the Valuation Office Agency (VOA) of Her 
Majesty’s Revenue and Customs (Valuation Office Agency 2015). In Britain, property taxes (‘rates’) 
are levied on premises, not on land as in many other countries. For this reason the VOA makes 
detailed surveys of all those premises – or what it calls ‘hereditaments’ – that are subject to rates. 

The rating data for houses and flats are not publicly available. However the data for non-domestic 
hereditaments are released, in the form of two overlapping types of VOA records. The Rating List 
covers most hereditaments – with certain exceptions detailed below - giving the activities of the 
occupiers and the valuations on which the rates are paid. The Summary Valuation (SMV) database 
gives more detail for approximately 90% of these same hereditaments, including their floor areas. 
Here the ‘primary’ activity (e.g. ‘Commercial office’) is broken down into sub-activities (e.g. 
office, storage, kitchen, computer room etc). The database gives the floor areas devoted to these 
sub-activities on each floor level. The areas are measured according to different conventions, usually 
net internal area (NIA) or gross internal area (GIA). Analytical work carried out on VOA records 
and drawings in the 1990s showed that these measurements are very accurate, no doubt in part 
because they are open to challenge by ratepayers (Gakovic et al 1993). Both Rating List and SMV 
are updated continuously, with revisions and new entries prompted by applications for planning 
permission and building regulations approval.

Although the SMV database contains information on the majority of non-domestic hereditaments, 
there are several types of building that it does not cover. These include churches, central government 
offices, law courts and prisons. Some other types are included in the Rating List, for example hotels 
and pubs, but have no Line Entries or area data in the SMV. Information on floor areas for these 
types will have to come from other public and commercial databases. 

Matching digital map footprints to hereditaments

Valuation Office Agency hereditaments can be matched to map polygons representing building 
footprints, by their respective addresses. Ordnance Survey Address Base holds a link in many (but 
not all) cases to unique address reference numbers (UARNs) in the VOA Rating List. OSAB also 
enforces compliance by local authorities with the British Standard for the formatting of address 
information, BS7666 (British Standards Institution 2006), and it coordinates and enforces the 
maintenance of Local Land and Property Gazetteers. For cases where this link does not exist, we 
have developed a special module to clean and match addresses. This module first attempts to clean 
non-BS7666 addresses into something close to BS7666, and then uses a number of different 
methods to find the closest match within the OSAB database. For the Camden, one of our case 
study areas, a 98% match rate was achieved for the 2010 Rating List.

The OSAB data can then be cross-referenced to topographic map data providing building footprints 
and other ‘objects’ viewable on detailed maps. The most consistent data set for the whole of 
England and Wales is the Ordnance Survey Mastermap Topography layer (OSTopo), which has 
polygons for buildings, roads, rivers and railways (Ordnance Survey 2015c). For the buildings, the 
address data are attached to points within footprint polygons. In this way Valuation Office UARNs 
can be matched to building footprints.

In practice the picture is however more complex than this brief account would suggest. First, there 
can be more than one address point within one footprint polygon. Second, there can be several 
addresses attached to each point. Third, a single ‘building’ may be represented by a group of 
adjoining polygons, only some of which are addressed in OSAB. 

Taking these issues in order: the Ordnance Survey addressing can sometimes have a hierarchical 
structure, where there is just one unique property reference number (UPRN) for an address that 
might contain several hereditaments. For example a large office building (‘Central House’) might 
have a single UPRN, while a separate office hereditament in that building (e.g. ‘University College 
London, first floor, Central House’) might have a UARN, as might hereditaments on other floors. In 
other cases however a single building might have a ‘parent’ UPRN (‘Central House’), which in turn 
has related ‘child’ UPRNs (e.g. ‘University College London, first floor, Central House’), which in 
turn match to the individual hereditaments (UARNs). ‘Unique Property Reference Numbers’ do not 
therefore always correspond to a 'unique building'. These complexities are important when it comes 
to matching electricity and gas meter data, and for the units of construction defined for analysis in 
the modelling work, which do not equate simply to ‘buildings’. These points are discussed further 
below.

‘Polygon capture’

Then there is the problem that not all footprint polygons carry addresses. A typical case would be 
where a polygon on the street front corresponding to the major part of the building carries the 
address of a hereditament, but there are further unaddressed polygons corresponding to extensions 
at the back. Another frequent type of situation is where a shop or restaurant in a row of terraced 
properties has expanded into a neighbouring building, and only one of the two footprint polygons in 
question carries the address. 

Figure 1. An example of ‘polygon capture’ where a restaurant has expanded into an adjoining building. The polygon at 
number 36 is the ‘addressable’ polygon for this hereditament. The SMV shows that it requires an area of 77 m2 (GEA) 
but the polygon only offers 41 m2. The unaddressed polygon at number 38 next door offers 37 m2, which gives a 
combined floor area of 78 m2. The software accepts this as an optimal match.

In order to cope with this problem, we have developed special ‘polygon capture’ software using a 
spatial topological model and combinatorial optimisation. This compares the area of the footprint 
polygon with the floor area on each level given by the VOA line entries for the hereditament in 
question (Figure 1). If these differ significantly – allowing for the respective conventions of area 
measurement – then the method seeks additional floor area in adjoining unaddressed polygons at the 
same level. The method finds the best solution that creates a combined footprint with the closest 
match to the figure given by the line entries. In cases where two addresses are competing for the 
same polygon, the solutions can be compared and the better result retained. In this way, the software 
can be run many times until an optimum solution is achieved.  

This method works well for cases where a hereditament occupies several adjoining polygons. It 
cannot at present cope however with ‘campus’ hereditaments consisting of many separate buildings, 
since there are no simple criteria by which neighbouring (but not adjoining) polygons might be 
selected for capture. These problems can be overcome by using the Ordnance Survey 'Sites' product, 
which gives boundary polygons for campuses and similar sites with many buildings (Ordnance 
Survey 2015d). (Figure 2) The dataset is only available for a subset of the cases where this is 
required however, and so for other cases we use a digital version of the Land Registry property 
boundaries (HMLR, 2015) to identify cases where a number of buildings are contained by a single 
property boundary (Figure 3).

Figure 2. An example of a 'campus' where only the main building is 'addressed' (left) yet activity and energy use is 
probably spread across a number of buildings (right).

Figure 3. The thick black outline shows the land boundaries from the HMLR being used to identify all 4 detached 
buildings that make up the 'Corporation Yard and Engineering Depot'

Another issue that is unresolved at present can be thought of as the opposite of the polygon capture 
problem. It occurs when a hereditament falls in a polygon that is far larger than the ground floor 

area recorded by the VOA. This situation occurs when small hereditaments occupy space under the 
umbrella of a much bigger structure, such as a shopping centre containing many small shops, or a 
major transport hub like a mainline railway station with cafés and shops inside the ‘building’. 
Additional geometrical data – specifically internal plans of these buildings – would be needed 
before the methodology could be applied in these circumstances. The danger of ignoring this 
situation is that activities or energy are assigned to the much larger polygon resulting in incorrect 
analysis.

With the exceptions of these problematic cases, once these steps are complete we end up with 
hereditaments that are ‘stratified’ and piled up on the relevant footprint or footprints to make three-
dimensional ‘pseudo-buildings’ (Figure 4).  The fact that the SMV database gives floor areas by 
floor levels means that hereditaments can be allocated to the correct level or levels (including 
basements).

Figure 4.  Premises (UARNs) matched to a building 
footprint but jumbled (left), and stratified by floor 
level (right).

The Self Contained Unit

Through this work it has become clear to us that when attempting to model activity or energy use in 
the non-domestic building stock, the ‘building’ is not a wholly satisfactory unit. This is because of 
the various possible relationships that can exist between premises and buildings, in particular the 
fact that one premises can extend across two or more adjoining buildings. Should buildings be taken 
as units, then premises could become split, and it would be difficult to relate meters and energy 
consumption to the different parts. For the present work we have instead adopted the Self Contained 
Unit (SCU) introduced by Taylor et al (2014).

Should one or more hereditaments occupy a building with a single detached footprint polygon then 
building and SCU coincide. The SCU concept comes into its own with groups of adjoining 
footprints. Consider the two situations in Figure 5, showing three terraced properties, numbers 15, 
16 and 17 High Street. In (a) a single hereditament at ground level spans the two footprint polygons 
of numbers 15 and 16. The SCU (the black outline) is defined to contain the whole of both 
buildings. In (b) there is in addition a second hereditament spanning across numbers 16 and 17 at 
first floor level. Now the SCU (the black outline) must take in the whole of numbers 15, 16 and 17.

Figure 5. Self Contained Units (SCUs). Hereditaments are not broken between SCUs. In (a) a hereditament extends 
across 15 and 16 High Street at ground floor level, so the SCU (heavy line) spans the two footprints. In (b) another 
hereditament at first floor level extends across 16 and 17 High Street. Now the SCU (heavy line) must span all three 
footprints. 

The basic criterion for defining the SCU is thus that it must not break hereditaments on any floor 
level. The SCU has two further properties that are of key relevance to energy analysis. First, it has a 
well-defined thermal envelope (roof and external walls) through which heat is lost or gained. This is 
of obvious importance for energy use in space heating, cooling and air conditioning. The second 
property is that it is generally possible to know the relationship between the totality of floor space 
within the SCU, and the total metered energy supplied to the SCU (as discussed further below). It 
would often not be possible to determine this relationship of floor area to energy use at the 
hereditament or even the building level. For example in a large multi-tenant office block, 
hereditaments corresponding to the many tenants might all have their own electricity meters, but all 
might share a common heating system with its own meter or meters controlled by the landlord. 
Again, there might be just one electricity meter for a hereditament that extends across two or more 
adjoining buildings. The picture can be very complex at these levels, somewhat simpler at the SCU 
level. 

Figure 6. (left) A Self Contained Unit in Camden 
containing two buildings with a ground floor shop 
extending across both addresses. In this case the buildings 
are of similar age and construction. But in other comparable 
cases they might not be.

One drawback of the SCU, arising out of its very definition, has to do with the properties of the 
envelope. It is possible for different buildings making up one SCU to have different ages, different 
types of roof, or different wall materials and patterns of glazing (Figure 6). These could all in turn 
affect rates of fabric heat loss and gain. However this is an unavoidable dilemma, and there are only 
two choices: use the building as unit and subdivide hereditaments, or use the SCU and accept the 
problem of mixtures of fabric properties. We have chosen the latter.

We have created models of a number of regions including Leicester, Tamworth, Swindon and the 
London Borough of Camden. The examples from here onwards will focus on the model of Camden.

Once we have modelled a region at the SCU level we can begin to produce visualisations which 
show how the floorspace is occupied on each floor level.  Figure 7 show one such visualisation for 
part of Camden showing all SCUs in an area along Camden High Street. The colours code for 
predominant activities in floors within SCUs. Figure 8 shows the breakdown by both activity and 
floor level for Camden, in the form of a Sankey diagram. Activities are distinguished at the left, in 
fourteen groups aggregated from VOA activity classifications. Offices dominate, followed by shops. 
Floor levels are given at the right. Shops unsurprisingly are mostly at ground level while offices are 
found on all floors.

Figure 7. Three-dimensional visualisation of part of the Camden model for an area along Camden High Street. The 
dominant activity in each floor of each SCU is colour coded. Hereditaments that are not non-domestic (hence probably 
domestic) are shown in grey.

Figure 8. Sankey diagram to show the distribution of non-domestic activities in fourteen groups (left) between floor 
levels (right) in Camden. (Only floor area in the SMV database is included.) Shops are predominantly on the ground 
floor while offices are found on all floor levels.

Matching electricity and gas meter data to SCUs

The Department of Energy and Climate Change (DECC) has made 2011 gas and electricity meter 
data available to the project for all premises in Camden, both domestic and non-domestic. Use of 
these data is covered by strict confidentiality constraints. We will therefore discuss their processing 
and analysis in general terms, and present aggregated non-disclosive results.

Every electricity meter has a unique meter point administration number (mpan) with an address. 
The equivalent for gas meters is the meter point reference number (mprn). Both types of meter have 
been matched by their addresses to OSAB addresses (UPRNs). Through these they can be matched 
to the addresses of VOA hereditaments (UARNs). Successful matches were achieved automatically 
for 97% of gas meters and 99% of electricity meters in Camden. 

Figure 9 gives a hypothetical illustration of the types of three-way relationship that can result. The 
meters here are all for electricity (mpans). The premises on the first floor at 42 High Street (A1 
Hairdressing) is fairly straightforward. It is a child UPRN and has matched correctly to both a VOA 
hereditament and a meter, making it possible to assess electricity use in the hereditament as such. 
Downstairs however the VOA record for the Wine Bar and Premises has found the best match 
available, to the parent UPRN (42 High Street), despite the existence of a child UPRN for Bill’s Bar 

– probably because of the difference between the two descriptions of the Bar. The electricity meter 
for this same hereditament has nevertheless matched correctly to the child UPRN for Bill’s Bar. The 
result is a slightly less clear-cut relationship between hereditament and electricity use. On the other 
hand when we look at total floor area and total electricity use for the whole SCU at number 42, then 
the overall relationship is correct. Here lies the power of the Self Contained Unit.

Figure 9. (left) A hypothetical 
illustration of possible types of 
match within SCUs of electricity 
meters (mpans) and gas meters 
(mprns) with OSAB addresses 
(UPRNs) and VOA hereditaments 
(UARNs). (See text for detailed 
explanation.) Even where some 
matches are not made, the overall 
relationship between floor area, 
activities and energy use is known, 
showing the power of the Self 
Contained Unit.

In actual cases such relationships can vary from the very simple to the highly complex as identified 
by Neffendorf et al (2009). We illustrate a series of real examples in Camden with all potentially 
identifying details removed. A SCU containing a single-storey shop (Figure 10a) has one 
hereditament (UARN), one gas meter and one electricity meter, all matched to the single UPRN. 
This is as simple as the relationships get. A two-storey SCU with one UPRN  (Figure 10b) has two 
office hereditaments (UARNs), one shared gas meter, and one shared electricity meter. 

Figure 10. b (right) Two premises in one building where they 
both share the gas and electricity meter

Figure 10. a (left) Single storey shop with one gas 
(yellow) and one electricity (cyan) meters. This and the 
subsequent figures show real examples of different 
relationships between meters, premises and footprints 
in Self Contained Units. The key distinguishes urban 
blocks (BuiltBlocks), building footprints in the OS 
Topo layer (topo), SCUs, Basic Land and Property 
Units (blpus, each of which has a UPRN), VOA 
hereditaments (UARNs), electricity meters (mpans) and 
gas meters (mprns).

A four-storey SCU (Figure 11a) has one parent UPRN matched to a dry cleaner’s on the ground 

floor, plus three domestic child UPRNs on the upper floors, one of which is a maisonette. One gas 
meter is matched to the maisonette, and two electricity meters to the parent UPRN. These are 
presumably shared between the dry cleaner’s and the flats. A large office building SCU (Figure 11b) 
has a parent UPRN with fourteen child UPRNs, thirteen of which are matched to different VOA 
UARNs. There are twelve electricity meters and six gas meters matched to the parent UPRN, and a 
further two electricity meters matched to the fourteenth child UPRN. 

Figure 11. a (left) Relationships between meters, premises 
and footprint in a four-storey SCU with a dry cleaner’s on 
the ground floor, and two flats and a maisonette above.

Figure 11. b (right) Relationships between meters, premises 
and footprint in a large multi-storey SCU containing 
fourteen office hereditaments.

Some large office building SCUs can nevertheless be simple in these terms. A multi-storey block 
under single ownership (Figure 12) has one UPRN matched directly to two UARNs, two electricity 
meters, and a gas meter. 

Figure 12. Relationships between meters, premises and footprint in a large multi-storey SCU with one office 
hereditament which has two electricity meters and one gas meter.

The more complex cases here reinforce the case for the SCU as a basic unit in which the overall 
relationship between floor area and energy consumption can be determined, even if the proportions 

of gas and electricity use supplied to different parts of the SCU are unknown. This would in many 
instances be impossible for separate premises/ hereditaments, as the examples have shown. The 
combinations of uses that occur in buildings nevertheless raise some problematic issues about what 
are actually meant by typical or benchmark values for energy intensity for specified activities. 
Should these be measured at the building/SCU level, in which case they refer to mixtures of 
activities? Or should they be measured at the hereditament level? But those hereditaments may not 
be independent in energy supply terms, and may for example share a heating or air conditioning 
system. Also they will share an envelope with other hereditaments.

Non-domestic ‘energy epidemiology’

Energy meter data (gas or electricity or both) have been successfully matched, as described, to 5329 
Self Contained Units in Camden. These data can be analysed in relation to activities and the results 
compared with energy benchmarks from other sources, for example Display Energy Certificates 
(Department for Communities and Local Government 2012). We are limited in publishing figures by 
confidentiality constraints. However, analyses of floor areas and the intensities of gas and electrical 
energy use are given for a number of the more numerous activity types. Note that Table 1 and 
Figures 13 and 14 refer only to SCUs that have been identified in each case as containing a single 
hereditament and no domestic (residential) addresses.  This means there is a high probability that 
the SCU is a single building, containing a single non-domestic occupier. Also, meters where the 
consumption has a value of less than 2 kWh (including negative values) have been removed, as 
these records may be indicating that premises are not occupied, or that the meter is not ‘live’ and is 
merely being maintained in the energy supplier’s database. Alternatively, the anomalous values may 
be due to factors of which the research project is not aware, such as the effect of annualisation of the 
data, rather than the provision of actual meter records.

Principal activity in single-hereditament SCU
Floor area of sample SCUs, standardised to gross internal area (m2 )

Private 
school

Cafe

Office

387
9
150
278
574
17145
722

Shop Workshop Restaurant
56
39
106
164
215
383
166

Count
Minimum
First quartile
Median
Third quartile
Maximum
Mean
Standard 
deviation
2217
Table 1. Analysis of sample floor areas for selected types of activity in Camden. These are for Self Contained Units 
(SCUs) each of which contains just one hereditament with floor area and at least one electricity meter.

69
37
74
158
390
18164
574

344
11
54
94
150
1223
127

28
134
405
563
801
2172
645

27
23
48
72
104
157
79

1555

404

141

37

78

Table 1 presents the profile of floor areas for a sample of significant SCU activity types in Camden, 
where meters have been matched. Floor areas have been standardised to the VOA gross internal area 
(GIA) measurement convention. The samples clearly show the influence of outliers with large floor 
areas, especially in commercial offices, shops and workshops, but less so in schools, restaurants and 
cafes. For Figures 13 and 14 data have been purged of energy use intensity (EUI) values (presented 
as kWh/m2/year) greater than three standard deviations above the mean within each activity class. 
Such high values are deemed to be unrealistic and are most likely due to questionable energy data, 
or errors/omissions in address matching.

Figure 19. Analysis of electricity energy use intensity 
(EUI) for samples of Camden SCUs containing one 
hereditament. EUIs greater than three standard deviations 
above the mean have been removed prior to compilation. 
Floor areas are calculated to gross internal area. On the x 
axis, the numbers in parentheses indicate the size of the 
sample.

Figure 20. Analysis of gas energy use intensity (EUI) for 
samples of Camden SCUs containing one hereditament. 
EUIs greater than three standard deviations above the mean 
have been removed prior to compilation. Floor areas are 
calculated to gross internal area. On the x axis, the numbers 
in parentheses indicate the size of the sample. Cafes omitted 
due to small sample size.

Except for private schools, the spread of EUIs is extremely wide within each activity, considering 
these are cleaned data. Only restaurants and private schools have something close to a normal 
distribution in both electricity and gas use, whilst electricity EUIs in workshops are slightly skewed. 
High outliers are tending to skew the results in other activity types, and the middle two quartiles 
(represented by the boxes in each figure) cover a broad range of values in most activities. This 
degree of variability in the annualised meter consumption data is testament to the difficulties of 
modelling energy use in the non-domestic sector.

Beyond this, it is possible to study consumption in relation to characteristics of building geometry 
and construction, measured from or associated with SCUs. To do this for such a large sample of 
non-domestic premises/ buildings has not previously been possible. For the domestic stock of 
Britain, DECC and the Energy Saving Trust have developed the Homes Energy Efficiency Database 
(HEED, now part of NEED), which has made it possible to study gas and electricity consumption in 
relation to a range of built form and household characteristics for millions of dwellings (Hamilton et 
al 2011). This type of large-scale statistical approach has been called ‘energy epidemiology’ by 
analogy with epidemiology in medicine, where health outcomes in populations are studied in 
relation to lifestyle, environmental and other variables (Hamilton et al 2013). The present work 
opens the prospect of a non-domestic energy epidemiology.

Conclusions

Significant breakthroughs in the past few years mean it is now possible to create detailed spatial 
models of activity, floorspace and energy for the non-domestic building stock of England and 
Wales. This can be achieved by automated procedures and can be rolled out to any region of 
England and Wales. To achieve this kind of modelling we have found that it is essential to take into 
account the relationship between premises and buildings and to develop a modelling method that 
can cope with these often complex relationships. At present, much of the data we are trying to align 

has no direct spatial element to allow its integration into a GIS. Our experiences with the models 
have shown that the development of bespoke software that enables the address matching of often 
fuzzy addresses to a spine of 'correct' addresses is an essential first step in this process. In most 
cases, but most particularly in urban areas, it is essential to be able to model these activities in 3D so 
that the 'stratification' of premises in say a multi-tenant office block can be handled. The methods 
we have developed are automated which makes it possible for us to model any region of England 
and Wales in this manner.

Our aim was to model the energy requirements of the building stock using energy data derived at 
the end user meter level. This meant that the model structure we adopted was of great importance if 
we were to avoid producing statistical outputs that obscured the finer details of the activities within 
these buildings. Our experience is that if this step is ignored then there will be cases where the 
energy use per building is modelling something that could involve mixed activities, and even a 
mixture of domestic and non-domestic activities which would lead to misleading statistical 
conclusions.

In a similar way there will be cases where energy use has to be spread across many buildings, as is 
the case when a premises occupies a campus (for example a school) or a large factory uses several 
buildings on one site. Cadastral mapping can help here, although this cadastral mapping may also 
need to be modelled in 3D if at the same time it needs to be able to cope with the increasing 
complexity of the premises to buildings relationships that are theoretically possible and can occur in 
densely built up areas.

For the work presented, our aim was to produce energy use standardised by area (for example EUI 
of kWh per m2 of floorspace). To do this a reliable measurement of floorspace within the buildings 
is required. Our experience has been that when this floorspace data contains even minor errors, it 
can have a large impact on the EUI's produced. Estimating this floorspace from building footprints 
should be practised with caution. 

Our aims from here are to integrate further datasets on the building stock such as age of buildings, 
construction materials, glazing ratios and roof types. This would open the prospect of a programme 
of statistical work on correlations and the possible derivation of types, by which unknown properties 
might be inferred from known properties. These results could be used in developing models of other 
locations. Work on basements has already shown that their incidence and size is related to building 
age. Work in the 1990s using detailed measurements of glazing areas and glazing types in a sample 
of 100 non-domestic buildings showed strong relationships between glazed area and floor area in 
day lit built forms (Gakovic 2000).

We hope to be able to roll the data out to much larger parts of England and Wales. However access 
to individual meter data at the level of detail described in this paper is not possible without special 
access being granted by central government, and so we may have to turn to modelled data or 
spatially aggregated meter data for this future work. This will add complications and uncertainties to 
what is already a complex model.

Data sources: 

Data provided by the Valuation Office Agency contains public sector information licensed under the 
Open Government Licence v1.0.

Map data © Crown Copyright/database right 2012: an Ordnance Survey / EDINA supplied service.

Gas and electricity meter data was supplied by the Department of Energy and Climate Change 
(DECC).

References

British Standards Institution 2006 Spatial datasets for geographical referencing. General models for 
gazetteers and spatial referencing (BS7666-0:2006) (BSI, London)
Gakovic B 2000 “Areas and types of glazing and other openings in the nondomestic building stock”, 
Environment and Planning B: Planning and Design 27 667-694
HMLR 2015 https://www.gov.uk/government/organisations/land-registry/about accessed 3 
November 2015
Gakovic B, Smith M, Steadman P 1993 The Development of a Database of Energy Use on Non-
Domestic Buildings: Comparisons between Floor Areas as Recorded by Valuation Office and Open 
University Surveys, Report to the Department of Environment and the Building Research 
Establishment, Centre for Configurational Studies, The Open University, Milton Keynes
Hamilton I, Steadman P, Lowe R, Summerfield A, Bruhns H 2011 Report on the exploratory 
analysis of the Homes Energy Efficiency Database and Energy Demand, Energy Institute, 
University College London, www.bartlett.ucl.ac.uk/energy/research/themes/buildings/ building-and-
energy-data-frameworks/BEDF_UCL_HE
Hamilton I G, Summerfield A J, Lowe R, Ruyssevelt P, Elwell C A, Oreszczyn T 2013 “Energy 
epidemiology: a new approach to end-use energy demand research”, Building Research and 
Information 41 482-97
Neffendorf, H., Bruhns, H. & Harrison, A. 2009. Non-domestic Energy Efficiency Data Framework 
(NEED): scoping study findings. Department of Energy and Climate Change.
Ordnance Survey 2015a MasterMap www.ordnancesurvey.co.uk/business-and-
government/products/mastermap-products.html accessed 1 April 2015 
Ordnance Survey 2015b Address Base www.ordnancesurvey.co.uk/business-and-
government/products/addressbase-products.html accessed 1 April 2015
Ordnance Survey 2015c Topography Layer www.ordnancesurvey.co.uk/business-and-
government/products/topography-layer.html accessed 1 April 2015
Ordnance Survey 2015d Sites www.ordnancesurvey.co.uk/business-and-government/products/sites-
layer.html accessed 1 April 2015
Ordnance Survey 2015e Building Heights www.ordnancesurvey.co.uk/blog/tag/building-heights/ 
accessed 1 April 2015 
Steadman P, Bruhns H R, Rickaby P A (2000). ‘An introduction to the National Non-Domestic 
Building Stock database', Environment and Planning B: Planning and Design 27, 11-24
Taylor S, Fan D, Rylatt M 2014 “Enabling urban-scale energy modelling: a new spatial approach” 
Building Research and Information 42 4-16
Valuation Office Agency 2015 Business rates www.gov.uk/introduction-to-business-rates accessed 1 
April 2015

Energy and Location: CIMNE point of view 

 
 
 
 
 
 
 
 
1.  CIMNE and BEE Group Introduction  

CIMNE  is  a  consortium  between  Government  of  Catalonia  and  the  Polytechnic  University  of 
Catalonia.    CIMNE  has  the  experience  and  reputation  gained  through  participation  in  more 
than  910  R&D  projects  with  the  financial  support  of  the  European  Community,  and  various 
organizations  as  well  as  Spanish  and  International  enterprises.  Within  CIMNE,  the  Buildings 
Energy  and  Environment  (BEE)  Group  is  an  independent  research  group  of  around  20 
researchers, focussing their R&D activities on methodologies and tools for the reduction of CO 
2 emissions in the urban environment. Within this focus, work is organised along three lines: 
applied research for the energy retrofitting of buildings and neighbourhoods; applied research 
for the introduction of renewable energies and energy efficient methodologies in the building 
sector  and  consultancy  and  assessment  to  municipalities  to  enhance  their  sustainable  urban 
development.  CIMNE  BEE  Group  are  dedicated  to  achieving  the  highest  possible  energy 
performance in as many buildings as possible, by the practical application of research results in 
a  real,  business  oriented,  environment.  In  practical  terms  this  means  practicing  what  we 
preach. 

2.  Experiences on Energy and Location  

CIMNE‐BEE  Group  has  been  working  on  projects  and  development  of  tools  and  services  to 
optimise the energy consumption in public, private and residential buildings for more than 15 
years. In this section we want to share the lessons learned during that time, believing that the 
experiences  we  describe  can  show  how  treatment  of  data  combined  with  the  appropriate 
delivery  of  results  to  end  users  improves  the  energy  management  of  buildings  and  achieves 
energy savings. But for doing this you need the right data and methodology. 

A  first  group  of  projects  are  several  projects  for  improvement  of  the  energy  management  in 
buildings  through  ICT,  developed  from  2009  to  2015.  The  projects  use  monitoring  of  key 
parameters  related  with  the  energy  consumption  in  the  buildings,  available  third‐party  data 
(weather  data,  consumption  of  energy  companies,  etc...)  and  treat  analytically  this  data 
displaying the results for different types of end users (building managers, occupants) with the 
objective to achieve energy savings. 

We  can  distinguish  two  subgroups  of  projects  in  this  field.  The  first  one  is  focused  on  the 
residential  sector,  where  we  have  developed  three  projects  (eSESH,  BECA  and  Encerticus 
(www.med‐encerticus.eu)). These projects provided services to 250 homes in Spanish pilots s 
led by CIMNE and more than 5,000 homes in total. These projects allowed CIMNE to improve 
the  knowledge and service related to data processing and visualization  to the end user along 
of time. Paper, web and mobile app were used as user interfaces. 

 
 
 
 
 
 
Figure 1: Example of final visualization addressed to final residential users (Encerticus) 

In all of these projects a significant energy savings has been achieved. The average savings in 
each final energy source have been:  6% electricity, 4% thermal energy, 4 % water. 

The  second  group  of  projects  are  based  on  the  same  concepts  as  the  first  one  but  the  final 
target are public buildings. In these projects is very important the correct identification of the 
various types of users who have influence on the energy consumption of the buildings and the 
information they need. The visualization takes special importance in this case, as the different 
types  of  users  need  different  information.  A  good  example  is  the  SmartSpaces  project 
(www.smartspaces.eu) where CIMNE, as leader of the Spanish pilot, developed analytical tool 
and  visualization  interfaces  for  building  users  at  all  levels,  providing  services  to  30  buildings 
and saving 30% of the thermal energy and 10% electricity. 

Figure 2: Some example of visualization for different user’s level.

Another example of successful application of ICT‐based services for energy efficiency are those 
provided by the company INERGY (www.inergybcn.com) created by CIMNE. INERGY has been 
offering services since 2005 to around 3,000 public buildings in more than 400 municipalities, 
and has achieved sustained savings, between 2% and 10% (depending on service given). 

Lessons learned from these projects: 

‐ 

The  users  in  the  residential  sector  are  not  prone  to  make  investments  in  energy 
services,  but  the  majority  of  them  are  interested  in  receiving  this  kind  of  services 
without cost. 

 
 
 
 
 
 
 
 
‐ 

In  many  cases  the  data  necessary  for  the  services  already  exists  in  utilities  or  public 
authorities and the investment can be minimal. 

‐  Not  all  buildings  have  a  sufficient  savings  potential  that  justifies  the  investment  in 

additional monitoring equipment. 
Training and motivation of users is a key to achieve real savings. 

‐ 

Analysing  the  lessons  learned  from  these  projects  we  realized  that  the  best  channel  of 
communication  with  the  residential  user  were  utility  companies.  For  these  reason  we 
developed the EMPOWERING project (www.iee‐empowering.eu) where services for awareness 
and empowering end users are given to more than 350,000 customers, of four utilities around 
Europe (Austria, Italy, France and Spain). In this project CIMNE is responsible for the analytical 
data (using our own Big Data Analytics system) and communication with utilities and existing 
databases (cadastral, weather data, etc ...) and the utilities are responsible for displaying the 
results to their customers. The services received by end users can include in: 

Indications of performance improvements over time. 

‐  Comparison with similar households (by power, tariff, Postal Code,…) . 
‐ 
‐  Consumption‐weather dependence. 
‐  Detailed consumption visualization, and breakdown. 
‐  Personalised energy saving tips. 
‐  Alerts (high consumption, high bill, extreme temperature, etc.). 

Figure  3:  work  scheme  of  CIMNE  Big  Data 
system 

Figure  4:  Some  examples  of  visualization  to 
final customers.  

The  average  savings  of  customers  who  received  this  type  of  services  is  around  5%,  both  in 
electricity and thermal energy. 

Given  the  success  of  these  services  CIMNE  started  marketing  the  services  developed  in 
Empowering, under the trade name BEE Data (www.beegroup‐cimne.com/solutions/solutions‐
for‐utility‐companies/  )  and  now  the  service  is  provided  to  8  utilities  and  approximately 
450,000 final users. 

Lessons learned in EMPOWERING: 

‐ 

The  user  interfaces  and  the  personalization  of  the  information  shown  is  a  key  to 
achieve savings 

 
 
 
 
‐ 

‐ 

To engage users to these services the reports attached to their bills is one of the best 
options. 
Sending the information to large group of users without previous consent (opt‐out) is 
better that offering the services through voluntary registration (opt‐in) 

‐  Working with anonymized data permits to preserve user privacy 

Alongside Empowering which partly has solved the energy management services of residential 
buildings,  at  least  at  the  level  of  end‐user  or  owner,  It  is  necessary  to  dig  deeper  into 
improving  the  management  of  public  building  stock  and  improve  the  services  to  decision‐
makers in order to take full advantage of the existing and usable data for energy management.  
The project SEMANCO (www.semanco‐project.eu/) is an example of that approach.  

This project aimed to make a semantic integration of existing data in multiple databases that 
were  scattered  in  different  organizations  to  improve  the  support  on  decision  making  at  the 
political level and public authorities, and thus, improve the management and urban planning 
by municipalities.  

In this project we realize that integration of some public data, with effort, was relatively easy 
to acquire and work them (eg. cadaster, weather data, etc ...) others data such as population 
size, GDP, etc .., they were accessible but only at the municipal level or by zip code and, finally, 
other  data  exist  such  as  energy  consumption  or  specific  building  descriptions  that  are 
impossible to achieve at any level (if you work with large utilities). But we realized that there 
are  still  many  public  regulations  and  practices  that  oblige  the  collection  of  valuable  building 
information  on  paper,  not  accessible  for  automatic  processing.  An  example  is  the  data  from 
the technical building inspections (ITE) in Spain. 

Figure 5: work scheme of SEMANCO system 

Figure  6:  Some  examples  of  visualization  of 
SEMACO project.  

To continue with the project, we had to work with simulation and modeling of demand, using 
as  base  the  data  we  actually  got.  Thus  we  could  develop  the  different  visualizations  of  data 
that were previously marked as SEMANCO project objectives. 

Learned lessons in SEMANCO project: 

 
 
 
 
 
 
‐  Not all available data to facilitate energy management are really accessible. 
‐  Public  authorities  are  not  prepared  to  make  the  data  accessible  to  users.  Users  that 
previously  had  been  requested  them  a  lot  of  information  of  their  building  and 
facilities. Due to there are many records still on paper (projects, maintenance, etc...). 
"Personal data" is very difficult to access or use due to the data protection law. Using 
aggregated data (user averages) is a possibility. 

‐ 

‐  Use of open source software facilitates the massive exploitation of the results in such 

projects, as it permits the collaboration of different service providers. 

3.  The Catalan initiative on E&L  

After exposing the projects we have done in CIMNE during the last 10 years related to Energy 
and  Location,  together  with  lessons  learned  in  them,  we  want  to  expose  our  view  on  the 
future  possible  exploitation  in  Catalonia  and  the  opportunities  brought  by  the  signing  of  the 
"Catalan  Strategy  for  Energy  Rehabilitation  of  Buildings".  This  agreement  was  signed  in  2014 
by more than 70 organizations involved in the energy management of buildings in Catalonia. 

This strategy sets clear and ambitious targets for intervention in the Catalan building park to 
promote  the  reduction  of  energy  consumptions  of  buildings  by  2020.  These  objectives  are 
shown in the following table: 

Field 
Energy 

CO2 

Cost savings 

Buildings 

Mobilized 
investment 
Jobs 

Goal  
Reduction  of  14%  of  final  energy  consumption  of  the 
residential  and  tertiary  buildings  in  Catalonia  (equivalent  to 
558kTep) 
Emissions reduction of 22% of CO2 emissions of the residential 
and  tertiary  buildings  in  Catalonia  (equivalent  to  2.6  million 
TnCO2) 
Savings 21% of the economic cost of the residential and tertiary 
buildings in Catalonia (equivalent to 800 million €) 
Intervention in 61% of the residential and tertiary buildings in 
Catalonia (790,672 buildings) 
Investment  of  1,400m  euros  in  120  macro  energy  renovation 
projects with public and private funds. 
Creation and / or recycling of more than 14,000 workplaces. 

To achieve the objectives of this strategy has been prepared the action plan which is divided 
into five main lines: 

1.  Information System 
2.  Involvement and training 
3.  Innovation products and services 
4.  Organizational Model 
5.  Investment program and financial mechanisms 

CIMNE  leads  the  Information  System  line  and  actively  participates  in  the  others  in  order  to 
achieve the objectives of this strategy.  

 
 
 
 
The  information  system  aims  to  collect  all  information  that  is  useful  for  the  improvement  of 
the  energy  management  in  buildings  (both  previously  recorded  information,  such  as 
information  not  collected  yet  but  that  must  be  collected),  to  process  and  analyse  data, 
returning valuable results to the different stakeholders involved in the energy management of 
the buildings. 

Figure 7: User interaction for Catalan Building renovation Strategy 

Implementation of the Information System 

The  implementation  and  development  of  the  Information  System  is  structured  in  5  phases 
within the period 2014 ‐2018 which are described in the table. 

Buildings 
F1‐  Catalan  Government 
Buildings pilot  (~ 500) 

Year 
2014 

F2‐  All  Catalan  Government 
Buildings pilot (4000)   

2015 

F3‐  Catalan  Municipalities  + 
extend  de  system  to  other 
EU regions. 
F4‐ Tertiary sector  

F5‐ Residencial  

2016 

2017 

2018 

Inputs 
Cadastral and meteorological data, key 
Id    (cups,  ref.  cat),  EEM  (investment, 
application date)   
(monthly) 
Real  consumption  data 
Other 
and 
thermal 
descriptive  data.  Energy  building 
certification.  
Consumption  data  (hourly)  of  public 
entities. Energy Efficiency Measures.  

electrical. 

Business  and  company  description 
data. Private consumption data. 
Residential  building  description  data. 
Residential sector consumption.  

Tasks  
Gather  the  information,  pre‐
process  and  store  it,  prepare 
the outputs for Authorities. 
Development 
of 
methodologies  and  analytics, 
outputs for all users.  

of 

analytics 

and 
the 

Big  Data 
improvement 
visualization.  
Analytic development for the 
private sector.   
Analytic development for the 
residential sector.   

Phase 1: The development of the strategy in 2014 begins with a pilot consisting of a group of 
buildings  belonging  to  the  Government  of  Catalonia  (400  of  4000  buildings  available),  where 
the  information  system  is  implemented  and  connected  to  existing  databases  for  collecting 
additional information necessary for improving the energy efficiency. The system manages also 
the energy saving actions applied in buildings as well as the date and investments made in the 
previous period (2012‐2014).   

Cadastral data 

All Catalan Buildings  

 
 
 
 
 
 
Building energy certification   
Meteorological data 
Building  description 
cadastral reference)   
Energy  Efficiency  Measures  (EEM)  applied, 
application date. 
Investment in EEM  

(typology 

,  CUPS, 

All Cert Buildings 
150 stations  
~400 buildings 

~1500 EEM 

~ 20 M€ 

Phase  2:  For  this  phase,  in  late  2015,  are  given  access  to  all  users  with  buildings  of  the 
Government of Catalonia (4000 buildings), and the data of electricity and thermal consumption 
(1800  contracts)  is  added.  All  users  have  access  to  the  monitoring  reports  and  evolution  of 
buildings. 

Decision makers in the administration: They have a geolocation on interactive map to see the 
status  of  the  various  analysis  general  state  of  building  park.  A  status  report  with  number  of 
buildings, energy state of buildings, implemented measures, with economical investment and 
personalized treatment is also generated by: 

‐  By department. 
‐  By type of building (schools, offices, etc ...). 
‐  By geographical area. 

The  building  responsible  person  will  receive  a  report  that  provides  him  with  overview  of 
building energy consumption including the evolution of building consumption compared to the 
historical, consumption compared to similar buildings, evaluation of return on the measures, 
and  suggestions  of  measures  to  implement  depending  on  their  typology  (measures  more 
implemented with successful in similar buildings). 

Figure  8:  Building  responsible  visualization 
example 

Figure 9: Authorities visualization example.  

Phase 3. The target set for 2016 is to integrate all the buildings of the Government of Catalonia 
and  also  give  access  to  the  most  important  municipalities  in  Catalonia  progressively,  with 
special attention to those participating in the CoM. In parallel, is expected extend the initiative 
to other European regions. The fund to do that will be provided by the EDI‐Net , EE07 program 
of  H2020.  This  project  will  be  extended  to  the  cities  of  Leicester  and  Nuremberg  in  the  first 

 
 
 
 
 
 
 
 
 
 
 
 
year  of  the  project  and  in  the  second  year,  the  project  aims  to  expand  to  150  cities 
(municipalities that are partners of "Climate Alliance"). 

Phase  4:  In  this  fourth  phase  aims  to  adapt  the  system  to  give  service  to  private  buildings, 
initially tertiary sector (hotels, supermarkets, shops, bars, etc.). The necessary adjustments will 
be in obtain data necessary for management in the private sector and improving analytical for 
classifications, similarity, and customization by sector. 

Phase 5‐ For 2018, the goal is to also enter the residential buildings of Catalonia. In this way, 
the system could reach 12 million of Catalan buildings processed. The tasks to be performed 
are  the  same  as  in  Phase  4  but  for  the  residential  sector.  We  see  that  the  most  important 
limitation  is  the  obtaining  of  the  additional  data,  being  the  analytical  part  similar  to  those 
developed in the Empowering project. 

4.  Conclusions 

From  experience  in  previous  projects  we‘d  like  conclude  the  most  important  issues  that  are 
necessary  to  take  into  account  when  someone  want  start  one  project  related  to  Energy  and 
Location, these are:  

‐ 

‐ 

‐ 

‐ 

Final  users  and  their  needs  must  be  the  focus  of  any  application  or  data 
processing. Process and visualization of data without taking into account the final 
user needs and knowledge are useless. 
Public authorities at all levels should be closely involved in any initiative that seeks 
to use data available to the government.   
There  is  potential  to  treat  personal  data  without  infringing  data  protection  law, 
but it is vital to be clear about the goal we want to achieve with your treatment. 
The use of open‐source tools can guarantee much more profitable development of 
demonstration projects, replication and research. Also make easy the collaborative 
future developments or favoring exploitation by third parties, reducing the initial 
investment and fixed costs of the systems. 

In the other hand, we think that the "Catalan Strategy for Energy Building Rehabilitation" can 
be an appropriated candidate for a pilot to test the INSPIRE directive due to the structure of 
work and the necessary involvement of the authorities as a key facilitator of data. As can be 
seen from the marked in  the objectives of the Phase 1 and 2, which are the phases that are 
already developed, have been carried out successfully. Phase 3 is also on the way to reach its 
goals. For this reason, we think that the rest of phases should be accomplished with success.  

 
 
 
Date 
2010-11-12 

The Swedish EPC register 

The Swedish National Board of Housing, Building and Planning is a 
central government authority and is responsible for the Energy 
Performance Certificate (EPC)-register. The first version of the EPC-
register was released in September year 2007.  

Today there are nearly 600 000 buildings in the EPC-register. Everybody 
can access some basic information for the EPC from our website.  

Our register contains all type of buildings, new buildings as well as 
existing buildings. There are three main types of buildings: single family 
houses, multifamily houses and non-residential houses 

In year 2014 we made two major changes in EPC, first we changed the 
design of the certificate. From a fixed scale (picture 1) for every building 
not taking in consideration what kind of building in, which use the 
building has or where in the country it is placed. Today we use a 
common A- G (picture 2) scale where the energy performance is related to 
the different values of the energy performance for new buildings. 

Secondly we have changed the way to access the register to perform 
EPC: s. Before year 2104, we have a system with accreditation, meaning 
that a company has an accreditation and that not all of the employed in 
the company has to be certified to perform EPC:s  

From 2014 we have change the way to access the register and perform 
EPC:s where everybody has to be certified by an accredited company. 
There is an exam as one part of the certification, the persons have to have 
relevant education and some years in work practice in the area buildings 
and energy. Every certification has to be updated within five years. 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
Date 
2010-11-12 

Using GIS 
We have recently used our GIS (Geographic Information System)-
application to deliver a map, with indoor radon concentration measures in 
Sweden, to JRC (picture 3&4). Most of the values have been downloaded 
from our EPC-register. There is no obligation to perform a radon measure 
in all buildings in Sweden so therefore not all EPC: s include a radon 
measurement.    

Radon is a part of the information in the EPC:s in Sweden, radon is not 
directly connected to energy, but the principle is the same of how the data 
from register works. 

The map was divided into 10*10 km grids by our GIS-application, and 
we used the unique identification number of the buildings in the EPC-
register to get the coordinates of central point of the buildings. These 
coordinate where downloaded from the authority that is responsible of 
the cadastre. With this information we could gather a number of radon 
measurements in the different 10*10 km square by using the GIS-
application. 

The GIS- application produces an average value of the radon in each 
square, by summing the values of measurements and divides it by the 
numbers of measurements. 

We have used the EPC-data in the same way as the radon map delivered 
to JRC, for internal use. The map was divided in squares of 250*250 m 
using the energy performance to display the average in each area to see 
the differences in the country.  

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
Date 
2010-11-12 

Validating the information 

All building in Sweden have a unique identification number, with a 
single number prefix and a number (1-234567). By this number it is 
possible to connect to coordinates. This number follows the building 
through its lifecycle and is never reused.  

When we started the register for EPC: s we did not validate the 
identification for the buildings and therefore we have old EPC: s where 
we cannot identify what building the EPC refers to. The energy experts 
had to fill information about the identification but they were not used to 
identifying buildings. The energy experts and the owner of the buildings 
know the name of the property and the addresses, but that was not 
enough to get the right identification of the buildings. 

As a first step we verified the name of the property and that the property 
was placed in the right municipality, by an online service to the cadastre. 
But we did not know if it was the right building at this time. 

In the next step we got access to all information of buildings and 
properties. With this information we used our own GIS-server with a map 
and put a layer with the buildings upon it, using there coordinates. This 
was the moment when we introduced the map in the application to 
support the energy experts to identify the buildings (picture 5). Using this 
technique, we nearly have 100% correct identification on every building 
in the register from this date.  

We have recently changed the way our system access this map. Today we 
are accessing the map in the application through a web-service from the 
authority that is responsible for the cadastre.  

The risk using another service is the reliability, but we felt that it was an 
insignificant risk compared with the maintenance of the map, so we 
decided to use this web-service. The map has to be updated periodical 
because of changes in properties. Updating and cashing the map is 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
Date 
2010-11-12 

process that we felt too hard to handle on our own. The building where 
spotted in the right place on the map, but the information in the map was 
not always correct. 

The energy expert has to choose municipality and fill the name of the 
property to get to the information in the map. The property can contain a 
number of buildings that will be marked as spots in the map. The energy 
expert chooses the building by clicking in the map and the information 
will be filled into the form. 

As we know that the information about the buildings identification are 
correct we can link this information to other specific information of the 
buildings  

If there will be a mistake, by the energy expert, choosing the wrong 
building at the wrong property, we have a possibility to correct this 
information, using the same map-support as the energy experts. 

In the EPC- application we have validation in most of the fields that have 
to be filled, sometimes the validation lead to a stop, for impossible 
values.  Sometimes the validation is a warning to alert the energy expert 
that something might be wrong. In some cases we have fields that are 
checked in correlation to each other. There are even guidelines of every 
field how to fill the values, accessed by hoover in the field (picture 6).  

It is important not to have too much warnings/stops that make the 
application annoying and hard to interact with. 

It is important that there is possibility to use the form to fill information 
even though it is an extra ordinary building for example a really large 
building.  

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
 
Date 
2010-11-12 

Picture 1 

Picture 2 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
 
 
 
Date 
2010-11-12 

Picture 3  

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
Date 
2010-11-12 

Picture 4 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
 
Date 
2010-11-12 

Picture 5 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
 
Date 
2010-11-12 

Picture 6 

National Board of Housing, 
Building and Planning 

Box 534 
SE-371 23 Karlskrona 
Sweden 
Office: Drottninggatan 18 

Tel: +46 455 35 30 00 
Fax: +46 455 35 31 00 
E-mail: registraturen@boverket.se 
Web site: www.boverket.se 

 
 
 
 
 
 
   
 
 
 
 
 
 
 
Heating Demand Calculation of build stock 
based on 3D City Models using SimStadt 

V. Coors, M. Bruse, R. Nouvel 

HFT Stuttgart, University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany 

{Volker.coors, marcel.bruse, romain.nouvel}@hft-stuttgart.de 

Introduction 

According to the Sustainable cities’ report (2013), cities account for over 70% of both energy 
consumption and carbon emissions. An urban energy transition is thus essential to lower greenhouse 
gases that contribute to climate change (Van der Veken et al. 2004) and the exhaustion of energy 
resources (Pérez-Lombard et al. 2008).  

In most European countries, cadastral databases store building information such as building footprint 
and usage. Such databases enable a first statistical assessment of the building energy demand based 
on statistical energy consumption ratio. In addition to the cadastre databases, virtual 3D City models 
allow to consider the real geometry of each building, including in particular their volume and 
façade/roof orientations. This data is a very valuable input for the calculation of individual building 
space heating and cooling demand, photovoltaic potential assessment and other energy-specific 
simulations at urban scale. Residents and urban planners could evaluate saving potentials according 
to different refurbishment scenarios in order to optimize the cost-benefit ratio. Estimates about solar 
potentials and heating/cooling demands could help citizens, urban planners and investors to 
contribute in the development of new energy efficient city districts.  

In this paper, the urban energy simulation platform SimStadt is introduced. It enables the mass 
processing of large 3D city models in urban energy simulations, mainly heating and cooling demand, 
CO2 emissions and PV potential analysis. The system has been evaluated and applied in the scope of 
the environment projection plan of the administrative district of Ludwigsburg with it 34 
municipalities and a total population of over half a million inhabitants.  

State of the Art 

There are already some examples for the use and enrichment of 3D city models with energy-related 
key figures for urban energy simulation purposes. Dalla Costa et al. (2011) extended 3D building 
models to allow the definition of thermal zones for buildings, opaque envelop elements for boundary 
surfaces and transparent envelope elements for openings. They used the 3D model of the town of 
Monteveglio to come up with energy indicators like distribution of heat losses, thermal consumption 
trend and domestic hot water production at building-scale and city-scale. Their data model can be 
used for energy simulations and allows the representation of feature types and relationships which 
are important for monthly energy demand calculations. Nouvel et al (2013) performed monthly 
heating demand calculations on 3D city models of Ludwigsburg and Karlsruhe and considered 

supplementary parameters such as building physics (e.g. window to facade ratios, U-Values) and 
building usage (e.g. occupancy density, average intern gains) for their calculations.  Schrenk et al. 
(2013) used 3D city models to compute energy balances and estimate building energy performances. 
They aimed at supporting planning activities at building-scale as well as city-scale. Prandi et al. (2013) 
performed solar irradiation calculations based on 3D city models. Kaden & Kolbe (2014) developed a 
system based to estimate energy demands of buildings in the city of Berlin to calculate a building 
energy performance label for space heating and hot water. 

CityGML 

The CityGML standard (Gröger et al. 2012) of the Open Geospatial Consortium is an internationally 
accepted model for the storage and exchange of 3D city models. In contrast to pure visualization 
formats such as VRML/X3D and Collada, CityGML is a feature-oriented semantically rich data model. 
CityGML provides schemas for topographic objects in 3D city and landscape models with respect to 
their geometry, topology, appearance, and attributes. CityGML represents, digital terrain models, 
water bodies, vegetation, transportation, and city furniture objects. The Application Domain 
Extension (ADE) mechanism in CityGML enables the extension and also the restriction (profile) of 
CityGML to domain specific applications.  

The building module of CityGML supports a multi-resolution model with five discrete Levels of Detail 
(LoD) to represent the building geometry as shown in Figure 1. LoD 0 is similar to the 2D building 
footprint embedded in 3D space.  In LoD 1, the building footprint is extruded to either the highest 
point or the average height of the roof. Metadata will be used to give an exact definition of the 
building height in an LoD 1 data set. LoD 2 includes the geometry of the roof and may use image 
textures for the building façades. From LoD 2 on, a semantic decomposition of the building into 
boundary surfaces such as roof-, wall-, and ground surfaces can be applied. In Lod 3, the façade of a 
building is modelled in more geometric detail. In addition, windows and doors become semantic 
elements. The interior of the building is modelled in LoD 4.     

Figure  1.  The  Levels  of  Detail  1  to  4  of  CityGML  applied  to  an  example  building  of  the  Stuttgart  University  of 
Applied Science  

 
 
Most of the existing CityGML models consist of terrain and building data in LoD 1 and / or LoD 2, for 
example Stuttgart 3D, Karlsruhe 3D, Zürich 3D, and Graz (Xu 2015). Some of the 3D city models are 
freely available as open data (Berlin3D, Rotterdam 3D, Lyon (Plane 2015)), but most of them have to 
be licensed. 

In Germany, mainly two types of 3D city models have to be distinguished. On municipality level, 3D 
city models exist for most of the larger cities. These models usually follow the SIG3D modelling 
guidelines for building models (SIG3D 2013), with building geometry mainly LoD 2 including some 
buildings in LoD 3. On state level, 3D city models are available in LoD1 and partially in LoD 2 
(Oestereich und Schleyer, 2015), covering the complete building stock in Germany. These models 
follow the defined ADV CityGML profile (Gruber et al. 2014). This profile restricts the modelling 
possibilities of CityGML but enables a fully automated model generation based on aerial imagery and 
airborne laser scanning. On European level, a mapping to the INSPIRE 3D building model has been 
developed with the CityGML INSPIRE Application Domain Extension.  Figure 2 illustrates the role of 
these slightly different data models.  A detailed analysis of the data models is given in (Löwner et al 
(2012, 2013). 

Figure 2: Data models for 3D City models based on CityGML from municipality to European level  

In order to perform energy-specific calculations of 3D city models based on CityGML, not only the 3D 
building geometries are needed, but also additional energy-related key figures about building 
physics, energy systems and occupant behaviour. These figures are required for thermal modelling of 
the buildings and for predicting their heating demands more accurately. CityGML as of version 2.0 
offers only few semantic attributes relevant for energy simulation, such as the building function, year 
of construction, number of storeys and roof type. Other important energy-related key figures like 
thermal zones, energy systems and occupant behaviour cannot be encoded. These shortcomings of 
the CityGML standard from the energy simulation point of view led to the development of a CityGML 
application domain extension (ADE) for energy-related key figures. This so called CityGML Energy 
ADE allows the thermal modelling of buildings required for urban scale energy simulation according 
to the heat demand calculation standards ISO 13790 and DIN V-18599-2 (Nouvel et al. 2015). 

 
SimStadt Workflow for Heating Demand Calculation 

SimStadt is a modular urban energy simulation system which allows the composition and execution 
of different urban simulation workflows corresponding to various urban energy analyses based on a 
CityGML model. Among those are analyses on space heating and cooling demands, photovoltaic 
potentials and refurbishment scenarios (SimStadt, 2013).  

The system is modular in so far, that every simulation workflow is a sequence of exchangeable 
workflow steps. Each workflow step performs a defined action and takes a defined set of simulation 
parameters. The computed results of each workflow step are passed on to subsequent steps. In the 
following an overview of the SimStadt's DIN V-18599 compliant monthly heating balance workflow is 
given. Figure 3 shows the user interface for this SimStadt workflow.  

Figure 3: SimStadt Workflow for space heating demand. The figure shows the radiation processor.  

The presented approach is based on the heating demand calculation method described in the 
German norm DIN V-18599 (based on the same methodology than the European norm ISO 13790). 
Before we describe the approach in detail, the calculation method is outlined in the following 
sections.  

The norm DIN V-18599-2 describes the heating demand calculation of buildings considering the 
heating energy balance inside building thermal envelope. In this balance, the following heat sources 
and heat sinks are considered on a monthly basis: transmission heat sinks, ventilation heat sinks, 
internal heat sources (occupants, electric appliances, e.g.), heat sinks for irradiation losses and heat 
sources due to solar insolation. 

The monthly energy balance method is basically a static energy calculation method. However, the 
heat capacity of a building is considered for the calculation of the average building time constant. 
This time constant and the resulting utilization factor allows for consideration of the simultaneity 
effect between heat sinks and heat sources in the monthly energy balance. 

 
This method has input data requirements which can partly be satisfied through the 3D city model 
itself, through benchmarking values or standard values (e.g. European Building Typology Tabula 
(TABULA, 2015), Standard usage data from the DIN V-18599-10): 

  Geometric data: building volume, areas and orientation of outer- and shared walls, roofs and 

basement. 

  Building physics data: Heat transfer coefficients (U-values) of the different components, total 

energy transmittance (G-value) of the transparent components, total heat capacity, 
infiltration rate and thermal bridge factor of the building. 

  Building usage data: Density of occupation and resulting internal gains, schedules for heating 
and ventilation (times of reduced operation mode, set-point temperatures and air change 
rates). 

Monthly weather data: local monthly ambient and sky temperatures together with insolation per 
orientation. This data is not part of the building model, but necessary to calculate the heating 
demand of the buildings in a given climate and location. 

For the assessment of the building energy performance the calculated heating demand is generally 
normalized over the heated area like in the Energy Performance Certificate. 

Use Case: Administrative district of Ludwigsburg 

The administrative district of Ludwigsburg is located in the center of the state of Baden-Württemberg 
in Southwestern Germany. The district has an area of 687 km2
civilians. The whole study area includes 34 municipalities in the administrative district of 
Ludwigsburg. The city of Ludwigsburg was not part of this study, but the same methodology was 
applied to the city of Ludwigsburg already in 2014 (Nouvel et al. 2014). 

 and a total population of 517.000 

An Energy action plan was conducted to identify and plan CO2 emission savings, based on a CityGML 
LoD 2 model. The data includes about 100.000 building models, with total data size on disk amounts 
2.4 GB. The terrain tiles count 1554 with size on disk about 43.5 GB. Each tile has a resolution of 1 x 1 
m and covers a ground area of 1 km2. The orthophotos count also 1554 (one for each terrain tile) 
with total size on disk of 77.4 GB. The ground resolution of the orthophotos is of 0.25 m. The 
orthophotos and terrain data are used for visualization purposes only.  

The building models are used in different workflows of the urban energy simulation platform 
SimStadt to assess the actual space heating demand and the related CO2 emissions per building, 
predict energy savings potential following different refurbishment scenarios, and identify the solar 
energy potential.  

For the whole studied area, the total yearly heating demand reaches 3.9 TWh, with an average 
specific heating demand of 145 kWh/m².yr. Considering the heating system distribution available for 
each municipality (from census data survey), this corresponds to 0.92 Megatonnes equivalent CO2 
per year.  

For the photovoltaic potential analysis, only roofs with a surface larger than 40 m² and receiving 
more than 1100 kWh/m².yr solar radiation have been selected. In total, the diffuse power central 

formed by all these roofs could generate 668 MWh/yr, which would cover 26% of the electric 
demand (for building electrical appliances) of the administrative district. 

Figure 4 shows the specific space heating demand per building in a 3D visualization. The visualization 
is done using X3D which enables a web-based access using X3DOM (Athanasios and Coors, 2015). 

Figure 4: Specific heating demand per building calculated with SimStadt. 

Conclusion and Perspectives 

In the recent years, considerable progresses have been realized in both domains urban energy 
simulation and 3D Geographical Information System, however without notable cross-domain 
interactions. The new urban energy simulation platform SimStadt combines both domains. The 
current version integrates workflows, which allow for first city-scale energy demand diagnoses as 
well as low-carbon scenario simulations. The definition of a workflow to calculate energy 
performance labels per building on regional and national level is feasible.  Meanwhile, it remains a 
great deal of work to model energy flows more precisely, with higher Level of Details, shorter time 
resolutions (typically hourly loads) and taking into account the whole complexity of urban scale 
interactions. In any case, based on its extensible modular structure and on the full potential of virtual 
city models, the new SimStadt platform may provide an adequate and powerful solution to plan and 
coordinate the energy transition, at the scale of neighbourhoods, cities or whole regions. 

Acknowledgement 

The terrain data and the orthophotos were provided by the District Office of Ludwigsburg. The 
CityGML data were provided by the Department of Geoinformation and Land Development Baden-
Württemberg (LGL | Landesamt für Geoinformation und Landentwicklung). The contracting entity of 
the project "Integrated Climate Protection Concept for the district of Ludwigsburg" is the 
Department 21 of the district Ludwigsburg. The concept is developed by a consortium headed by 
Drees & Sommer. The funding of the SimStadt project by the Federal Ministry for Economic Affairs 
and Energy is gratefully acknowledged.  

 
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Gröger, G., Kolbe T.,  Nagel, C., and Häfele, K. (Editors) 2012: OpenGIS City Geography Markup 
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in der Praxis, zfv 2/2013, 138. Jahrgang, Wißner Verlag, pp. 131-143. 

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1/2016 

 
 
ZEROcarbon 
Roadmap2020

Sonderborg - a Bright Green Danish Municipality 

Bright Green Business

Roadmap for creating green growth and a ZEROcarbon Sonderborg 

AugustenborgNordborgGuderupHøruphavSonderborgBroagerGråstenThe ProjectZero Road to Zero 2020

Sonderborg is a Danish Municipality beautifully located 

What we do

near the Baltic Sea in Southern Denmark. We are 300 km 

We strive to create market-driven concepts benefitting 

drive from Copenhagen, and 200 km drive from Hamburg 

citizens and businesses. We do this by developing new 

Germany. Sonderborg has a population of 76,000 people, 

solutions and collaborative partnerships based on smart 

of which 27,000 people live in the Sonderborg city area. 

climate solutions. 

The 850 year old Sonderborg castle and the Battlefields 

of Dybbøl (1864) showcase Sonderborg’s rich cultural 

What we aim at achieving 

heritage together with a vibrant modern community. 

In 2007, the City Council of Sonderborg adopted the vi-

We focus on good education, culture, living and business. 

sion of becoming ZEROcarbon. Since then, approximately 

Sonderborg is home for leading global tech companies 

like Danfoss and Linak. 

What we want

800 new green jobs have been created. By 2014, the 
Sonderborg-area has reduced its CO2 emission by 30%, 
well beyond the first sub-goal of 25% emission reduction 

by 2015. The next sub-goal is to achieve a 50% reduction 

Sonderborg aims to be a ZEROcarbon growth area 20 

by 2020 (compared to the 2007-baseline). This will create 

years ahead of Denmark. ZEROcarbon means phasing 

almost 1000 new jobs, and the perspective of becoming 

out all energy-related carbon-emissions. 

ZEROcarbon by 2029.

ProjectZero is the vision for making Sonderborg ZERO-

carbon by 2029, creating sustainable growth and new 

A robust open platform for participation 

green jobs along the road - based on ambitious carbon 

Roadmap2020 is built on ProjectZero’s past years of 

reduction goals and new Bright Green Business solutions.     

experience from Roadmap2015 and the Masterplan2029, 

Our vision is a powerful innovation engine for new solu-

which provide the overall framework for the creation of 

tions and business concepts. The innovation engine will 

the ZEROcarbon Sonderborg by 2029. Roadmap2020 

show the future use of energy, food, water and other 

outlines our plan to meet the next set of goals.

resources. 

www.projectzero.dk

2

CopenhagenHamburgSonderborgWe achieve our goals! 

In Sonderborg we think big and act.

sions. The concepts do not always represent the entire 

solution, but they are facilitators for “Sonderborg’s Great 

We are driven by enthusiasm - new technology and 

Transition”.   

science can be used to create a better life. Within that 

principle, a major part of our business is developing new 

Sonderborg´s Great Transition is a story about solutions

technological solutions to save power, for which the whole 

and determination created through commitment, col-

world desires.   

laboration and networking across industries and civil 

society. It is the story of how we develop green lifestyles 

Many people travel vast distances to visit Sonderborg to 

for ourselves and the next generations. Our children are 

see how our citizens, institutions and businesses jointly 
transform the area to a sustainable CO2 neutral future. 
ProjectZero’s Roadmap2020 is an important milestone on 

given a tailored curriculum from kindergarten onward.  

They are taught how to live in a sustainable, innovative 

and interconnected world. These values also charac-

the road to becoming ZEROcarbon by 2029. 

terize the everyday life of our university and international 

businesses and thereby also support the Bright Green 

The challenges are significant, but together we will show 

Business creation.

that it´s possible to solve them.

We have a strong local interest in climate and energy. Our 

tions, educational institutions, utility companies and our 

desire is to do more to identify and develop the solutions 

municipality to jointly implement the next exiting phase 

that are necessary to achieve the 2020 goal. Several of 

of the ProjectZero vision.

It is now up to all of us: citizens, businesses, organiza-

the factors that will be decisive to a continued success 

are yet unknown. It is not yet possible to draw the com-

plete roadmap towards the year 2020.   

However, through active participation of approximately 

100 local citizens and national experts, we have suc-

ceeded to create specific proposals for future solutions 

and business concepts. These concepts outline how the 

climate challenges of tomorrow can be solved.  

Our concept opens new opportunities for market-based 

Peter Mads Clausen

energy and climate solutions though new partnerships. 
This creates business and jobs while reducing CO2 emis-

Chairman of The ProjectZero Foundation (2007-2014)

Chairman of The Danfoss Foundation

2

3

How to use Roadmap2020: 
Focus on green growth and job creation

ProjectZero is the vision of creating economic growth and 

Roadmap2020 also describes three new specific 

new jobs in Sonderborg, transitioning to a ZEROcarbon 

business-oriented Development Themes, to be incor-

society. This is what we call “The Great Transition”. By 
2020, a 50% CO2 reduction is expected to be achieved. 
The ProjectZero vision is a demonstration for Denmark 
and the rest of the world on how CO2 reductions and 
growth go hand in hand. 

porated by private and public stakeholders assisted by 

the ProjectZero Company: SmartGrid, Bio-Economy and 

Green Transportation.

Three new Development Themes 

SmartGrid: The joint regional development of a Smart 

Roadmap2020 is a specific proposal for how the Sonder-

Grid phasing-in more renewable energy e.g. solar and 

borg area can achieve its ambitious 2020 goal through 

wind. 

market-based partnerships that accelerate the transition 

and kick-start new development projects.

Bio-Economy: Active involvement of businesses that 

use biomass resources in production of food, energy, bio-

Roadmap2020 consist of “SixBigConcepts.” Three focus 

based products and the establishment of new bio-energy 

segments are based on the Roadmap2015 learning: 

production facilities in the Sonderborg area. 

Community & Citizens, Business and the Public Sector. 

Each of the focus segment programs have been reviewed 

Green Transportation: Develop coherent solutions 

and fine-tuned to maximize impact based on value 

and concepts for green transportation in urban and rural 

creation, networks and cross-sector synergies for the 

areas.

Roadmap2020.

Three Focus Sectors

For the SixBigConcepts to work, it requires commitment 

and active stakeholder participation within and outside the 

Community & Citizens: Educating and engaging the 

Sonderborg area. Therefore, Roadmap2020 is an open 

citizens based on value and benefit creation. Specific 

platform for all interested stakeholders to participate and 

programs include ZEROhome, ZEROdrivers license and 

benefit from. Bright Green Business and learning potential 

House of Science.

will occur from growth as a result of new partnerships, 

technology development and implementation towards 

Businesses: Partnering with business to develop and 

2020.  

share best practices to save energy and reduce carbon-

footprint. Specific programs include ZERO+Business, 

The following pages will describe each of the SixBigCon-

ZEROshop and financial optimization. 

cepts by outlining the goals, challenges, best practice 

examples and specific tasks to come.

Public Sector: Create policies and funds to reduce the 

public sectors carbon footprint by energy retrofitting 

public buildings and schools in cooperation with utility 

Peter Rathje

companies. 

CEO, ProjectZero Company

4

The vision: 
A Green Growth Engine for Sonderborg 

The SixBigConcepts

Since its creation in 2007, the ProjectZero vision has 

The vast majority of the new jobs (42%) will be created 

inspired citizens and businesses to implement green 

within the building and construction sector, due to the 

initiatives. An impact analysis conducted by NIRAS 

expected investments in new energy-efficient solutions by 

(an independent Danish consulting engineering company) 

homeowners and citizens. 

of the impact on job creation and investment concluded 

in 2013 that implementing the ProjectZero vision has cre-

In the coming years, the Sonderborg area expects to 

ated about 800 new green jobs since 2007. 

install large costal wind turbine project and a new bio-

refinery. The related job creation has not been calculated 

The implementation of Roadmap2020 is projected to 

because the exact implementation of these projects are 

create additional 900 new green jobs annually. Almost 

not yet clear. Due to similar uncertainties, the jobs created 

80% of the new jobs will be created in Sonderborg and 

by Sonderborg’s Bright Green Business solution companies 

Southern Jutland. Large capital investments are expected 

is not included in our economic projections.     

in tandem. 

From 2015 onward, it is expected that the Sonderborg 

areas’ green transition will “attract” 700 million DKK 

(129.6 million USD) in capital investments per annum. 

Local businesses are expected to contribute with about 

                                        Annual job creation 

 Accumulated jobs

200 million DKK (37.0 million USD) every year as well. 

Sonderborg Municipality 

490 

The analysis shows that regional, sub-regional and cross-

border projects and cooperation have a great additional 

potential for job creation in Sonderborg. 

Additional growth potential will be generated by export of 

Bright Green Business solutions, international business

participation in new demonstration projects, visitors 

spending and the enhanced global branding and market-

ing of ProjectZero’s efforts.

9 %

21 %

19  %

18 %

28  %

5 %

The SixBigConcepts Carbon reductions

Remaining part of 
Southern Jutland  

Remaining part of 
Southern Denmark  

Greater Denmark  

Total 

276 

67 

154  

987 

2456

1379

335

771

4941

The total estimated direct and indirect impact on employment as a result 
of the implementation of Roadmap2020. The indirect employment impact 
is only calculated for the Municipality of Sonderborg. Accumulated figures 
cover the accumulated job creation during 2015 - 2020. 

Community/Citizens

Businesses 

The Public Sector  

SmartGrid 

Bio-Economy 

Green Transportation 

CO2 reductions 2015 - 2020
The distributions of the 2015-2020 accumulated carbon 
reduction (150,000 tons of CO2) in per cent of the total 
reduction 

4

5

 
Udviklingstema

Focus segment 
Community & Citizens 

Goals 
Community and citizens are important resources in the 

The good examples 
Since 2010 ProjectZero has created the ZEROhome 

transition and will continue to be challenged by improving 

program to engage homeowners to energy retrofit their 

energy efficiency. Not only heating in their homes, lighting 

buildings/homes. Over 1,100 private homeowners in 

and appliances, but also awareness of the use and sources 

Sonderborg have been visited by trained ZEROhome 

of energy.     

During 2015 to 2020 the focus segment of Community 
& Citizens must reduce their CO2 emissions by more than 
33,700 tons. This corresponds to 21% of the total 

Roadmap2020 reduction. 

Challenges
New programs and initiatives shall be driven by business, 

based on new cross sector partnerships to offer the 

community and citizens the best possible private energy 

solutions. 

Attractive solutions for energy-efficient renovations must 

be available and easily understood by all citizens.

energy advisors to improve awareness about options and 

motivation to act. At the same time, the ZEROconstruc-

tion program has trained 65% of the area’s craftsmen in 

state of the art energy consulting and -solutions.

The program support have enabled over 600 homeown-

ers to invest an average of 155,000 DKK (28,700 USD) 

in mainly building improvements. The average household 

saved 5,800 MWh. The savings are equivalent to 2,000 
tons of CO2 per-annum for the entire Sonderborg area. 
The total investments by private home-owners equivalent 

to 105 million DKK (19.4 million USD) since 2010. 

The specific tasks 
We will continue to facilitate the training of efficient and 

dedicated energy advisors in the construction industry to 

Better and more attractive financing options must be 

ensure that home owners have the best information, guid-

secured to motivate citizens to make the necessary 

ance and confidence in the transition. In order to speed 

investments. 

up the transition of the public housing sector, a new inno-

vative cooperation between Sonderborg`s seven housing 

Local craftsmen must take a lead in providing the citizens 

associations and their residents will be established.  

with information and guidance on energy renovations.     

Key players

•  The ZEROconstruction program and EUC Syd (technical  

  college and vocational training school) train craftsmen as  

  energy advisors

•  The housing associations of Sonderborg in a project  

  cooperation with EUC Syd and contracting company  

  Jorton

•  The private rental housing owners 

•  The citizens of Sonderborg

6

 
New participation methods will be developed for the 

transition in the private rental buildings sector. 

In order to promote energy renovation of the housing 

Did you know…

•  There are 18,600 private homes in Sonderborg,  

stock, new concepts and cooperation with partners will 

  averaging 65 years of age?

•  That approximately 94% of Sonderborg`s houses have  

  an energy label of “D” or less?

•  From 2016, it is not permitted for the homeowners to  

replace an old oil burner with a new one, if they are  

  able to connect to district heating?

•  That EU strong policy ambitions for energy-retrofitting  

  all buildings so that energy consumption in buildings/ 

  homes by 2050 will be reduced by 90% ? 

be developed. Specifically, cooperation and solutions with 

local craftsmen must be developed to accelerate the shift 

of heating sources to renewables.

Impact and value creation
We will get an energy-optimized housing stock across all 

housing sectors. The stock will be better prepared to meet 

the future Nearly Zero Energy Buildings Directive, promul-

gating that the European housing stock to use 90% less 

energy by 2050.

Less energy consumption improves the building’s energy 

label, lowers the energy cost and improves the building’s 

sales price, when the building is offered for sale. 

The initiatives also ensures further job creation and growth 

in the construction industry.

6

A B C D E F G

7

 
Focus segment 
Businesses 

Goals 
Sonderborg’s manufacturing, trade, agriculture and 
construction industries must reduce their CO2 emissions 
by another 28,000 tons before 2020. This corresponds to 
18% of Sonderborg’s total CO2-emmission reduction from 
2015 to 2020.

All local companies must be aware of opportunities and 

The good examples 
ProjectZero has developed a business concept called 

ZERO+Business. It consist of two programs: the ZERO-

company and the ZEROshop program. 

The ZEROcompany program trains companies in the most 
effective practices to reduce energy consumption and CO2 
emissions while maintaining or improving competitiveness 

motivated to participate and contribute.

in the market. 45 local companies are committed to the 

ZEROcompany program, reducing their carbon footprint 

The majority of the companies must commit to specific 

from 10 to 45%. 

initiatives in order to reduce their energy consumption. 

Accelerate green growth further through scaling up Bright 

products and services offering climate and energy saving 

Green Business solutions developed by Sonderborg’s 

solutions to all kind of applications – what we call Bright 

leading technology companies.  

Green Business. 

Several of these companies have specifically developed 

Challenges
The financial crisis caused a decrease in economic 
activity, which also decreased CO2 emissions. As the 
economic recovery commences, companies will have to 

work hard to maintain and improve the post-financial crisis 
levels of CO2 emissions. The economic recovery must be 
CO2 neutral. 

The ZEROshop program enables storefront retailers to save 

energy and lower carbon emissions on site or in their sup-

ply chain.  Almost 100 of Sonderborg´s shops and small 

business have committed to the ZEROshop program. 

They have received a free energy screening by ProjectZero 

or local electrician companies, which has helped them to 
reduce their CO2 emission from 10 to 45%. 

Analysis shows that our companies normally invest in 

projects with a payback period of four years maximum. 
This is challenging as many investments in CO2 reduc-
tion and energy savings take longer to accumulate gains. 

This will continue to be a major sticking point for many 

companies. 

The specific tasks
Sonderborg areas’ Corporate Social Reasonability (CSR) 

culture must become more cooperative in finding joint 
solutions to reduce CO2 emissions while growing and 
creating green jobs.

Key players

•  Businesses, shops, energy companies, consultants  

  and solution providers

•  Electrician companies are trained as energy advisors  

  and assist shops to achieve further carbon reductions

Climate strategy 
and action plan

Involvement and 
branding

Carbon 
Disclosure 
Project

CSR, buttomline
and savings

Follow–up 
and further 
reduction

 go ZERO

Carbon-footprint 
Verification

8

A C O 2 neutral 
So nderbor g

ProjectZero and partners will facilitate a best practice 

business solutions knowledge base to be shared. This will 

enable businesses to develop ambitious carbon footprint  

reductions strategies, improve their bottom line and 

strengthen their CSR-activities. These synergies go hand 

in hand with the implementation of Roadmap2020.

Value and impact
The ZERO+Business concept does more than facilitate a 

better bottom line and improve the environment: partici-

pating firms in ZEROcompany and ZEROshop demon-

strate strong corporate social responsibility values. This 

is key to build a strong loyal community relationships and 

develop new Bright Green Business markets. 

By executing the ZERO+Business concept another 250 

jobs are annually expected to be created within the  

Businesses focus segment.

The successful creation of new Bright Green Business 

solutions will be based on businesses’ core competencies, 

lessons learned in Sonderborg’s demonstration lab and 

scaling-up opportunities abroad.   

Did you know...

•  Global technology leaders like LINAK, OJ Electronics  

  and Danfoss have their corporate headquarters in  

  Sonderborg

•  LINAK is a company that is pioneering the actuator  

  market within healthcare, office, energy and home-  

  based lifting applications. Linak employs people in  
  26 countries. LINAK aims to become CO2 neutral by  
improving energy efficiency and using energy from 

local renewables

•  OJ Electronics is a leading manufacturer of floor  

  heating control systems. OJ Electronics has devel- 

  oped a corporate climate and energy policy which has  
resulted in a 46% CO2 reduction from 2011 to 2013. 

•  Danfoss is a global energy and climate technology  

leader, with operations in more than 100 countries 

  and with more than 24,000 employees

•  The businesses in the Sonderborg- area accounted 

for 39% of the area’s CO2 emission by 2011

•  There are approximately 300 agriculture farms in 

  Sonderborg

Committet to a carbon
neutral Sonderborg

8

9

 
 
 
 
 
Focus segment 
The Public Sector  

Goals
The public sector must reduce its CO2 emission by 8,000 
tons by 2020. That is twice the expected reduction by 

The specific tasks 
The ZEROcity business concept will enable Sonderborg 

as an entire community to become a best practice model, 

2015. The reduction must primarily come from heating, 

demonstrating the newest energy and climate solutions 

lighting and appliances.   

used for the green transition.

Challenges 
From 2007-2011 the public sector was unable to curb 

Collaboration across sectors and stakeholders are key to 

success. The transition will include new innovative finan-

CO2 emissions, despite several good cases and ambi-

cing instruments and solutions. Increased focus on Bright 

tions. We need a strong commitment to achieve Sonder-

Green Business with global scaling will accelerate the 

borg’s 2020 goal.

The good examples
The Municipality of Sonderborg has shown a strong 
commitment to achieving a 25% CO2 reduction within its 
own operation. Such initiatives include the monitoring of 

value creation to participating stakeholders and consoli-

date “Sonderborg’s Great Transition”. 

The professionalization of new “green business tourism” 

initiatives with efficient and effective service and follow up 

on visiting guests will increase the awareness of Project-

general operational energy use, retrofitting of municipal 

Zero. Supporting educational learning and consulting 

buildings and schools and facilitating green learning and 

must be strengthened to inspire cities all over the world to 

education. 

get started and achieve ambitious ZEROcarbon goals.

Sønderborg Forsyning is Sonderborg’s public water, 

Establishment of strong stakeholder partnerships across 

energy and waste utility company. The utility company has 
initiated a plan to transition into CO2 neutral operations by:
•  Bio-gas generator using sewage 

the municipality who can challenge the current thought 

paradigm will be required. Sonderborg must actively 

engage in regional networks both north and south of the 

•  Efficient sorting of household waste by citizens, 

border to create a larger market for green business devel-

•  Mounting 1,300 m2 of solar panels on its administration  

opment and to impact regional thinking and policies.  

  buildings 

•  Adding SmartGrid management solutions to its thous- 

  ands of water pumps

The area’s schools have created a “green thread” in the 

school curriculum from kindergarten to Ph.D courses. 

They are based on climate, innovation and sustainabil-

ity. The City Council supports the green curriculum that 

started in the school year 2013/14. 

Did you know… 

•  Sonderborg is visited weekly by delegations from abroad  

  who seek inspiration for own local carbon reduction  

initiatives

•  Sonderborg accounted for 29 out of 35 green transition/ 

The municipality’s school administration has collaborated 

  sustainability projects in Southern Jutland in a national 

with the (private non-profit) School of Production and the 

  promotion project featured by Danish National Television 

in 2013. www.voresomstilling.dk  

EUC Syd (the technical college and vocational training 

school) to retrofit school buildings and construct new to 

be more energy efficient. 

This demonstrates environmental sustainability in the 

actually learning environment. This shows our youth we 

are serious about climate change and encourages their 

commitment, participation and support in the future. 

10

 
 
 
 
 
 
Value and impact
8,000 tons of CO2 can be saved for the benefit of the 
climate while taxpayers save operating costs. 

The “Bright Green Business” solutions and knowledge is 

scalable and has large global market potentials. Cities act 

while nations talk and cities represent a new growing 

buying group for the business segment.

Businesses and citizens have new competencies 

Key players 

•  The Municipality of Sonderborg (as company and local 

  authority) and utility company “Sønderborg Forsyning”  

  with its ambitions to become a first mover in 

implementing SmartGrid solutions

•  The Public-Private Partnerships behind ProjectZero in  

  cooperation with firms, utility companies, universities  

  and other institutions from all over the world that will  

  assist creating a full-scale transition - based on what  

and embrace new sustainable behavior necessary for 

  cities can achieve 

a sustainable transition of our cities.

•  Sonderborg´s learning and educational institutions

•  Citizens and businesses’ of Sonderborg

10

Luftfoto Syd

11

 
Development Theme 
A Smart Grid

Goals 
The ambition of the Smart Grid Development Theme is to 

The good examples 
Sønderborg Fjernvarme (district heating company) has 

develop and demonstrate the future integrated SmartGrid 

established Denmark’s first commercial geothermal hot 

solution at city-level in Sonderborg. The goal is to develop 

water plant and is one of the greenest district heating 

a full scale demonstration that strategically connects the 

producers in the country based on multiple renewable 

energy demand and supply, based on electricity as lead-

resources. Gråsten Fjernvarme follows the same model 

ing future energy source, combined with green district 

transitioning from fossil fuel to solar heat and biomass 

heating and green gas from renewable sources. 

- and was 2015 nominated as Sustania100 candidate.    

On the supply side, Sonderborg´s renewable energy pro-

Sønderborg Fjernvarme has increased its impact from 

duction capacity must be expanded with 25 MW through 

34% (in 2013) to cover more than 43% of Sonderborg´s 

a combination from 15 MW wind and 10 MW of solar 

total heating demand. The wind energy capacity has been 

energy output by 2020. 

increased by 50% and the solar capacity doubled within 

Demonstration and development projects must promote 

two years.

the integration of renewable energy into a “SmartGrid” 

Several SmartGrid-related projects have been initiated.  

demonstration area. The first demonstration and develop-

ment project of this kind is ZERO-GI. 

The company GODevelopment is creating a new innova-

The continued expansion of Sonderborg’s district heating 

lates electricity from wind in a high-pressure pumped 

networks must be coordinated and supported. This will 

water-based system. Enabling surplus of electricity from 

ensure a stronger utilization of the established renewable 

wind to be stored and turned back to electricity when 

energy production facilities.

required by the SmartGrid.

tive energy storage system. The GOD-solution accumu-

From 2015 to 2020, the SmartGrid initiatives are expected 
to reduce Sonderborg’s CO2 emissions by at least 43,000 
tons. This is equivalent to 28% of the total scheduled 

Future surplus of wind energy will also be accumulated 

as hot water in Sonderborg`s district heating networks.  

Augustenborg Fjernvarme has installed an electric boiler 

reductions during 2015-2020.

Challenges
Securing a strong community support to the establishment

and Broager Fjernvarme plans to install a large electric 

heat pump to produce heat from green electricity when 

the energy system is overloaded with wind energy. 

of large renewable installation facilities, such as wind farms.

25 smart heat-pumps have been installed in private 

Securing value creation for all parties involved in the 

homes. They save money for the homeowners by accu-

expansion of renewable energy capacity and within the 

mulating heat in the buildings when energy prices are low 

SmartGrid projects. 

and gather data for future development of new SmartGrid 

Managing changing frames and policy conditions pro-

home-solutions. 

vided at national and EU-level. 

Nordborg

Guderup

Augustenborg

Høruphav

Sonderborg

Gråsten

Broager

12

Key players

Wind Power Capacity: citizens (future neighbors), land-

owners (investors and hosts), energy companies (investors 

and distributors), the Municipality and the City Council 

(authority and a frame setting), university scientists and 

NGO`s like  the Danish Society of Nature Conservation. 

Photovoltaic Capacity: house associations, the Munici-

pality, farmers, local technology suppliers and SE 

ZERO-GI: the Danfoss Company, SE, CLEAN Cluster, 

district heating companies, A/C-service companies and 

supermarkets.

The specific tasks
Sonderborg must establish an additional 15 MW from in-

land wind turbines in Sonderborg before the end of 2020 

by initiating part of Sonderborg`s municipal “Theme Plan 

for Wind Turbines”. The establishment must cooperate 

with all stakeholders and maintain high local ownership.  

Implement targeted campaigns to ensure continued 

Did you know...

•  Sønderborg Fjernvarme and Gråsten Fjernvarme supply  

the citizens and companies with district heating based  

  on almost 100% renewable energy sources

•  The world´s first intelligent heat pump was installed by  

  a Sonderborg citizen Christel Juhl Thomsen in Høruphav  

expansion of Sonderborg`s total installed photovoltaic (PV) 

in 2010

solar capacity. This will be based on a collaboration with 

the area’s PV-stakeholders and include house associa-

tions, farmers, the municipality (as building owner and as 

authority), local technology suppliers and SE.  

•  During 2013, almost 9 MW out of Sonderborg´s 2015  

  goal of 10 MW of Photo Voltaic was installed. During  

  2014-15 additional 9 MW PV capacity has been added

Develop and establish a ZERO-GI supermarket demon-

ZERO-GI is a play of words: synergy and exergy (energy 

stration plant in Sonderborg and pursue other SmartGrid 

quality and availability). The ZERO-GI concept aims to use 

opportunities in partnerships between relevant stakeholders. 

excess heat and overcapacity in cooling systems (e.g. 

Value and impact
A dynamic and SmartGrid-based energy system is key to 

from supermarkets) to deliver heat into the district heating 

system in an intelligent and dynamic way. 

achieving both national and international targets phasing-

Glossary:

out fossil fuels and phasing-in energy from wind and other 

PV (Photo Voltaic) is a term for a solar cell-based technol-

renewable sources. And it adds economic growth and job 

ogy that produces electricity from solar radiation.  

creation at local, regional and national levels.  

Expanding the production of renewable energy creates 

value for local investors and new green jobs during the 

implementation phase. 

Sonderborg becomes increasingly self-supporting with 

renewable energy.

RE (Renewable Energy) is a generic term for energy 

based on sustainable energy sources such as wind, sun, 

geothermal etc.

12

13

 
 
Development Theme
Bio-Economy

Goals
Towards 2020, Sonderborg aim at transitioning into a 

national demonstration area for a sustainable manufacture 

of biomass based products. 

Sonderborg  already has three bio-gas plants: Clausen 

Biogas at Nord-Als (manure-, biomass- based), the 

Sonderborg Sewage Treatment Plant (sewage sludge 

from waste-water treatment-based) and at Skodsbøl 

Deponi (waste-based).

New sustainable concepts and systems shall be developed 

and tested based on local resources and generating eco-

nomic growth and new jobs.

Strong local demand for locally-produced bio-energy shall 

contribute to phasing out the local use of fossil fuels.

During 2015-2020, the Bio-Economy Development 
Theme is expected to reduce Sonderborg’s CO2 emis-
sions by   more than 30,500 tons, equivalent to 19% of the 

total expected reduction during the same timeline.

Challenges 
The competing demand for biomass from food, materials 

and energy manufacturing is a challenge for rethinking the 

future use of resources.

There is still uncertainty regarding which technology 

solutions will become profitable standards in Denmark 

and abroad.

Win-win-win - all value chain stakeholders must see a 

profitable business and have incentives to participate 

actively in the transition as suppliers, manufacturers and 

consumers.

It is challenging to secure financing to develop scale 

production of bio-based materials.

In collaboration with the Municipality of Sonderborg, the 

Sønderborg Forsyning, LandboSyd (farmers association) 

and ProjectZero have surveyed the biomass-potentials of 

the Sonderborg-area and concluded that Sonderborg has 

great potentials for exploring the Bio-Economy opportu-

nities.

Sønderborg Forsyning is a partner in an unprecedented 

initiative to collect and use the area’s organic household 

waste for new biomass facilities.  

Since March 2012, the sewage treatment plant in 
Sonderborg has been CO2 neutral. The facility uses 
sewage sludge to produce biogas, which is converted 

into electricity and heating.

The specific tasks
Roadmap2020 recommends an integrated three-pronged 

approach to a future sustainable use of the Sonderborg 

area’s biomass resources:

Establishment of a bio-gas plant that will produce 10  

million Normal Cubic Meters of gas. This shall be based  

on a combination of livestock manure and bio-mass  

waste from other local sources. 

A “Waste-to-value” demonstration project turning food 

waste and other residual biomass into high-value  

products such as proteins and fertilizers. Residual  

waste is utilized at the biogas plant.

The good examples
Sonderborg’s district heating plants have almost phased 

Establishing a bio-refinery in the Sonderborg-area,  

alternatively make use of Sonderborg´s biomass in a new 

out fossil fuels and instead undertaken extensive use of 

joint regional solution.

bio-mass to produce heat for buildings. The plants in 

Sonderborg, Gråsten and Vollerup turn waste, wood 

chips, wood pellets, straw and bio-oil into heat. District 

heating has created an increased local demand for bio-

mass. The use of bio-mass has become an important 
step towards a 100% CO2 neutral heating.

14

   
 
Value and impact 
Biomass contributes significantly to a future fossil-free 

Did you know...

society.

•  More than 700,000 tons of manure are produced per  

  year in the Sonderborg-area - mainly from pig-farming

The development of bio-economic solutions and systems 

has a considerable export potential and can potentially 

•  Biomass and wind energy are the two main pillars of  

create a significant number of local jobs.  

  Denmark’s future energy system

The use of bio-fuel and biogas will support a green transi-

•  Proteins, food ingredients and phosphorus can be  

tion within the transport sector.

  extracted from food waste, green residuals and 

  process waste from the food industry

Companies and public institutions can strengthen their 

green profiles by switching to bio-energy based products 

and services. 

Key players

•  Biomass suppliers, owners, manufacturers, farmers,  

  Sønderborg Forsyning, local food manufacturers etc.

•  The Municipality of Sonderborg (as authority, facilitator  

  and consumer)

•  Energy companies and operators (SE, DONG Energy,  

  NGF Nature, E.ON and others) 

•  End users (businesses, energy producers, citizens, 

the transport sector)

14

15

 
 
Development Theme
Green Transportation

Goals 
Sonderborg is a pioneer in implementing green transpor-

The specific tasks
Towards the year 2020, it is important to change citizen’ 

tation solutions and today the Municipality of Sonderborg 

attitudes on Green Transportation options. We must  

enjoys both the status of being Denmark`s first “Green 

generate the means for information, education, and infra-

Transportation Municipality” and the “bike-municipality of 

structure at both the collective and individual levels 

Southern Denmark”. But transitioning to Green Transpor-

– we must make Green Transportation easy, safe and 

tation requires also new thinking, policies, planning and 

smart especially for citizens.

more action. 

From 2015 to 2020, the Green Transportation Develop-

provide answers to relevant questions and influence 

ment Theme initiatives are expected to reduce Sonder-
borg’s CO2 emission by   more than 15,000 tons. This 
accounts for 9% of the total scheduled reductions during 

citizens to start using green transport options.

Campaigns will support people switching use to bicycles 

(and electric bicycles) instead of cars.

A new local Green Transportation portal webpage must 

the same timeline.

Challenges
Economic gains of transitioning to green methods of 

transportation.

Changing behavior and attitudes.  

Lack of user familiarity with alternative green transpor-

tation means.

The good examples 
70 Sonderborg families have participated in the 

“Test an Electric Car” program. 

Since 2009, the Municipality of Sonderborg has been 

certified as a Green Transportation Municipality.

Post Danmark (Danish Mail) and the Municipality of 

Sonderborg use electric bicycles and electric vehicles. 

Danfoss, SE, Sonderborg Forsyning among other com-

panies have also integrated electric vehicles in their 

operations. 

Companies with more than ten vehicles will be offered 

“fleet consultancy”, where a consultant can contribute to 

a more efficient utilization of the fleet. 

The Municipality is exploring the possibility of establishing 

a Green Transportation center to create a green service 

and logistics center for professional commercial and 

public transportation. This will create new opportunities to 

bundle transport logistics making better use of capacity.

Walking /
Running

Cycling

Utility bikes

Taxi/rentalcar

Public trans-
portation

Car sharing

Private car

Flight

16

The Traficpyramide
 Source: Bicycle Innovation Lab

Value and impact 
There are three major benefits of the Green Transportation 

Did you know...

Development Theme: 

•  More than 13% of Sonderborg´s citizens are using  

•  The citizens health will improve, due more widespread use  

  bicycles as a primary means of  transportation.

  of bicycles and corresponding reduction in car traffic. This  

  will potentially also lead to less cars in the old city center  

•  More than 32,000 cars are registered in Sonderborg,  

  and a reduction in rush-hour traffic. 

  with an average age of 9.3 years.

•  Noise, particulate and CO2 emissions in urban and rural  
  areas will be reduced as more vehicles become “green”. 

•  Sonderborg’s green transition will be highly visible to both  

residents and visitors due to more green vehicles in use.

•  Sonderborg`s largest companies in relation to transport  

  of goods are Danish Crown (food), Danfoss (industry),  

  LINAK (industry), Sønderborg Forsyning (waste) and 

the local hospital.

Key players

•  Motivation and participation of citizens are important,  

  as citizens are private transport consumers as well as  

  employees. It is key to improve citizens’ knowledge and  

  experience with green transportation means.

•  The Municipality of Sonderborg is a transportation front  

runner with its own cars and the public transportation.  

  The municipality undertakes information campaigns on  

  Green Transportation.

•  The participation of local companies are important. 

  Companies participate by switching company vehicles,  

  develop low carbon logistic plans for transportation of  

  goods etc.

16

17

 
 
 
 
A green thread in education

Sonderborg’s transition to a CO2 neutral community 
require that citizens and businesses understand and com-

skills for craftsmen in Sonderborg and other municipalities

of the Southern Denmark Region. It is funded by the 

mit to the SixBigConcepts. Consequently, ProjectZero has 

European Social Fund and the Southern Denmark Growth 

created partnerships with key educational institutions to 

Forum. 

ensure that community is offered early green learning from 

Kindergarten to PhD and  in-service training of craftsmen.

Combing education and real world training, students from 

Sonderborg’s House of Science is an education network 

Southern Denmark collaborate with local craftsmen and 

which facilitates a green curriculum from Kindergarten 

industrial companies to solve specific cases or undertake 

to PhD. The curriculum’s focus is on climate, innovation 

joint projects. These partnerships have made consider-

the Business Academy SydVest and the University of 

and sustainability. The City Council has approved the 

able contributions. 

green curriculum through which the House of Science has 

ensured that thousands of students are actively a part of 

On the finance side, the local banks and real estate com-

Sonderborg’s great transition.

Examples:

panies have been trained to understand how energy 

efficiency can strengthen personal finances of home-

owners and ensure a better selling price of real-estate in 

In the winter of 2012, more than 100 citizens have joined 

future.

the ZEROdriverslicense learning program. The citizens 

now know how to “drive” energy consumption in their 

New activities will include a training package for municipal 

homes and actively promote energy saving in local society 

schools. The package will inform children about biking 

- as Ambassadors.  

and green transport. It includes child-adult interaction. In 

this way, young people may act as “change agents” to 

Sonderborg-area’s craftsmen have significantly strength-

improve the families transport habits.

ened their energy consulting. 65% have participated in 

energy advisor training programs. The ZEROconstruction 

Sonderborg is in the process of becoming a UNESCO 

project, also lead by EUC Syd, facilitates energy instillation 

Learning City.

18

Foto Jacob Dall

A green mark on the global development

Countries, cities, companies and citizens are all looking 

•  Sonderborg inspires and assists Chinese county Haiyan  

for sustainable solutions that work. The solutions must be 

(440,000 citizens) to achieve its ambitions to become  

economically, environmentally and socially beneficial.

  a leading Chinese role model for green urban transition  

in China. Sonderborg also joined the EU-China Urba- 

Sonderborg’s ProjectZero public-private partnership has 

  nization Partnership Forum in Beijing 2013 and signed  

become a global role model for how society can turn local 

together with Haiyan as one of only 5 EU-China energy  

energy saving and carbon reduction ambitions into action, 

role models for city-cooperation’s.

based on specific plans and local ownership. 

In 2010, ProjectZero received the EU Sustainable Energy 

Award for “Sustainable Energy Communities”. Today 

ProjectZero participates in a variety of networks and 

cooperation nationally and internationally. Several of 

these are among the world’s leading climate and energy 

networks for cities and universities:

•  The Climate Positive Development Program working  

  with the Clinton Climate Initiative – in cooperation with  

  C40 Cities.

•  ProjectZero initiated in 2012 a municipal network of  

  strategic energy planning across municipal boarders in  

  collaboration with the Region of Southern Denmark.  

  The Network is scaling up ProjectZero’s best practice  

  solutions and in cooperation developing new solutions.

•  Sonderborg has in 2015 committed to the Compact 

  of Mayors, as the new global platform for City climate  

initiatives, commitment, planning and reporting.

Sonderborg attracts international attention due to two 

•  NORD-STAR - ProjectZero is the only non-university  

factors: Firstly, the founding partners of ProjectZero 

  partner in the joint “Nordic Centre of Excellence” within  

linked the climate agenda to business and job creation. 

  climate research.

•  Sonderborg has signed a Friendship City partnership  

  with the Chinese City of Baoding (11 million citizens),  

the partnership was originally initiated by WWF in 2008.

•  Sonderborg participates in the “Covenant of Mayors”  

  cooperation which includes both European and 

  Chinese cities. In 2012, Sonderborg was a co-signer  

  by the EU-cooperation agreement with China.

Secondly, the initiation of Bright Green Business activities, 

including demonstrations of energy efficiency and conver-

sion to renewable energy. Sonderborg is an awarded and 

recognized front-runner among the world’s climate and 

energy networks.

Sonderborg’s ProjectZero has demonstrated that it’s 

doable to address the climate challenge at local and city/

municipal level. And Sonderborg is committed to defend 

and preserve the position as global front-runner in order to 

secure local climate actions together with businesses. 

ProjectZero 
Price Winner
            2010 

18

19

 
 
 
 
 
 
 
The ProjectZero initiative

The ProjectZero Company was established in 2007 as 

a public-private partnership by SE, Danfoss Foundation, 

Municipality of Sonderborg, DONG Energy and Nordea 

Bank Foundation. Sønderborg Forsyning joined the part-

nership in 2014. University of Southern Denmark is also a 

boardmember.

The Board of Directors, in cooperation with local stake-

holders, develop plans and specific initiatives to achieve 

the ambitious community goals, monitor results and 

impact and initiate corrective actions, when required. 

The thinking, the lessons learned and the concepts are 

scalable to much bigger cities all over the world – and 

prove that combating the climate challenge is doable.

Bright Green Business

ProjectZero · Alsion 2 · DK-6400 Sønderborg · +45 65 50 80 98  · www.projectzero.dk 

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The  Land  Use-based  Integrated  Sustainability  Assessment  (LUISA) 
Platform 

Baranzelli C., Lavalle C. (JRC-IES-H8) 

Scenario-based  modelling  is  a  technique  employed  when  investigating  the  future  evolutions  of  a  given 
territory (a city, a region, a country or even the entire world). Models can be employed to simulate the direct 
and indirect impacts of a policy measure (e.g. an investment in an economic sector, the definition of zoning 
plans, the construction of a road or the installation of a technological infrastructure) or of wider trends, such 
as those related to climate or demography. Scenario modelling helps in combining effects of more matters 
simultaneously, and also in evaluating the impacts of potential or possible alternatives of evolution.  

Based upon the new concept of ‘Land Functions’, the Directorate General Joint Research Centre (DG JRC) 
of  the  European  Commission  (EC)  has  developed  the  Land-Use-based  Integrated  Sustainability 
Assessment (LUISA) Modelling Platform to contribute to the evaluation of impacts of policies and socio-
economic trends on European cities and regions.  

Land  functions  are  instrumental  to  better  understand  territorial  processes  and  to  better  inform  on  the 
impacts  of  policy  options.  A  land  function  can,  for  example,  be  physical  (e.g.  related  to  hydrology  or 
topography),  ecological  (e.g.  related  to  landscape  or  phenology),  social  (e.g.  related  to  housing  or 
recreation), economic (e.g. related to employment or production or to an infrastructural asset) or political 
(e.g.  consequence  of  policy  decisions).  Commonly,  one  portion  of  land  is  perceived  to  exercise  many 
functions. Land functions are temporally dynamic, depend on the characteristics of land parcels, and are 
constrained and driven by natural, socio-economic, and technological processes. 

In  order  to  cope  with  the  European-wide  coverage  and  multi-thematic  nature  of  the  explored  territorial 
processes, a wide knowledge base is needed. LUISA accommodates and integrate in a consistent way 
geographically referenced information from diverse sources, ensuring consistency of data nomenclature, 
quality and resolution, in order to allow cross-country/region/city comparison. Depending on the specific 
application, spatial and thematic resolutions can be adjusted in order to resolve local features and provide 
continental patterns. 

LUISA  simulates  land  functions  described  by  means  of  spatially  explicit  indicators.  The  indicators  are 
grouped according to 6 themes, projected in time until typically year 2030 or 2050, and can be represented 
at various levels (national, regional or other). 

 
 
 
 
 
 
 
 
 
A specific application of LUISA concerns the implementation of the "EU Energy, Transport and GHG 
emission trends until 2050 – Reference Scenario 2013"1. 

The  allocation  of  population,  activities  and  services  (at  high  spatial  resolution:  100x100  meter,  EU28 
coverage) allows to build profiles of energy consumption / use for different sectors (residential, industrial, 
transport,  etc.).  It  also  provides  information  on  the  availability  (including  costs  of  logistics)  of  resources 
relevant for energy production (e.g. biomass, solar suitability). Combined with the JRC-EU-TIMES model, 
this  approach  allows  to  compute  an  ‘energy  balance’  at  regional  (sub-national  scale).  In  principle,  this 
capacity can be used to generate ad-hoc scenarios of ‘optimisation’ of energy production/consumption, on 
the basis of the characteristic of each region. 

In particular for the residential sector, LUISA is equipped to downscale and project energy consumption 
trends, as function of buildings and households variables, climatic factors and urban characteristics (e.g. 
presence of green spaces). 

1 Baranzelli, C., Jacobs-Crisioni, C., Batista, F., Perpiña Castillo, C., Barbosa, A., Torres, J. A., Lavalle, C. ,2014. The Reference 
scenario in the LUISA platform – Updated configuration 2014 Towards a Common Baseline Scenario for EC Impact Assessment 
procedures. 

 
 
 
 
 
 
                                                            
Building Energy Consumption and Location; Big Data handling for Optimized 
Integration of Energy Systems in the Building Sector. 

Hans Bloem, JRC – IET-REE Unit 

Introduction 

Careful examination of energy consumption in the building sector, which is about 38% of the 
final  energy  consumption  in  EU-27  (Eurostat  data  2011)  is  needed  in  order  to  identify  the 
specific areas for energy savings. Due to improved insulation levels of buildings this saving 
potential moves to more dynamic energy use sectors such as gains from appliances, high energy 
demand  patterns  (such  as  from  heat  pump)  and  consumer  behaviour.  At  present,  the  EU 
residential  building  stock  consumes  about  2/3  of  the  energy  mainly  for  space  heating  and 
cooling, while the remaining 1/3 (electricity) is consumed by appliances and light. The recast 
of the  Energy  Performance for  Buildings  Directive  (EPBD)  (2010/31/EU)  requests for  high 
energy performance buildings that can be fulfilled when overall building energy performance 
is improved, renewable energy systems are installed and storage technology is applied. It is 
already  anticipated  that  the  current  electricity  grid  cannot  manage  this  unless  proper 
information and computation technology is put in place. The EPBD fixes the reduction of the 
space heating and cooling, ventilation, lighting and domestic hot water energy consumption of 
buildings.  Since  the  electrical  loads  are  characterized  for  being  highly  user  dependent  and 
stochastic  by  definition, this  will add  more complexity  to the analysis and  prediction  of the 
overall energy loads of the high energy performance buildings. 

Big Data. 

The role out of smart electricity and gas meters will create a huge amount of data that offers an 
opportunity to make the realisation of the optimised energy use possible. Although metering is 
essentially for billing purposes it may facilitate other purpose to consumers as well as producers 
and distributors of electricity, gas and other forms of fuels such as district heat. The EC in its 
COM (2014) 356 document has reported the targets for the role out of intelligent metering in 
the EU by 2020 in all its Member States. Apart from an economical benefit it states that a 3% 
energy saving potential is achievable. 

This discussion document focuses on the residential building sector, covering > 200 Million 
dwellings  (detached,  semi-detached,  multi-apartment  blocks,  etc.)  in  which  >500  Million 
people are living. The buildings as well as the inhabitants consume energy, 28% of the overall 
energy consumption in the EU. These numbers reflect a huge complexity dealing with different 
issues,  ranging  from  billing,  distribution  management  and  GHG emissions.  So  the  driver  to 
reduce GHG emissions is the reduction of energy consumption in the building sector, involving 
> 500 Million people. 

One may simplify the energy consumption to 2 sectors, e.g. the energy needed for achieving a 
comfortable building indoor environment in a given climate and the energy consumed by the 
people living and/or working in that specific environment. With the rolling out of advanced 
metering in the residential sector the importance of time series analysis becomes evident. 

 
 
JRC: Benchmarking smart metering deployment in the EU-27.  

An assessment of the energy performance of a building can be performed in different ways. In 
the schematic representation below is given a top-down approach, starting from administrative 
building data. A bottom-up approach starts from the calculation method as presented by the 
CEN  standards,  related  to  the  EPB  Directive  and  hence  minimum  requirements  set  by  the 
individual Member States 

Table 1. 
Top – Down approach (empirical – databases, metering, Big Data) 

Building administration, location, climate 

Energy performance and consumption related information 

Metering data (time series) energy performance, consumption assessment 

Building performance assessment by measurement 

Detailed calculation according to CEN standards 

Simplified calculation 

Bottom – Up approach (Calculation, individual building(s)) 

Modelling Approach 

The modern ICT sector can deliver tools that are vitally needed to collect, process and manage 
the  data  and  present  it  in  a  standardized  format.  The  approach  for  assessing  information 
contained  in  these  regular  and  more  frequent  readings  is  by  means  of  application  of 
sophisticated mathematical and statistical techniques. To assess from limited but at the same 
time  informative  observations  from  the  metering  equipment,  the  energy  performance  of  the 
building as well as the energy consumption by the occupants can be obtained. 

The model-choice as an important part of the method. A model is by definition a simplification 
of reality and a proper assessment is required in order to justify the applied model. 

In  relation to  this assessment the  time  aspect  should  be considered, e.g. the  impact  of  time 
related aspects of different energy related processes. This includes hourly, daily, monthly or 
seasonal and yearly approaches of the overall performance assessment. 

•  Hourly : light, ventilation, occupant behaviour 
•  Daily: solar gains, weather dependence, window-opening, gains, ventilation 
•  Monthly: seasonal: solar gains, wind, temperature, functionality, system performance 

issues 

•  Yearly: solar gains, temperature. 

The consequences for the assessment are evident  in  particular  when the uncertainty  of  the 
assessed performance value is taken into account. Energy labelling is in most cases performed 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
by auditors who examine drawings, climate and other building administration, that may include 
age  of  the  building,  local  requirements  for  insulation  and  ventilation,  which  may  result  in 
different label resulting values for the same object.  

Figure 1. Relation between energy performance and energy consumption 

Information on the energy performance can be made available to the building owner and energy 
providers or building construction companies. One way of doing so is by an energy-label as 
given  in  figure  1.  The  uncertainty  is  about  15  -20%,  derived  from  the  scaling  A  to  G.  The 
method behind the energy label is much simplified and not based on detailed calculation nor 
on measurements and should be considered as an indicator only. The indicator is mostly linked 
to the information from the cadastre, such as age, construction requirements, type of building 
and climate, e.g. location of the object. 

The Energy Performance for Buildings Directive 

The  Energy  Performance for Buildings  Directive  demands for the assessment  of  the  energy 
performance in two ways: by calculation or by measurement. Calculation methods are provided 
through national standardisation for which CEN has been working hard to define a harmonised 
structure  of  sub-standards.  It  demands  for  monthly  or  hourly  calculations  using  reference 
climate data. The well-known problem of the calculation assessment is the gap between the 
design energy performance value for a building and the real one. 

The measurement method offers a different approach to the energy performance assessment 
which can be considered as more realistic although the method has the drawback of the variable 
climatic  conditions  as  well  as  the  uncertainty  of  the  separate  elements  to  identify  areas  for 
renovation. On-site measurements are therefore recommended for building elements (wall, roof 
structures etc.) and for whole buildings (including infiltration assessment) in order to derive a 
realistic energy performance value. 

Regular  measurements  of  energy  consumption  by  means  of  metering  may  offer  detailed 
information of electricity, gas and even water usage. Time series analysis of these data will 

 
 
give an indication of the energy required by the building due to the climate but also the energy 
needed for using the building, e.g. living or working. 

Proposed Method 

A combination of statistical and dynamic evaluation methods, are techniques that must be used 
to analyse time series of data related to dynamic processes and to identify occupant behaviour 
and typical parameters of the physical processes for evaluation. Data from smart meters are 
typical examples of such time series and provides details of energy usage patterns. By using 
dynamic evaluation techniques (system identification) dynamic effects due to accumulation of 
energy (heat and electricity) in the building interior construction, envelope and equipment are 
properly taken into account, which combined with the occupant behaviour, provides the overall 
vision necessary to model the residential thermal and power demand. 

The aim of the herewith presented method is to derive from available metering data and climate 
data the amount of energy that is needed by the building to fulfil the minimum requirements 
for  the  type  of  building,  e.g.  for  residential  dwellings  a  design  indoor  temperature  of  for 
example 20°C. This energy need would correspond with the performance of the construction 
in  a  typical  climate  while  the  energy  consumed  by  the  occupants,  their  appliances  and 
behaviour (like different indoor temperature setting) would result in the remaining part of the 
energy readings. See figure 2 for further explanation.  

The proposed method therefore has to be able to distinguish between the building energy needs 
and the occupants' energy consumption. 

Metering 

Empirical 
assessment 

Calculation 

or 

Measurement 

Figure 2. Relation between energy performance and energy consumption 

Building energy performance and consumption assessment. 

According to the EPBD the performance of the building deals with energy needed for heating, 
cooling, ventilation, hot water and light (for non-residential buildings), referred to as EPB use. 
These  building  energy  needs  are  strongly  related  to  the  climate,  in  particular  the  ambient 

 
 
 
 
 
 
 
 
temperature, the solar radiation and the impact of wind. In general, the major part of energy 
consumption is required for the building energy needs. 

The trick is to correlate EPB use of energy to climate and fabric parameters and to correlate 
non-EPB use to the user of the building. The latter includes energy consumed by appliances 
and the pattern of usage by the occupants. The variation depends a lot on how many appliances 
and  people  are  living  or  working  in  the  building  comprising  when  energy  is  used.  High 
frequency  readings  of  electricity  consumption  facilitate  the  assessment  of  this  information. 
Readings  of  water,  heat  and  gas  or  other  carriers  would  make  the  assessment  even  more 
informative. 

Gathered from building administration is knowledge about the exact location and the nearest 
weather station for reliable climate data. Which fuel is used and what data is available, like 
interval, unit but also for which purpose (like gas for heating, cooking, hot water).  

In order to deal with many buildings (millions) and huge amount of data (hourly, daily, etc.)  it 
is feasible to develop a method that analysis  the  continuous flow of data from the object and 
climate. The method can be incorporated in a local device that communicates with the utilities 
as well as the building owner. 

Discussion 
Boundaries  for  the  assessment;  Member  State  level,  building  level,  in-between  level  (City, 

urban area) 

Top- down versus Bottom-up approach;  

•  where are the limits, e.g. boundaries for assessment? 
•  who benefits? (energy end-consumer, the energy producer/ distributor) 
•  what are the requirements for data input? 
•  what are the requirements for output results? 
•  uncertainty and confidentiality 

References. 

J.  Bloem,  et  al.  Dynamic  Methodology  for  the  Evaluation  of  Occupant  Behaviour  and 
Residential Energy Consumption. EEDAL 2015, Lucerne, CH. 

H. A., Nielsen, et al.  Analysis of energy consumption in single family houses. In: DYNASTEE, 
11-12 October 2010, Brussels. 

COM (2014) 356 document. Benchmarking smart metering deployment in the EU-27 with a 
focus on electricity 

COM 2010/31/EU Energy Performance for Buildings Directive (EPBD). 

…. 

 
 
 
 
 
 
“Spatial data for modelling building stock energy needs” : 

Enhancing Sustainable Energy Action Plans at local level  

Albana Kona, JRC –IET-REE Unit 

1. 

Introduction 

The paper addresses the need for tools and data enabling the redaction of Sustainable Energy 
Action Plans at local level within the frame of Covenant of Mayors Initiative. Under the EU 
Covenant  of  Mayor  Initiative,  more  than  6500  cities  and  municipalities  have  signed  (as  of 
September  2015),  who  commit  to  reductions  in  CO2  emissions  in  their  municipalities  of  at 
least 20% by 2020. The majority of CoM signatories (88%) are small medium towns with less 
than 50,000 inhabitants and they often encounter many obstacles in getting data about energy 
consumption  at  the  right  level  of  detail.  The  importance  of  small  and  medium-sized  cities 
should  not  be  underestimated.  The  generic  features  of  small  and  medium-sized  cities  – 
particularly their human scale, liveability, the conviviality of their neighbourhoods, and their 
geographical  embeddedness  and  historical  character  –  in  many  ways  constitute  an  ideal  of 
sustainable  urbanism1.  Europe  is  characterised  by  a  more  polycentric  and  less  concentrated 
urban  structure  compared  to,  for  instance,  the  USA  or  China2.  56  %  of  the  European  urban 
population – around 38 % of the total European population – live in small and medium-sized 
cities  and  towns  of  between  5  000  and  100,000  inhabitants.  Furthermore,  in  Europe,  CO2 
emission per person is much lower in urban areas compared to non-urban areas3. The density 
of  urban  areas  allows  for  more  energy-efficient  forms  of  housing,  transport  and  service 
provision. Consequently, measures to address climate change may be more efficient and cost 
effective in big and compact cities than in less densely built space.  

Local authorities prepare sustainable energy plans often in the frame of national strategies and 
aiming for ambitious energy efficiency and decarbonisation goals. These plans often consider 
the heating system in an integrated way, looking at the evolution of the whole energy supply 
and demand in a municipality to define the least cost approaches.  

Therefore,  reliable  data, at  national  and  at  local  level, are  necessary  in  all  the  phases  of the 
policy  life  cycle,  from  inventories,  planning  to  implementation  and  monitoring.  Valuable 
support to satisfying these data needs can be provided by the INSPIRE Directive (2007/2/EC) 
: INSPIRE is the reference directive for spatial data, establishing an infrastructure for spatial 
information  in  Europe  to  support  Community  environmental  policies,  and  policies  or 
activities  which  may  have  an  impact  on  the  environment,  such  as  energy  policies.  The 
INSPIRE  Directive  is  well-timed  as  it  is  due  to  be  operational  by  2020,  when  MS  have  to 
report about data which are of interest for CoM.  

1 Farr, D., Sustainable Urbanism : Urban Design with Nature, John Wiley & Sons, New Jersey, 2008 
2 source DG Regio: Only 7 % of the EU population live in cities of over 5 million inhabitants compared to 25 % in the 
USA 
3 A rural resident would consume an average of 4.9 tonnes of oil equivalent/year in Europe while a city resident would 
consume  3.5  tonnes  of  oil  equivalent.  Source  :  IEA,  2008  and  World  Energy  Outlook,  2008,  International  Energy 
Agency, Geneva 

1 

 
 
                                                             
Based on this background, the "Location data for energy efficiency policies" feasibility study 
aims  to  verify  the  potential  for  an  effective  application  of  spatial  data  to  support  the 
monitoring requirements of the different EU energy efficiency policies and initiatives, which 
include data from different sources and at different scales (building, district and national). 

Figure 1. The different scales in the application of energy efficiency policies 

2. 

Data requirement for CoM  

In  the  Covenant  of  Mayors  special  focus  is  placed  on  the  data  collection  required  for  the 
Sustainable  Energy  Action  Plans  (SEAPs)  prepared  by  CoM  Signatories,  "where  local  and 
detailed  data  about  energy  needs  and  consumptions  are  needed  at  different  geographical 
scales".  

In the SEAPs the data collection process and the data sources should be well documented and 
publicly available, so that the process is made transparent and stakeholders can be confident 
with the inventory of emissions.  

Data should be relevant to the particular situation of the local authority, i.e. based on energy 
consumption  /  production  data,  mobility  data  etc.  within  the  territory  of  the  local  authority, 
and  estimates  based  on  national or regional  averages  are  not considered appropriate  as  they 
would not capture the efforts made by the local authority to reach its CO2 targets4.  

In all of this, the collection of reliable data with an acceptable level of accuracy is one of the 
most difficult tasks in designing, implementing and monitoring a SEAP.  

The SEAP template allows signatories to report data concerning their action plan, namely the 
adopted target, the Baseline Emission Inventory and the actions planned to achieve the target. 
The  Monitoring  template  focuses  on  tracking  progress  in  SEAP  implementation.  The 
templates are composed of three main parts as described in the following figure. 

4 SEAP guidelines (EU, 2010b, p.9)  

2 

 
 
 
                                                             
Figure 2. Sustainable Energy Action Plan process in the framework  
of Covenant of Mayors 

3. 

Modelling approach 

To achieve the 2020 climate objectives, modelling forecasts illustrate the need for the EU 
to move away from fossil fuels to low carbon sources and deeply transform its energy system. 

There  are  tools  that  could  be  useful  at  local/regional  levels  to  explore  the  different 
pathways  and  identify  of  the  options  for  action  which  are  necessary  to  drive  the  changes 
needed  to  achieve  the  sustainability  goals.  In  buildings  there  is  scope  for  speeding  up  the 
renovation  rate.  While  heat  demand  is  reduced,  the  focus  should  also  be  on  the  supply  of 
heating  or  cooling  to  ensure  a  gradual  transition  to  renewable  energy  fuels  and  the  increase 
fuel,  carbon  and  conversion  efficiency  for  the  remaining  part  of  fossil  fuels  (eg.  SEAP  of 
Sønderborg). 

Figure 3. SEAP of Sønderborg : SmartEnergy stabilizes the balance between supply  
and demand of energy 

3 

 
 
 
 
 
 
In  the  following  some  examples  of  methodological  approaches  to  allow  for  analysis  of 
scenarios with a focus on the heating sector are reported for enabling the SEAP redaction of 
CoM signatories. 

An hourly heat modelling approach combined 
with  the  mapping  of  heat  densities  and 
renewable  and  waste  heat  resources  at 
European  level  has  been  developed  by  a 
consortium led by Alborg University.  

The  European  Heat  Atlas  has  been  used as a 
tool  for  modelling  energy  demand  in  some 
cities CoM signatories in the above countries 
(eg. London). The results have quantified the 
share  and 
the  energy,  environment  and 
investment,  system  cost  impacts  of  installing 
district  heating  and  individual  heating  and 

Figure 4. European Heat Atlas Demand density 

cooling  appliances  in  the  EU  energy  system. 
Since  then,  several  other  studies  have  been 
developed,  focusing  on  national  or  local  energy  systems.  The  IEE  Stratego  project5  applied 
holistic  energy  system  analysis  to  perform  comprehensive  assessments  for  heating  and 
cooling for five Member States: the Czech Republic, Croatia, Italy, Romania, and the United 
Kingdom..  To  begin  the  mapping  of  heat  demands  is  done  in  a  top-down  manner,  where 
national level energy statistics allows for the calculation of Member State average per-capita 
heat  demands,  which  are  subsequently  associated  to  total  population  counts  within  each 
NUTS3  region in  each respective  country.  Afterwards,  the  general  climate  of  each  Member 
State  is  represented  by  use  of  the  European  Heating  Index  (EHI),  a  concept  presented  by 
Werner  (2005)6,  to  map  sub-national  deviations  from  national  and  European  heat  demand 
averages. Using the EHI adjusted heat demands per NUTS3 region, the European population 
grid (by GISCO 2012) was converted to highly detailed heat atlas for Europe7. 

Adequate  planning  is  required  as  only  in  this  manner  the  efficiencies  and  the  use  of 
renewables can be maximised. Examples of this could include, for instance, those situations in 
which  a  city  planning  authority  grants  approval  for  new  development  on  the  condition  of 
connecting  to an existing  heating  network.  Also,  some  cities  have  developed  guidelines and 
(GIS)  systems  to  decide  the  depth  and  the  situation  of  the  wells  that  are  required  to  supply 
ground source heat pumps.  

Heat planning by municipalities in Denmark has recently been complemented by national 
level  assessment  for  heating  and  cooling  and  the  relevant  energy  sectors.  For  example: 

5 http://stratego-project.eu/ 
6  Werner,  S.,  2005.  Ecoheatcool:  The  European  heat  market  (WP1).  Euroheat  &  Power,  Brussels.  Available 
from: 〈http://euroheat.org/ecoheatcool〉 
7  Heat  Roadmap  Europe:  Combining  district  heating  with  heat  savings  to  decarbonise  the  EU  energy  system. 
Energy Policy 65(2014) 475–489. D. Connolly et.al. 

4 

 
                                                             
Energi2020  is  Ringkøbing-Skjern  Municipality’s8  vision  to  become  self-sufficient  in 
renewable  energy  by  year  2020.  This 
means 
that  we  produce  as  much 
renewable  energy  as  the  citizens  and 
businesses  in  the  municipality  consumes. 
For  that  purpose,  GIS-based  energy  atlas 
has  been  developed  for  Ringkøbing-
Skjern  municipality.  The  data  about 
energy  supply  and  demand,  transmission 
and  distribution 
infrastructure,  energy 
resources,  societal  and  other  energy  data 
have  been  geographically  referenced  and 
combined with the tools built in ArcGIS.  

Figure  6:  Ringkobing–Skjern  Energy  Atlas  and 
Heating Model 

in 

labelling  Atlas 

The  Energy 
the 
Netherlands9 provides an identification of 
the  energy  performance  of  individual 
buildings.  The  Atlas  Energy  uses  three 
types  of  energy  labels,  which  differ  in 
completeness  of  housing  data:  1  star: 
Energy  based  on  a  number  of  standard 
housing 
from  public 
sources  such  as  the  year  of  construction, 
floor area and number of floors;  2 stars : 
Energy  adjusted  using  the  button  'Check 
my  label’;  3  stars:  Energy  label  by 
certified  consultant.  In  the  modelling  of 
default  values,  assumption  has  been 
made, such as the one on the glazing area (glazing this covers 25% of the façade.). Based on 
this  atlas  city  of  Amsterdam,  is  enhancing  the  quality  of  the  SEAP  by  upgrading  it  into  a 
Energy Atlas Plus including the mapping of renewables. 

Figure 5: Energy Labelling Atlas in the Netherlands 

characteristics 

4. 

Discussion 

The  mapping  of  CoM  signatories,  along  with  energy  and  emissions  indicators,  will  be 
developed in the pilot study. This will allow local authorities to rely on high quality and local 
data to better inform the planning of energy efficiency measures. 

8  Ringkøbing-Skjern  energy  atlas  for  municipal  energy  planning:  SDEWES  2015.  Stefan  Petrović,  Kenneth 
Karlsson 
9 http://www.energielabelatlas.nl/# 

5 

 
 
                                                             
The aims of the EULF Pilot Project on "Location data for energy efficiency policies" include 
the following: 

a)  To  support  local  authorities  (e.g.  Covenant  of  Mayors  signatories)  to  enhance  the 
energy  efficiency  policy-making  process  (from  planning  to  implementation  and 
monitoring),  improving  the  quality  of  data  they  use  (particularly  time  series  local 
data). 

b)  To  develop  a  methodology  and  to  provide  local  authorities  with  tools  to  build  and 
update a robust inventory of reliable local energy performance of buildings data (not 
only based on statistical indicators), to generate energy density maps, a dashboard of 
indicators and changes over time of energy performance. 

c)  To provide local authorities with tools to share data of better quality related to energy 
consumption, which can be used by citizens and businesses, including energy saving 
companies. 

d)  To  put  in  place  harmonised  data  flows  to  support  energy  related  initiatives  at  local 
level  (e.g.  the  CoM)  and  to  align  them  with  the  national  energy  efficiency  planning 
obligations  (set  by  the  Energy  Efficiency  Directive  or  the  Renewable  Energy 
Directive). 

6 

 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

The Use of Spatial Data for Energy and Exergy Mapping of University 
Campuses with Implications for a Net-Zero District in Sweden 

Dr. Şiir KILKIŞ 

1. Introduction: Energy Efficiency Directives and Initiatives 

The use of location specific data presents multiple benefits for modeling the energy needs of 
the  building  stock.  This  contribution  for  the  “JRC  EULF  Workshop  on  Spatial  Data  for 
Modelling  Building  Stock  Energy  Needs”  focuses  on  future-oriented  perspectives  for 
combining location specific energy, exergy (the quality of energy), and CO2 emissions data. 
The pilot case studies are based on two university campuses in cities that are signatories to 
the  Covenant  of  Mayors  (CoM)  [1].  These  cities  are  namely  Delft  in  the  Netherlands  and 
Stockholm  in  Sweden,  the  latter  of  which  has  advanced  to  the  monitoring  stage  of  the 
Sustainable Energy Action Plan (SEAP) [2]. The diffusion of using the method of collecting 
location  specific  data  from  university  campuses  to  the  urban  level  has  the  potential  to 
represent  “Case  5”  of  the  envisioned  cases  for  the  European  Union  Location  Framework 
(EULF) Pilot Project [3]. Both cities can have the opportunity to couple demand and supply 
side  data,  including  energy  production,  to  monitor  the  progress  that  is  being  made  towards 
sustainable energy landscapes. The cases demonstrate the use of spatial data for the mapping 
of different scenarios for building clusters in university campuses, which represent the way in 
which  geo-location  data  can  be  used  to  support  action  in  the  policy  domain  of  energy.  

The  contribution  concludes  with  further  implications  of  the  use  of  spatial  data  for  net-zero 
districts  in  Sweden  that  are  to  use  fourth  generation  district  heating  networks  [4].  The 
methods that are involved can support common methods to use energy based spatial data for 
the betterment of urban areas in the context of the Infrastructure for Spatial Information in the 
European Community (INSPIRE) Directive [5], including various energy density maps.  

2. Proposed Mapping of Location and Energy, Exergy and CO2 Emissions Data 

Figure 1 represents the main data point inputs that are needed to realize mappings of energy, 
exergy and CO2 emissions data at the building level with a spatial dimension. In Figure 1, Ex.y 
represents an energy data input for a particular building with spatial coordinates x and y while 
similarly, ɛx.y and Cx.y represent the exergy and CO2 emission data inputs for the same set of 
buildings. The multi-dimensional mapping of building level data with a spatial dimension for 
energy,  exergy  and  CO2  emissions  can  enable  a  thorough  process  of  planning  at  multiple 
stages, namely stages of energy planning, exergy planning, and CO2 emissions planning. 

1 

 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 1. Multi-dimensional mapping of building level energy, exergy and CO2 emissions data 

Such  an  integrated  approach  provides  benefits  to  consider  the  energy  performance  of 
buildings.  At  the  same  time,  the  level  of  match  between  the  demand  and  supply  of  energy 
resources  to  satisfy  building  services  based  on  the  quality  of  energy  (exergy)  enables  an 
additional  stage  of  planning  to  increase  the  exergy  performance  of  buildings.  These  may 
provide  further  opportunities  to  consider  optimized  community  energy  supply  structures 
beyond  the  building  level,  such  as  district  heating  and  cooling  (DHC)  networks  based  on 
renewable  energy  driven  combined  heat  and  power  (CHP)  options.  According  to  Heat 
Roadmap Europe [6], DH/C networks are expected to more than double by the year 2030. 

In addition, at least one of these inputs may be used to determine the CO2 emission impacts 
of  buildings.  For  example,  the  Rational  Exergy  Management  Model  [7]  provides  the 
avoidable  CO2  emissions  impact  in  the  energy  system  based  on  any  mismatch  between  the 
supply and demand of exergy. Two case studies that have integrated an exergy aspect in the 
multi-dimensional  mapping  of  building  level  data  are  provided  in  this  contribution.  As 
indicated in Figure 2, these two case studies focus on the scales of a university campus and 
building clusters within university campuses and the urban vicinity. The results of these pilot 
cases are further evaluated based on the implications for advancing spatial planning in a net-
zero exergy  district  in  Sweden,  i.e.  the  Östra  Sala  backe  project  as  analyzed  in  Refs.  [8-9].  

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 2. Scope of the Contribution and Implications for Net-Zero Exergy District 

2.1. Current State-of-the-Art Example 1: Energy Label Atlas (Building Level) 

The  Energy  Label  Atlas  of  the  Netherlands  [10]  provides  an  identification  of  the  energy 
performance  of  individual  buildings.  Such  an  application  is  one  of  the  best  practices  in 
combining energy and location data as given in the JRC Technical Report on “Location data 
for buildings related energy efficiency policies” [11]. Figure 3 provides a screenshot of this 
atlas for the city of Delft in the south of the country. With some exceptions, the buildings that 
do not have a label in this atlas (see grey areas) represent the campus buildings of the Delft 
University  of  Technology  (TU  Delft).  In  the  first  case  study  of  this  contribution,  all  of  the 
campus  buildings  were  subjugated  to  the  proposed  energy,  exergy,  and  CO2  emissions 
mapping  technique.  The  results  were  used  for  planning  in  multiple  aspects  for  scenarios.    

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 3. Screenshot of the Energie Label Atlas [10] for an area representing TU Delft (dotted line)        

2.2. Current State-of-the-Art Example 2: BPIE Data Hub (Country Level) 

Another  relevant  example  for  energy  and  location  data  as  provided  in  the  JRC  Technical 
Report [11] is the BPIE Data Hub [12]. In contrast to the building level focus of the Energie 
Label Atlas [10], the focus of this database is on providing aggregated data for the building 
stock  of  European  countries.  Figure  4  provides  a  screenshot  of  an  exemplary  dataset  for 
Sweden  that  indicates  the  average  heating  related  energy  consumption  of  multi  and  single 
family  houses  by  age  groups.  Accordingly,  there  are  significant  energy  savings  in  younger 
buildings.  Beyond  this  location  based  energy  data  for  the  building  stock  of  Sweden,  the 
second  case  study  of  this  contribution  will  provide  an  original  analysis  for  the  campus 
buildings  of  the  KTH  Royal  Institute  of  Technology  and  the  university  related  district  of 
Albano.  The  dataset  for  this  case  study  involved  Akademiska  Hus,  which  owns  university 
buildings in Sweden and is in the process of monitoring energy data as provided in Figure 5.         

Figure 4. Screenshot of the BPIE Data Hub [12] for Sweden as the Country of Case Study 2 

4 

 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 5. Screenshot of the Akademiska Hus Energy Portal for University Campus Buildings [13] 

3. Pilot Case Studies with Potential for Scale-Up as EULF Partner Cities 

Beyond  examples  from  the  state-of-the-art  as  given  above  for  buildings  and  the  building 
stock, as well as other examples for the energy density maps of districts, including those in 
Italy [11], the pilot case studies of this contribution represent the use of building level spatial 
data for the planning of more sustainable energy landscapes. In this way, these case studies 
indicate  the  use  of  location  specific  data  for  scenario  analysis  to  assist  decision-makers  in 
selecting  desired  policy  outcomes.  The  two  case  studies  are  overviewed  below  in  detail. 
Table  1  compares  some  of  the  domains  of  consideration  within  the  foreseen  aims  of  the 
EULF pilot project and expectations from potential partner cities with the pilot case studies. 

Table 1. Comparison of EULF Domains of Consideration and Pilot Case Studies 

EULF Domains 
Desired output to be produced 
Functionalities of the demonstrator 
Assessment methodologies to be applied  
Data flows to be managed by the demonstrator  Building/building cluster/building stock energy data 
Energy monitoring portals/energy labels as available 
Data sources to be used into the data flows 
Application of Carnot and CO2 emission factors 
Data harmonization steps to be carried out 

Pilot Case Studies 
Energy, exergy and CO2 emissions mapping 
Survey of building and energy system data 
Data analysis and scenarios for sustainable energy 

The  proposed  method  has  the  potential  to  be  in  line  with  the  Boolean  attributes  in  the 
INSPIRE  data  model  for  buildings.  For  example,  the  attribute  “energyPerformance”  can  be 
used to store information related to the energy label [11]. The attributes of  “heatingSource” 
and “heatingSystem” can be used to store information related to the source of energy used for 
heating (i.e. electricity, natural gas, etc.) and the system of heating (i.e. stove, central heating, 
heat  pump,  etc.)  [11].  Such  attributes  can  be  used  to  find  exergy  and  CO2  emissions  data 
when the different options are combined with the relevant Carnot factors and CO2 intensities. 
Standard  values  for  these  options  can  be  defined  before  the  operational  phrase  of  the 
INSPIRE Directive and related reporting by the EU member states by the year 2020.   

5 

 
 
 
 
 
 
 
 
 
 
 
      
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

3.1. Case Study: Energy and Exergy Mapping of University Campus Buildings at TU Delft 

As the site of the first case study, data was collected for the TU Delft campus. The university is 
committed  to  reducing  the  consumption  of  fossil  fuels  per  m2  of  gross  floor  area  by  30% 
compared  to  2005  levels  until  the  year  2020  [14].  This  target  is  part  of  the  long-term 
agreement (MJA3) between the Dutch government and Dutch universities to improve energy 
efficiency by 2% per year until 2020. The university further aims to have at least a 50% share 
of renewable energy in the future, including the usage of geothermal energy [14]. Based on 
the system boundary of the campus, data was collected at two complementary levels.  

Building  level  data.  At  the  building  level,  data  was  collected  based  on  the  TU  Delft  Energy 
Monitor that provides the primary energy consumption, electricity usage, natural gas usage, heat 
usage,  and  CO2  emissions  of  each  faculty  and  building  [15].  The  function  and  operational 
activity of the buildings are among the factors that affect energy use profiles. The portal is part 
of a greater effort to make the energy usage of the campus public and transparent while inviting 
the campus  to  make suggestions for improvement  [15]. Annual reports for the most recent 5 
years for 28 buildings on the university campus were extracted from the portal. The portal also 
provides the total number of 40W light bulbs at the building level, which is already represented 
within the electricity use profile, as well as the total amount of water usage at the campus level. 
The building level energy data represents the end-use or the sink for energy usage on campus. 

Energy system data. On the source side of the energy system, the means of energy conversion 
and  energy  production  are  other  essential  components  for  data  collection.  In  addition  to  the 
initial  heat  plant  that  continues  to  produce  most  of  the  heat  for  the  campus,  the  campus  is 
equipped  with  two  new  CHP  units  that  produce  both  electrical  and  thermal  energy.  These 
CHP  units,  which  were  installed  in  2011,  each  have  an  electrical  capacity  of  3.7  MWe  and 
thermal capacity of 3.7 MWt [16]. The two CHP units and the boilers feed the heat distribution 
network that has 4 transmission pipelines [17]. The supply temperature of the hot water in the 
heat distribution network that reaches the buildings varies between 80°C and 130°C based on the 
outdoor air temperature [17]. An annual average was taken to be about 105°C (378.15 K). 

Communication  with  university  officials  confirmed  that  some  buildings  satisfy  their  entire 
thermal load based on the use of heat from the heat distribution network while other buildings 
directly combust  natural  gas,  mostly for heat-only boilers, hot water supply, and/or  laboratory 
use [18]. Three buildings are also equipped with heat pumps based on Aquifer Thermal Energy 
Storage (ATES). Based on the average of energy data for the most recent 5 years, i.e. between 
2010 and 2014, the average shares of the energy use profile of buildings, including laboratory 
facilities,  were  found  to  be  about  47%  electricity,  34%  heat  from  the  campus  network  and/or 
ATES (relatively minor share), and 19% natural gas for individual building level use and boilers. 

Figure 6 depicts a schematic of the energy system of the campus that provides the larger context 
of the analysis for the building level data. The components of the schematic are the purchased 
electricity and purchased natural gas that are used directly or converted into delivered electricity, 
delivered  heat,  and  delivered  natural  gas  (see  components  of  Figure  6).  Conversion  processes 
depend on CHP units at the campus level and heat boilers, either at the campus level that feed 
the heat network or the individual building level. The energy values in the system schematic are 
for illustrative purposes only for a representative year in the TU Delft Energy Monitor [16].  

6 

 
 
       
    
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 6. Schematic of the Energy System of the Campus based on Ref. [16] (Present Case) 

In  the  future,  a  substantial  part  of  campus  buildings  is  planned  to  be  supplied  by  a  lower 
temperature heat distribution network based on geothermal energy. In this project, namely the 
Delft Geothermal Project (DAP), the supply temperature is aimed to be 75°C (348.15 K) [14]. 
On  the demand  side, campus  buildings  are  expected  to  be  completely  renovated  in  the  next 
15-20 years with a focus on the use of sustainable energy [15]. Such a conjecture amplifies the 
importance of energy landscape mapping as conducted in this research work for the campus.    

Data processing 
The collected data at both the building and campus energy system level were processed to bring 
them in conformity with the aims of the research work. Figure 7 provides the compilation of the 
building  level  data  based  on  average  energy  values  for  the  amount  of  electricity,  heat,  and 
natural gas that was delivered to each building between the years 2010 and 2014. In this respect, 
Equation 1 was used to find the average values during the most recent 5 years. Here, EL is the 
delivered electricity, EH is the delivered heat, and EN is the delivered natural gas for a particular 
year, y. ET bar represents the average amount of energy delivered in total to each building. The 
components of total delivered energy as the stacked bars represent each term in Equation 1.  

Figure 7. Data Processing of Energy Data for TU Delft Campus Buildings 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

E
T



5



y

1


E

L

5/



5



y

1


E

H

5/



5



y

1


E

N

5/

(1) 

At the same time, both Equation 1 and Figure 7 represent a summation of energy amounts with 
different  useful  work  potentials  or  qualities.  For  example,  the  Biotechnology  and  Chemical 
Engineering buildings appear to use similar amounts of total energy at about 330 kWh per m2 
per year. A similar situation applies to the Sports Division and the Aerodynamics Laboratory. 
These buildings are not using energy at the same quality levels, e.g. electricity versus heat.  

Electricity can be used for a more diverse spectrum of energy services from lighting to powering 
electronic laboratory equipment. Natural gas also can be converted into electricity to provide for 
the same energy services. On the other hand, thermal energy, either based on the by-product of 
electricity production or other sources, has a limited range of energy services in which it may be 
used. The degree of diversity in satisfying a broader range of energy services is due to the useful 
work potential of energy resources, i.e. exergy. For this reason, further processing of the energy 
data was needed to differentiate these aspects and bring an exergy view into the research work.           

Exergy calculations. Exergy is the part of an energy flow that can be transformed into any other 
form of useful energy and depicts the potential of a given energy quantity to perform work [19]. 
In this way, it is a measure of the useful work potential of energy and can indicate the quality of 
energy carriers on the supply side and the desired quality of energy loads on the demand side.  

In Equation 2a, the average values of each term in Equation 1 as given by EL, EH, and EN are 
multiplied  by  the  respective  Carnot  factors  (CF)  to  obtain  the  total  exergy,  εT.  The  CF  for 
electricity is accepted to be 1  since  all of  electrical  energy can  theoretically be  converted  into 
mechanical  energy  [19].  For  thermal  energy,  the  Carnot  factor  is  based  on  the  temperature 
differential that is being made with a reference environment as represented in Equation 2b. This 
factor  results  in  the  asymmetry  between  energy  and  exergy  values  [20].  Tref  is  the  reference 
environment temperature and Tf is the temperature of the resource being used, e.g. combustion 
temperature for natural gas or the supply temperature of waste heat after electricity production.  

Since all of EH is not based on the supply of thermal energy from the CHP, the appropriate share 
that must be multiplied by the respective CF is defined based on a share, S (see Equation 2a). As 
a result, a certain share of EH bar is multiplied by CFH and the remaining share is multiplied by 
CFN. Based on Figure 6, the share of heat in the campus heat network based on the CHP units 
was  calculated  to  be  25%.  The  remaining  75%  of  the  heat  is  produced  based  on  heat-only 
boilers. Table 2 provides the values that are used or obtained in Equation 2b for these instances.         


T




CF

EL



E

L







S




CF
H



E

H



1(


S

)




CF
N



E

H







CF
N



E

N

where      

CF



1








T

ref
T

f






. 

(2a)

(2b) 

In  Table  2,  the  reference  environment  is  defined  based  on  the  ground  temperature  that  is  the 
ultimate sink for the seasonal swings in outdoor air temperatures [21]. The ground temperature 
is found to be in thermodynamic equilibrium at a depth of 9.1 m for a given location [22]. For 

8 

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

the Amsterdam – Delft area, the value of the ground temperature is given to be about 285.5 K 
[23]. The units of all temperature values in Equation 2b must be expressed in units of kelvin (K).   

Table 2. Temperature Value Inputs into Exergy Calculations for Campus Energy Resources 

Kinds of Thermal Energy EH 

  Heat based on heat-only boilers 
  Heat based on waste heat in CHP  
  Heat based on ATES systems 
  Heat based on geothermal wells 

Tref (K) 
285.5 
285.5 
285.5 
285.5 

Tf (K) 
2000 
378.15 
318.15 
348.15 

CF 
0.86 
0.25 
0.10 
0.18 

In addition, three of the campus buildings, namely the 3Me Faculty (building 34), the TU Delft 
Library, and the EEMCS1 Faculty (building 36) use heat pumps based on ATES. These systems 
provide an advantage to upgrade low quality energy at about 13 – 16 °C (286.15 – 289.15 K) to 
heat and/or cool buildings [24]. As a result, lower temperature heating is supplied on-site to the 
building at about 45°C that reduces the intake of heat from the heat distribution network. At the 
TU Delft Library, ATES is sufficient to cover 20% of the required heat in the winter [17]. In 
the  future,  a  geothermal  well  at  a  depth  of  2.3  km  is  also  intended  to  supply  heat  at  75  °C 
(348.15 K) [14]. While not yet realized, this value is also included in Table 2 for comparison.     

The application of Equation 2 to the energy values in Figure 7 will provide a more accurate sum 
that  takes  into  account  the  differing  quality  levels  of  the  various  components  of  the  total 
delivered energy.  Figure 8 provides the application of Equation 2 to the energy data for the 28 
campus  buildings.  These  values  represent  the  total  exergy  that  is  used  by  a  building,  εT. 
Buildings that had similar total energy values at different exergy levels are now differentiated.  

Figure 8. Data Processing of Exergy Data for TU Delft Campus Buildings 

1 The Electrical Engineering, Mathematics and Computer Sciences Faculty 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

CO2 emission calculations. The TU Delft Energy Monitor provides the CO2 emissions of each 
building on the campus. These values are based on the product of the total delivered energy and 
the  CO2  intensity  of  the  energy  mix.  This  intensity  also  takes  into  account  the  inefficiencies 
between the primary energy spending at the energy system level and the energy that is delivered 
to the buildings. Further data processing of 5 year data indicated that the average overall energy 
efficiency of the energy reaching the buildings, including the inefficiencies in the plants, electric 
grid, boilers, and heat network, is 0.60. Based on the relations between the boundary conditions, 
the average CO2 intensity was found to be 0.22 kg CO2 per kWh of primary energy and 0.37 kg 
CO2 per kWh of delivered energy that involves the value of 0.60. In comparison, this is a lower 
intensity than the standard CO2 emissions factor for consumed electricity in the Netherlands at 
0.44 kg CO2 per kWhe [25]. Equation 3 unifies the boundary conditions and represents the CO2 
emissions that are directly or indirectly emitted by the energy use of the buildings, ΣCO2. Here, 
c is the CO2 intensity of the energy resource, i.e.  0.202 kg CO2 per kWh for natural gas [26], η is 
the average overall energy efficiency, ET is the delivered energy, and EPE is the primary energy. 


CO
2


c


/



E
T



Ec

PE

(3)  

At the same time, the level of match between the supply and demand of exergy can indicate the 
ability  or  inability  of  a  particular  energy  allocation  to  reduce  primary  energy  spending  at  the 
energy system level [7]. For example, when a high exergy resource, such as natural gas, is used 
to satisfy a low exergy demand, such as space heating, there is a low level of exergy match. This 
situation results in an additional spending of primary energy resources at the energy system level 
to make-up for the exergy that is destroyed at the building level without being used, such as the 
re-supply of natural gas to produce electricity. When the scope of analysis is broadened to the 
energy system level, it is clear that there can always be better uses for exergy that is supplied in 
excess  of  the  exergy  demand.  The  additional  CO2  emissions  impact  that  arises  in  the  energy 
system based on the level of mismatch in the supply and demand of exergy is provided by the 
Rational  Exergy  Management  Model  (REMM)  [7].  As  developed  in  Ref.  [7],  Equation  4 
provides the compound CO2 emissions as the sum of direct and indirect CO2 emissions and the 
avoidable CO2 emissions impact in the energy system due to the level of exergy mismatches. 



CO
2

i



c

c
j
i








1



Ψ

Ri







E
T

(4) 

In Equation 4, the first term that is multiplied by ET involves the ratio of c and η as given in the 
previous  equation  to  unify  the  boundary  conditions.  In  contrast,  the  avoidable  CO2  emissions 
impact (second term) involves the ratio of the average CO2 content of the energy resources that 
are used at the energy system level, cj and the parameter, ψRi. The parameter ψRi bar indicates the 
average  level  of  match  between  the  supply  and  demand  of  exergy  as  the  rational  exergy 
management efficiency. Table 3 provides typical values for various technological options based 
on Ref. [7]. These options are limited to those that pertain to the energy system of the campus.  

Table 3. Typical Values for the Parameter ψRi based on Ref. [7] and/or Calculated    

Technological Options 

  Heat based on heat-only boilers using natural gas 
  Cooling based on electricity from the electric grid 
  Heating based on pumping of heat from geothermal wells  
  Electricity based on electricity only power plants 

ψRi 
0.04 
0.04 
0.24 
0.35 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

  Heating and/or cooling with heat pumps based on ATES 
  CHP (including electricity and thermal energy components) 

0.42 
0.80 

As indicated in Table 3, the value of the parameter ψRi for the provision of heat from heat-only 
boilers  using  natural  gas  is  0.04.  In  contrast,  the  value  of  the  parameter  ψRi  for  CHP  is  about 
0.80. The values of the parameter ψRi range between 0 (poor level of exergy match) to 1 (best 
level of exergy match). CHP represents a better level of exergy match than heat-only boilers.  

The share of electricity that is used for the cooling of buildings, which is a low exergy demand, 
is unknown. This share will change based on the building function. For example, data centers 
have higher cooling loads that can range between 25% to 37% of the total energy usage [27] or 
about 50% to 74% of the electricity load. Considering the different building functions, as a 
campus  average,  25%  of  the  electricity  load  may  be  used  for  space  cooling  purposes.  In 
addition, the three buildings with ATES also use these units to reduce the electric cooling load 
[15, 17]. In these buildings, the respective share was assumed to be 15% of the electricity load.          

Equation 5 provides the means of finding the value of the parameter ψRi as a weighted average 
of the relevant values in Table 3 and the associated energy shares based on sx from s1 to sx where 
x is the number of technology options. The calculated values of the parameter ψRi at the building 
level are given in Figure 9. The campus average that is given in the red line is ψRi = 0.42.      

Ψ

Ri

x


 



1


s

Ψs

x

Ri



E
T

/

E
T





(5) 

Figure 9. Values of the Parameter ψRi for Buildings (Bars) and Campus Average (Line)  

Based on Equations 4 and 5, Figure 10 provides both the reported CO2 emissions (lower bars) 
and the avoidable CO2 emission impacts that persist in the energy system based on the parameter 
ψRi (upper bars) per building. The proportion between the two components change based on ψRi.   

11 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 10. Compound CO2 Emissions at the Building Level 

Integration with spatial dimension. Three inputs of data, namely energy, exergy, and compound 
CO2 emissions, are integrated with the spatial dimension as depicted in Figure 11. The labels of 
the buildings in Figures 7-8 were matched to the respective spatial positions. As preparation for 
this process, the open source 3D buildings feature of the TU Delft campus buildings in Google 
Earth Pro [28], which were drawn based on the 3D modelling software Google SketchUp [29], 
were studied. For each building, the 3D building details were used as a reference to custom draw 
polygons around the buildings that can be color coded according to the analysis results.    

Figure 11. Integration of Data Inputs with the Spatial Dimension 

Data analysis 
The data analysis is conducted based on the identification of energy, exergy, and CO2 emissions 
data with  the  spatial  dimension. The  juxtaposition of the different  inputs of data  at the spatial 
dimension  required  principles  to  represent  the  data  visually  on  the  buildings.  These  principles 
were  based  on  methods  of  comparing  and  clustering  the  data  and  assigning  codes  to  the  data 
groups.  The  representation  of  these  clusters  enabled  the  mapping  of  the  campus  based  on  the 
inputs and provided a focus to compare the buildings to each other on a campus-wide landscape.  

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Campus-wide comparison. The ratio of the total delivered energy ET in Equation 1 and the total 
exergy εT in Equation 2 provides the average Carnot factor for a particular building. This ratio as 
given  in  Equation  6  can  distinguish  those  buildings  that  use  a  higher  or  lower  share  of  high 
exergy  energy  resources  than  others.  Figure  12  provides  a  scatter  plot  with  axes  of  ET 
(horizontal) and εT (vertical axis). The 45° line represents the condition ET = εT or CF = 1. The 
distance of the individual data points from the line is proportional to the value of CF in Equation 
6. Based on the histogram on the left top inset in Figure 12, the data points are grouped into bins 
with intervals of 0.05. The grouping does not necessarily follow the magnitude of the energy or 
exergy  spending.  For  example,  the  red  data  points,  which  represent  the  SIMONA  Research 
Flight Simulator and Data Center, uses entirely electricity that has a CF = 1. The building has a 
high exergy spending relative to other buildings. The grey dot is the average value of both axes.             

CF 

TE

(6) 

Scenario comparison. According to the analysis method, scenarios for possible changes in the 
campus energy landscape over time were considered. Table 4 provides the variables in the four 
scenarios. In each scenario, values of the present status of energy usage in the campus buildings 
were applied multipliers according to the scenario definition. The application of the scenarios to 
the spatial mapping enabled a visual representation of the possible changes over time given that 
one of the scenarios is implemented. These scenarios are also important given the targets of the 
campus to reduce energy usage and increase the share of renewable energy by the year 2020.  

Figure 12. Scatter Plot of the Total Energy and Exergy of Buildings and CF Groupings  

Table 4. Definition of Five Scenarios to Assess Possible Changes in the Energy Landscape 

Scenarios 

Variable /Scenario Description 

Scenario 1 

Energy savings in campus buildings 

 Diffusion  of  energy  saving  measures 

to  reduce 

electricity usage by 10% and thermal energy by 15% 

Present 
Value 

- 

Scenario 
Value 

10% (EL) 
15% (EH) 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Scenario 2 

Electricity load with reduced electric cooling 

 Increased  supply  from  CHP  in  summer  mode  for 

the use of waste heat with absorption cooling  

Scenario 3 

Share of CHP contribution in the heat network 
 Increased share of CHP in the heat network  

Scenario 4 

Scenario 5 

Share of heat-only boilers in the heat network 

 Substituted  with 

the  use  of  geothermal  energy 

in a lower temperature distribution network 

Reduction in CO2 emissions from electricity usage  
 Increased  share  of  wind,  solar  and  bioenergy  from 

on-site renewable energy projects on campus 

25% 

10% 

25% 

50% 

75% 

40% 

- 

20% 

The first scenario is based on the diffusion of measures to capture additional energy savings on 
campus  in  the  immediate,  near  term.  These  savings  are  a  modest  representation  of  the  full 
energy savings potential. In the second scenario, the existing CHP units are used in a summer 
mode to reduce the use of high exergy electricity for purposes of cooling in buildings that is a 
low exergy demand. This scenario is represented by reducing the high exergy electricity load 
of the respective buildings by 15%. The reduced amount is shifted over to the CHP units in 
which the waste heat is used based on absorption cooling. Initially, this change is restricted to 
buildings that are connected to the heat distribution network. In addition, the 7 buildings that 
are not connected to the network are included in another possible variation of this scenario.  

The next three scenarios propose other changes in the heat distribution network of the campus. 
The  second  scenario  modifies  the  existing  high  temperature  heating  network  based  on  a  25% 
increase in the share of the CHP, which is accompanied by a lowering of the share of heat-only 
boilers  by  the  same  amount.  In  the  third  scenario,  a  second  heat  distribution  network  with  a 
lower supply temperature is introduced that is based on geothermal energy. The campus already 
has plans for such a change based on a partnership with commercial partners [30]. In the last 
scenario,  the  campus  is  envisioned  to  increase  the  deployment  of  on-site  renewable  energy 
for electricity generation by 20% based on wind, solar, and/or bioenergy. The progression of 
scenarios follows a focus on energy savings and increases in the use of renewable energy.    

Currently, all scenarios are tested on a separate basis. In the future, different variables in the 
scenarios as given in Table 4 can be mixed and matched for other desired normative futures. 
It is also possible that multiple scenarios are arranged across the time dimension starting with 
energy savings, the optimized use of waste heat, and the maximization of renewable energy. 
Table 5 provides the results of the application of the scenarios on the spatial dimension.  

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 5. Results from the Spatial Exergy Mapping for TU Delft Campus Buildings 

Present Case 

Scenario 1 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 5. Results from the Spatial Exergy Mapping for TU Delft Campus Buildings (Cont.) 

Scenario 2 

Scenario 3 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 5. Results from the Spatial Exergy Mapping for TU Delft Campus Buildings (Cont.) 

Scenario 4 

3.2 Case Study: Energy/Exergy Analysis of Building Clusters at KTH and Albano District 

The second case study is based on building clusters at the KTH Royal Institute of Technology 
in  Stockholm,  Sweden.  KTH  is  the  first  polytechnic  of  Sweden  and  one  of  Scandinavia’s 
largest higher education institutes in technology. KTH also adopted the Vision 2027 strategy 
document that outlines the overall objective of the campus environment to be embodiments 
of  bold  and  sustainable  urban  design  as  one  of  Europe’s  most  sustainable  technical 
universities [31-32]. In addition, the university is a member of the International Sustainable 
Campus  Network  (ISCN).  Accordingly,  KTH  implements  a  Sustainability  Charter  [33]  in 
which the campus reports on the sustainability performance of buildings. In 2014, the KTH 
campus  as  a  whole  used  68,360  MWh  of  energy  [33]  that  resulted  in  an  average  of  about 
258.5 kWh of energy usage per  m2 of floor area per year considering all campus buildings. 
This  represented  a  14%  reduction  from  the  level  of  energy  usage  in  2012  that  was  79,078 
GWh [33]. Most recently, KTH was also the recipient of the 2015 ISCN Campus Excellence 
Award  for  its  campus  master  planning  based  on  32  projects  [34].  These  include  plans  to 
establish a CO2 neutral campus development in the Albano district of Stockholm [35].  

Furthermore, the state-owned enterprise Akademiska Hus that owns and manages most of the 
buildings  that  Swedish  universities  use,  including  KTH,  targets  to  reduce  campus-wide 
energy purchase at supplied energy per m2 by 50% between 2000 and 2025 [36]. The ability 
to reach this goal requires KTH to increase the pace of energy savings to about 4% per year 
[36]. Existing projects to reduce energy purchases include the use of a heat pump system for 
central heat recovery from servers, the survey of roof space on campus that is suitable for the 
placement of solar panels, and projects that reach below Swedish building code benchmarks 
[33]. Such a context makes the campus an ideal platform to analyse building energy usage.  

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Energy data collection and processing 
Data for the KTH campus was collected at the building level for a selected number of buildings 
and at the energy system level. The collection of data at both levels enabled a broadening and 
deepening  of  the  analysis  at  the  same  time.  The  building  level  data  made  it  possible  to 
differentiate  the  various  energy  profiles  based  on  the  diverse  functions  of  the  buildings  in 
providing  educational,  research,  administrative,  and  other  services  to  the  campus  community. 
The  energy  system  level  data  made  it  possible  to  enlarge  the  scope  of  analysis  beyond  the 
campus into the energy system, including the district heating and cooling network. The approach 
is also in line with the integrated energy system perspective that is vital for modelling [37].  

Selection  of  the  Building  Cluster.  Akademiska  Hus  is  in  the  process  of  making  building  level 
data available to the university administration based on an Energy Portal [13]. Currently, data on 
the monthly purchased electricity of 41 buildings on the main and related campuses of KTH is 
available in this portal. For the selection of buildings that will be included in the building cluster 
for the analysis of this paper, the annual purchased electricity per m2 of floor space for the most 
recent year (2014) was calculated per building and grouped into categories by building function.  

Figure 13 shows the annual purchased electricity of 39 buildings that satisfy different building 
services, including as faculty buildings and laboratories. The various averages for the different 
categories of buildings are provided as marked within Figure 13. For example, faculty buildings, 
which represent mixed used buildings with offices, lecture rooms, laboratories, and kitchenettes, 
purchase  an  average  of 118  kWh/m2/year. Such a value  is  about  the  average  of  the  electricity 
purchase of laboratory buildings at 174 kWh/m2/year and office buildings at 65 kWh/m2/year. 
Faculties  that  may  be  more  equipment  intense  have  a  higher  electricity  purchase,  e.g.  the 
Mechanical Engineering buildings. A similar situation appies to laboratory buildings, such as the 
Chemstry  buildings.  Buildings  in  the  “other”  category,  which  includes  the  KTH  Library,  the 
restaurant,  and  other  buildings  that  provide  a  series  of  specialzed  services  to  the  campus 
community, have a more diverse range of values. In contrast, the IT/Data Center and a storage 
building  are  excluded  from  Figure  13  since  these  buildings  represent  outliers  with  purchased 
electricity at over 1,500 kWh/m2 and less than 5 kWh/m2, respectively. As the average for the 39 
buildings that are included in Figure 13, the purchased electricity was 101 kWh/m2 per year.   

Figure 13. Annual Purchased Electricity per Square Meter per Building and Category 

18 

 
 
 
 
    
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 13 was used to support the original intent of the research work to select buildings with 
diverse  energy  profiles.  Accordingly,  a  cluster  of  8  buildings  was  selected  from  the  different 
building categories. The Civil and Architectural Engineering building, also known as Bergs, and 
the  Mechanical  Engineering  building  (43:38)  were  selected  as  faculty  buildings  that  provide 
mixed mode building services. One of the Chemistry Buildings (43:18) that has laboratories and 
was made to receive the waste heat of the IT/Data Center before the system was integrated into 
the campus heat distribution network was also included in the building cluster. In addition, the 
Sing-Sing building that hosts the Department of Industrial Economics and Management as well 
as lecture rooms and has an atrium that is used for campus events is also included. As buildings 
that provide specialized building services, the Library, the IT/Data Center, the new location of 
the university administration, namely the KTH Huset, and the Quantum Restaurant are further 
integrated  into  the  building  cluster.  The  Quantum  Restaurant  is  indicated  to  have  the  highest 
purchased electricity among all of the buildings included in Figure 13 after the IT/Data Center.   

Data Collection for the KTH Building Cluster. In the future, the Akademiska Hus Energy Portal 
is expected to provide building level data on the purchase of thermal energy, including heat and 
cold from district heating and cooling, which is currently missing. For this reason, the buildings 
that were selected for inclusion in the building cluster, which was limited in number as requested 
by Akademiska Hus officials, were submitted for additional data request. Based on the request, 
the  operational  reports  of  the  buildings  for  the  most  recent  year,  namely  the  “Driftrapport 
uppföljningsområde” reports, were obtained, which contained monthly energy purchase data for 
electricity, district heating, and district cooling. The values for the purchased amount of district 
heating were climate corrected. The purchase of hot water was not consistently available for all 
buildings. As a result, the purchase of hot water was not included in the overall analysis. Figure 
14 provides the graphs of the compiled data for the 8 buildings in the KTH campus cluster.   

Figure 14. Selected Buildings for Inclusion in the KTH Building Cluster 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

The compiled data in Table 6 indicates the differences in the energy profile of the buildings 
in the cluster. Among the faculty buildings, Civil and Architectural Engineering has a higher 
district  heating  purchase  at  139.4  kWh/m2/year  than  Mechanical  Engineering.  In  contrast, 
Mechanical Engineering, which has a relatively higher share of laboratory equipment, has a 
higher district cooling purchase year round and a higher electricity purchase. The Chemistry 
building has the highest district heating purchase at 268.8 kWh/m2/year as well as the lowest 
district cooling purchase at 6.5 kWh/m2/year among the buildings in the sample cluster. The 
Sing-Sing  building  has  a  similar  district  heating  purchase  as  the  Mechanical  Engineering 
building at 88.3 kWh/m2/year and an electricity purchase similar to KTH Huset.  

The  KTH  Library,  which  was  awarded  the  Helgopriset  Prize  for  the  restoration  of  existing 
buildings  [38],  has  a  district  heating  purchase  that  is  closest  to  the  average  value  in  the 
building  cluster  at  101.5  kWh/m2/year.  The  IT/Data  Center  has  the  lowest  district  heating 
purchase at 51.9 kWh/m2/year but the highest district cooling and electricity purchase due to 
the function of being a data center. Another feature of this center is that the cooling usage is 
relatively steady year round for a total of 1,504.5 kWh/m2/year purchase of district cooling. 
The  Quantum  Restaurant  also  takes  place  among  those  buildings  with  a  relatively  stable 
purchase  of  district  cooling  annually  but  at  a  lower  magnitude,  i.e.  65.5  kWh/m2/year. 
Including the IT/Data Center, an average building in the cluster purchases 113.1 kWh/m2 of 
district heating, 210.2 kWh/m2 of cooling, and 303.0 2 kWh/m2 of electricity per year.   

Table 6. Purchased Electricity, District Heating and District Cooling Characteristics 

Civil and Architectural Engineering (Bergs)  Mechanical Engineering Building (43:38) 

Season thermal energy peaks (heat/cold) 

 
  Relatively stable electricity purchase 

Annual Energy Profile (kWh/m2/year) 
□ EC : 13.1 

□ EL : 84.1 

□ EH : 139.4 

  Higher district cooling purchase year round 
  Electricity purchase is relatively higher  
Annual Energy Profile (kWh/m2/year) 
□ EC : 37.6 

□ EL : 164.5 

□ EH : 85.7 

Chemistry Buildings (43:18) * 

Industrial Economics Management (Sing-Sing) 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Significantly higher district heating purchase 

 
  Minor cooling, stable electricity purchase 
Annual Energy Profile (kWh/m2/year) 
□ EC : 6.5  

□ EL : 116.3 

□ EH : 268.8 

Season thermal energy peaks (heat/cold) 

 
  Relatively stable electricity purchase 

Annual Energy Profile (kWh/m2/year) 
□ EC : 13.1 

□ EL : 51.9 

□ EH : 88.3 

KTH Library 

KTH Huset 

  Lower district heating and cooling purchase 
  Relatively stable electricity purchase 

  Lower heating, similar cooling purchase 
  Relatively stable electricity purchase 

Annual Energy Profile (kWh/m2/year) 
□ EC : 19 

□ EL : 72.9 

□ EH : 101.5 

Annual Energy Profile (kWh/m2/year) 
□ EC : 21.9 

□ EL : 46.7 

□ EH : 80.1 

IT/Data Center * 

Quantum Restaurant * 

  District cooling purchase is high but steady 
  Highest electricity use per m2 on campus 
Annual Energy Profile (kWh/m2/year) 
□ EC : 1,504.5 

□ EL : 1658.8 

□ EH : 51.9 

  Relatively high but steady cooling purchase 
Second highest electricity purchase per m2  
 

Annual Energy Profile (kWh/m2/year) 
□ EC : 65.5 

□ EH : 89.9 

□ EL : 228.6 

* Indicates buildings with different y-axis scales above 35 kWh/m2/year to accommodate value differences 

The  Swedish  Building  Code  (BBR)  [39]  indicates  values  for  the  specific  energy  use  of 
residential  and  commercial  buildings.  Table  7  summarizes  the  values  in  the  BBR  for  the 
specific energy use of commercial buildings when the method of heating is other than electric 
heating.  Buildings  that  use  electric  heating  are  provided  with  other  values.  Stockholm  is  in 
climate  zone  3  with  a  value  for  specific  energy  use  at  70  kWh/m2.  Buildings  can  receive  a 
supplement in these upper bounds per m2 when the floor area is greater than 130 m2 [40]. For 
buildings  to  be  certified  under  the  scheme  of  the  Environmentally  Classified  Building, 
namely Miljöbyggnad, specific energy use must be less than a factor of 1, less than a factor of 
0.75, or less than a factor of 0.65 to rank as bronze, silver, and gold, respectively [41]. 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 7. Swedish Building Code Values for Building Specific Energy Use [39] 

Building specific energy use 

Buildings that have other heating 
means than electric heating 

BBR Value (kWh/m2 year) 

Zone 1 

Zone 2 

Zone 3 

Zone 4 

105 

90 

70 

65 

The aim of Akademiska Hus is that all new buildings must at least meet the requirements for 
Miljöbyggnad Silver [36]. Most recently, the new course and office building at the School of 
Architecture and the Built Environment (ABE) received Miljöbyggnad Gold based on energy 
savings  that  are  65%  less  than  the  BBR  building  code  [36].  In  addition,  one  remodelling 
project  at  the  KTH  campus  was  awarded  Miljöbyggnad  Silver  [36].  Another  project  to 
construct  a  new,  5-storey  building  for  offices  and  seminar  rooms  for  ABE  is  planned  to 
receive  Miljöbyggnad  Gold  once  completed  in  2017  [42].  The  new  building  will  also  have 
solar panels that will be part of a larger effort to integrate solar panels within the campus.     

Data Collection for the Energy System. The Värtaverket combined heat and power plant (CHP) 
is located in the east of the Hjorthagen district of Stockholm that is about a 2.5 km distance from 
the main KTH campus. In addition to CHP units, two heat pump stations nearby at Ropsten and 
Nimrod provide additional heating and cooling as needed based on the seawater at Lilla Värtan. 
Based on the outputs of the CHP plant and heat pumps, central Stockholm has one of Europe’s 
largest district heating and cooling systems. The heat distribution grid has a total length of about 
330 km and produces enough heat to cover the heating needs of about 140,000 houses a year 
[43]. The energy customers include the KTH campus and other buildings in central Stockholm. 
Figure 15 provides a rendering of the Värtaverket CHP facilities and Ropsten heat pumps.  

Figure 15. Representation of the CHP facilities (left) and heat pumps (right) based on Ref. [44]  

In a typical year, Värtaverket produced 4,530 GWh of energy [45]. Of this total, 3,287 GWh of 
heat and 313 GWh of cold were provided to the district heating and cooling (DHC) grid. A total 
of 930 GWh was provided as electricity. Table 8 provides the design capacities of the units in 
the Värtaverket CHP plant, including the heat pump stations.  Table 8 also provides the shares of 
the units in the total district heating capacity and the realized share of each unit in a typical year. 
CHP (47%) and the heat pumps (21%) have a dominant share in the district heating grid. The 
peak load boilers and electric boilers provide the remaining contribution to the district heating 
grid. In the district cooling grid, the heat pumps have a 100% share as indicated in Table 8.     

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 8. Technical Specifications of the Värtaverket Facility Based on Refs. [45-46]   

Värtaverket CHP Plant Facility  

Existing CHP unit KVV6  
Bio-oil fired CHP unit KVV1 
New biofuel CHP units (KVV8) [24] 
Water based heat pumps Ropsten 1, 2 
&3 
Water based heat pumps Nimrod 
Peak load boilers 
Electric boilers 
Gas turbine 

Total (Existing) b

Maximum Capacity (MW) 
EC 
0
0 
0 

EH 
310
320 
280 

EL 
145
190 
130 

250 

36 
620 
230 
0 
1766 

110 

48 
0 
0 
0 
158 

0 

0 
0 
0 
54 
465 

Share in Total DH a
Capacity   Realized  

0.18 
0.18 
N/A 

0.14 

0.02 
0.35 
0.13 
0.00 
1.00 

0.47 

N/A 

0.21 

0.19 
0.13 
0.00 
1.00 

a Based on capacity data and realized district heating share data in Refs. [45-46]  
b Does not include the new biofuel CHP units (KVV8) to be commissioned in January 2016 

Figure 16 provides a simplified schematic of the main energy conversion processes based on the 
input of biofuels, coal, and other sources, such as electricity. The facility has a shipport and is 
located near the train station to minimize the impact of transport based on the use of boat and 
rail, including for biomass [46]. In the near future, the share of renewable energy will increase 
above 70% based on the new biofuel based CHP plant KVV8 that will be installed in addition to 
the existing units KVV6 and KVV1 [46]. The installation is also indicated to assist the targets of 
the city of Stockholm for becoming fossil-fuel free in the long term by the year 2050 [47].   

Figure 16. Schematic of the Värtaverket CHP Plant that Services the KTH DHC Substation  

In the Värtaverket site, the mean value for the supply temperature in the flow conduit is about 82 
°C (355 K) and the return temperature in the return conduit is about 44 °C (317 K) [48]. These 
values confirm with the target of having the return temperature be lower than 50 °C (323 K) on 
the KTH campus. The owner of the Värtaverket CHP Plant and the supplier of the DHC grid 
Forum  charges  the  campus  with  a  higher  price  if  the  return  temperature  is  60  °C  (333  K)  or 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

higher  [49].  This  provides  an  incentive  to  purchase  and  utilize  the  heat  more  rationally.         

In the summer, heat pumps based on seawater intake from the sea bed at 4°C (277 K) supplies 
the buildings with chilled water at about 6°C (279 K) that returns back to the facilities at about 
16 °C (284 K) [50]. The heap pumps can operate either as standalone or in parallel or series with 
waste heat depending on the actual cooling requirements in the Stockholm area [50]. The facility 
also  has  an  Aquifer  Thermal  Energy  System  (ATES)  component  with  12  wells  that  increases 
peak capacity [50]. In the winter, heat pumps are made to benefit from treated waste water [50].   

The  KTH  campus  has  a  central  distribution  substation  behind  the  KTH  Library  at  the  old 
campus plant, namely Värme- och kraftcentral, which directs the purchased energy from the city 
DHC grid to the campus buildings. In addition, the Centre for High Performance Computing at 
the  IT/Data  Center  was  installed  with  a  heat  pump  system  that  recovers  waste  heat  from  the 
servers based on the need to cool 36,384 central processing unit (CPU) cores [51]. To some 
extent, the system reduces the energy load to cool the supercomputers [51]. The output of the 
waste heat recovery was used in the Chemistry buildings until 2014 before being reconnected 
to the central distribution substation to feed the supply of heat to the campus buildings [52].  

Data Collection for the Albano District Buildings. The Albano area, once an industrial site, is 
located  between  KTH,  Stockholm  University,  and  Karolinska  Institute  [53].  By  the  year 
2019,  the  area  is  expected  to  start  becoming  a  liveable  campus  and  sustainable  urban 
development  with  educational  facilities  (e.g.  lecture  halls)  as  well  as  accommodation 
facilities  for  about  15,000  students  and  guest  researchers  for  the  joint  use  of  the  three 
universities  [53].  The  Albano  district  is  expected  to  have  classrooms  and  auditoriums  with 
flexible  solutions,  study  carrels  and  group  workplaces  for  students,  and  new  research 
environments  [53].  The  main  entrance  of  the  campus  will  be  located  in  front  of  the  existing 
AlbaNova campus. In addition, some of the buildings are envisioned to produce as much heat 
and  electricity  as  consumed  on  an  annual  basis  with  hybrid  energy  systems  [53].  The 
proximity of housing and educational facilities is also expected to reduce CO2 emissions [53].  

As  a  future  urban  development  project,  there  are  limited  projections  for  energy  usage  at  the 
building level. The most available source of estimated energy data is for the southern part of the 
first phase of the  construction  process with  a  cluster of 10 buildings with a  total floor  area of 
98,000 m2 [54]. In this cluster, 5 of the buildings with a floor area of 60,000 m2 are expected to 
be  for  lecture  purposes  and  5  of  the  buildings  for  residential  uses.  The  specific  energy  data 
estimates for electricity, heating, and cooling are given in Table 9 based on Ref. [54].       

Table 9. Estimated Energy Usage by Building in the Albano District (Based on Ref. [54]) 

Building Function 

  Lecture 
  Residential 

Average 

Specific Energy Profile (kWh/m2/year) 

EH
23 
19 
22 

EC
8 
0 
5 

EL
32 
54 
40 

Total Floor 
Area (m2) 
AT
60,000 
38,000 
98,000 

Comparison of Building Data by Function. Based on the empirical data for buildings in the KTH 
cluster, the purchased electricity, heating, and cooling was weighted with the total floor area of 
the buildings to obtain average values by building function. Equation 7 provides the approach 
for finding these average values. Here, EH is the purchased energy from district heating, EC is the 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

purchased  energy  from  district  cooling,  EL  is  the  purchased  electricity,  and  ET  is  the  total 
purchased energy. Ab is the floor area of each building in the category of the relevant function 
that is being considered from b =1 to x. AT is the total floor area in the category. These values 
were calculated and compared to the estimated energy profile of the Albano building cluster.  

E
T

x


 



1


b

/
AA
b
T







E

H



E

C



E

L

(7) 

In  addition  to  a  comparison  by  similar  building  function  in  the  two  clusters  of  KTH  and  the 
Albano district, Figure 17 provides a collective comparison of all buildings based on the annual 
energy usage per m2 by energy component, i.e. purchased district energy, district cooling, and 
electricity.  The  IT/Data  Center  is  given  separately  since  the  axis  values  are  significantly 
different. Figure 18  provides  a  collective  comparison  of  all buildings from  an  exergy point  of 
view. There is a downscaling of magnitude in the exergy values since the energy values, except 
electricity,  are  multiplied  by  CF  less  than  1.0.  Buildings  that  use  less  electricity  have  a  more 
significant downscaling, e.g. Chemistry building. At the energy system level, Figure 19 provides 
the results of the level of match in the supply and demand of exergy for the CHP and the district 
heating  and  cooling  grid.  Based  on  the  shares  of  the  technologies  that  provide  a  value  of  the 
parameter ψRi at 0.49, the avoidable CO2 emissions impact per unit of energy in the present case 
is 0.06 kg CO2 per kWh. One of the motivations of the scenarios is to reduce this impact.     

Figure 17. Comparison of the KTH and Albano Clusters based on Energy (Present Case) 

 Figure 18. Comparison of the KTH and Albano Clusters based on Exergy (Present Case) 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Figure 19. Shares in the Energy System (Left) and Exergy Profile of the Present Case (Right) 

4. Implications for the Net-Zero Exergy District Planning of Östra-Sala backe 

EULF  aims  to  address  different  scales  in  the  application  of  spatial  energy  data  for  policies 
with  scopes  ranging  from  the  building  and  urban  context  up  to  the  member  state  and 
European levels [11]. Similar to this aim, this contribution also aims the process of scaling-up 
the best practices of spatial data acquisition for university campuses and building clusters to 
the urban district level and in particular, a special form of urban district based on the target of 
net-zero exergy district (NZEXD). According to the definition, a district is a NZEXD if and 
only if the annual consumption of energy is less than or equal to the production of energy at 
the same grade or quality level [7]. The Östra Sala backe district project is in the process of 
being planned  to  meet the NZEXD target  as  analyzed  in Refs. [8-9]. Table 10 provides  the 
phases  in  the  planning  process  based  on  a  total  of  four  main  phases.  The  integration  of  a 
building and/or building cluster spatial dimension can further support the aims of the project.  

In addition, Figure 20 depicts the process of scaling-up best practises from the scale of a single 
building to the scale of a pilot district or its sub-component (e.g. phase one) and to the level of 
an  extended  district  (e.g.  phase  two)  all  the  way  to  an  entire  district  and/or  city.  These  best 
practises are referred to as “exergy-green” solutions due to the awareness of bringing together 
green  solutions  in  an  exergy-conscience  way  that  considers  the  need  to  secure  better  exergy 
matches  between  the  demand  and  supply.  For  example,  solutions  in  the  first  phase  all  have  a 
high level of exergy match that is measured by the parameter ψRi, which is found to be 0.84 [8]. 
The  district  is  also  one  of  the  case  studies  in  the  International  Energy  Agency  (IEA)  EBC 
Annex 64 on Optimized Performance of Energy Supply Systems with Exergy Principles [55].    

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

Table 10. Depiction of the Four Phases of the Östra Sala backe Project in Sweden 

Visual Representation of Phases 1-4 

Description 

• Mix of housing types and  
  elements of business premises 

• Approximately 55,000 square 
  meter GFA (gross total area) 

• Neighborhoods in and around 
  Årsta center with 500 homes 

• Many of these houses will  
  be zero-energy buildings 

• Mix of housing types and  
  elements of business premises 

• Public transport interchange  
  is planned at shopping center 

• Mix of housing types and  
  elements of business premises 

• Southernmost district in  
  the area of development 

Figure 20. Process of Scaling-Up Best Practises Leading to an Extended District/City Level 

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

In  particular,  the  realization  of  the  CO2  mitigation  potential  from  an  exergy  point  of  view 
depends  on  the  use  of  dedicated  metrics  (e.g.  the  parameter  ψRi  and  the  NZEXD  target), 
decision-making  algorithms,  and  scenario  implementation.  Figure  21  integrates  these  aims 
into  four  key  steps  that  can  be  implemented  in  districts  in  the  process  of  scaling-up  best 
practises towards an exergy transition and realizing the CO2 mitigation potential of the district. 
These key steps include modelling of the NZEXD status of the district (step 1), modelling the 
level of exergy matches and compound CO2 emissions (step 2), considering scenarios for net-
zero  statuses  for  exergy  and/or  CO2  emissions  (step  3),  and  selecting  a  scenario  based  on  the 
results of the decision-making tools and the initiation of the scenario implementation (step 4).        

Figure 21. Four Key Steps in the Process of Planning for an Exergy Transition 

5. Conclusions for the Use of Spatial Data to Support an Exergy Transition 

The  integration  of  location  data  can  support  local  authorities,  such  as  CoM  signatories,  in 
realizing  the  full  potential  of  planning  and  implementing  energy  related  measures,  including 
measures  to  improve  the  level  of  match  between  the  supply  and  demand  of  exergy.  The 
integration  of  location  data  for  appropriate  indicators,  including  energy,  exergy  and  CO2 
emissions,  can  also foster the realization of  an  “exergy transition”  towards a  more  sustainable 
energy  landscape  in  the  future.  The  two  pilot  case  studies  of  this  JRC  EULF  Workshop 
contribution indicate the means of utilizing location specific data to assess the performance of 
buildings in university campuses and related building clusters, namely the campus of TU Delft 
in  the  Netherlands  and  KTH  in  Stockholm.  The  best  practices  of  these  pilot  cases  also  hold 
potential  for  the  planning  of  a  NZEXD  in  Sweden,  namely  the  Östra  Sala  backe  project.  The 
indicators that are used in these case studies can be assessed for inclusion in the dashboard of 
energy indicators in the context of the INSPIRE Directive, especially since the existing Boolean 
attributes  can  be  used  in  related  calculations.  Moreover,  an  extension  to  the  cities  of  the 
campuses or the NZEXD district can provide suitable cases for the EULF pilot project.    

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JRC EULF Workshop on Spatial Data for Modelling Building Stock Energy Needs 

6. References 

1.  Covenant of Mayors (CoM) <http://www.covenantofmayors.eu/index_en.html>.  
2.  Sustainable  Energy  Action  Plan 

(SEAP)  <http://www.covenantofmayors.eu/ 

actions/sustainable-energy-action-plans_en.html>. 

3.  European  Union  Location  Framework  (EULF)  <http://ec.europa.eu/isa/actions/02-

interoperability-architecture/2-13action_en.htm>. 

4.  Euroheat and Power, Delivering Sustainable Energy to the Heart of the City, 2014. 
5.  Spatial 

the  European  Community 

Information 

(INSPIRE)  Directive 

in 

<http://inspire.ec.europa.eu/>. 

6.  Heat Roadmap Europe 2050  <http://www.heatroadmap.eu/>.  
7.  Kılkış, Ş., A Rational Exergy Management Model to Curb CO2 Emissions in the Exergy-
Aware  Built  Environments  of  the  Future,  PhD  Thesis,  KTH  Royal  Institute  of 
Technology School of Architecture and the Built Environment, Stockholm, 2011. 

8.  Kılkış,  Ş.  Energy  System  Analysis  of  a  Pilot  Net-Zero  Exergy  District,  Energy 
Conversion  and  Management,  Vol.  87  pp.  1077-1092  <http://dx.doi.org/10.1016/ 
j.enconman.2014.05.014>. 

9.  Kılkış, Ş., Exergy Transition Planning for Net-Zero Exergy Districts, Energy, Article In 

Press, http://doi:10.1016/j.energy.2015.02.0092015. 

10. Energy  Label  Atlas  of  the  Netherlands  for  Delft  <http://energielabelatlas.nl/#zuid-

holland/delft/16/52.0008/4.3768>. 

11. Bloem,  H.,  Boguslawski,  R.,  Borzacchiello,  M.,  Cipriano,  P.,  Kona,  A.,  Martirano,  G., 
Maschio,  I.,  Pignatelli,  F.,  Location  Data  for  Buildings  Related  Energy  Efficiency 
Policies, JRC Technical Report Report EUR 27411 EN, 2015.  

12. BPIE Data Hub <http://www.buildingsdata.eu>.  
13. Akademiska Hus, Energy Portal <https://portal.akademiskahus.se/>. 
14. TU Delft Energy Monitor, Realisation of the Long-term Agreement on Energy (MJA3) < 

http://www.energymonitor.tudelft.nl/Campus>. 

15. TU Delft Energy Monitor, Building Data < http://www.energymonitor.tudelft.nl/Building 

>.  

16. TU Delft Energy Monitor, Combined Heat and Power Plant <http://www.energymonitor. 

tudelft.nl/Home>. 

17. Vliet,  E.,  Flexibility  in  heat  demand  at  the  TU  Delft  campus  smart  thermal  grid  with 
phase change materials, M.Sc. Thesis, Delft University of Technology, Process & Energy 
Department, Delft, 2013. 

18. TU Delft, Personal communication with Ad Winkels, dated 19 February 2015. 
19. IEA  ECBCS  Annex  49,  Summary  report:  low  exergy  systems  for  high  performance 
buildings  and  communities,  Schmidt  D,  Torío  H,  Editors.  Fraunhofer  –  IBP:  Stuttgart; 
2011. 

20. Hammond,  G.,  Engineering  Sustainability:  Thermodynamics,  Energy  Systems,  and 

Environment, International Journal of Energy Research 28, pp. 613–39, 2004. 

21. Kilkis, S., Green Cities and Compound Metrics Using Exergy Analysis, Encyclopedia of 
Energy  Engineering  and  Technology,  Taylor  and  Francis:  New  York,  Published  online: 
23 Jul 2012; 1-7. 

29 

 
 
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31 

 
 
Split of total energy consumption into space
heating and domestic hot water usage

Lasse Engbo Christiansen, Philip Anton de Saint-Aubain,
Peder Bacher, and Henrik Madsen
DTU Compute, Technical University of Denmark,
laec@dtu.dk

November 12, 2015

1

Introduction

The energy performance of buildings can be assessed in many ways. As
more and more data is collected on the actual energy consumption then
it becomes feasible to make more data driven assessments. When using
the total energy consumption of a building it is important to acknowledge
that it is composed of several components. The two overall components
are related to the building envelope and the occupancy behaviour. For
labeling the energy performance of a building it is important to consider
which components to include - often the interest is in the envelope.

In this paper a method for separating the total heat load into domestic
hot water (DHW) heating and space heating is presented. Data from an
individual residential building connected to a district heating network in
Denmark is used. It consists of a time series of 10 minute values of total
heat load, which is the sum of DHW and space heating. DHW is used by
the residents for showering, dish washing, etc., and the space heating is used
to heat the house. The DHW heating is seen as spikes on top of the space
heating. This is due to the fact that showering and dish washing use intense
amount of energy for short periods.

The described method for separating the total heat load is quite generic
and can therefore easily be used for other applications, where spikes need to

1

be separated from other signals. The separation can be useful for building
energy performance estimation based on data ([15] and [10]) and for load
forecasting where the presented method was actually used [2]. The separated
DHW consumption can be used for example for constructing load proﬁles for
DHW ([18] and [1]), the latter using in-homogeneous Markov chain models
providing a fully data-driven stochastic modelling approach. Other impor-
tant applications are control for heating systems enabling demand response
for integration of renewables, for example by using a hot water tank [7] or
the building structures [13] for energy storage.

Separating consumption signals into sub-components has been studied quite
intensively the last decades, mainly for electrical appliance load monitoring
([8] and [5]). Also residential water consumption dis-aggregation into end-
use categories has been studied [12], using high resolution readings (5 sec.).
Such methods are more event based where patterns are matched. Methods
for spike detection has been studied in-depth for medical applications such
as EEG. Several approaches to determining amplitude thresholds are used,
for example diﬀerent statistical approaches [3], and ﬁltering and wavelets
[14]. A comprehensive review of diﬀerent techniques is given by [19].

In the present study a statistical time series approach [11] based on kernel
smoothing techniques for time series ([4] and [16]) is applied. These are
combined with robust estimation (see [9] and [17]) enabling the separation
of the very high spikes to be carried out without interfering with the remain-
ing signal. The idea of the method is to use a non-parametric function to
estimate the space heating. The space heating changes during the day and
is a low-pass ﬁltered response of mainly the outdoor temperature and the so-
lar radiation, but it also depends on thermostat settings. Consequently, the
heating changes over time at frequencies related to those variables and the
method is therefore designed such that the non-parametric estimate follows
these changes, with limited inﬂuence from the spikes, which are signiﬁcantly
higher than this estimate and therefore can be separated as DHW heating.

2 Data

The data used in the study consists of the total heat load of a single-family
freestanding residential building with two occupants. Sønderborg District
Heating Company located in Southern Denmark delivered the data. The

2

period used is covering one month from 1st of March to 1st of April 2010.
The data was logged approximately every 10th minute. The unit of the
heating is mega joule per hour [MJ/h]. The total heat load is represented
t = 1, . . . , N }, where Qt is the total heat load
with the time series {Qt,
at time t and N = 4607 is the number of observations in the times series.
Figure 1 shows the raw data from this period. Some of the spikes are as
high as 160MJ/h and have been cut oﬀ by the frame in order to make the
lower variations visible. The ﬁgure shows that in a two weeks period from
Friday 12th until Friday 26th there are no spikes and the total heat load has
very little variation. It is assumed that the residents were on vacation and
left the house during these two weeks. The inclusion of this vacation period
provides an opportunity to see the performance of the models in separating
the DHW heating from the space heating, since the models should predict
that no DHW heating is used during that period.

Figure 1: Total energy consumption for March 2010.

3 Results

In the following an ordinary kernel smoother and a robust kernel smoother
are presented for separating the DHW and space heating.
It is assumed
that the spikes represent DHW heating and the remaining signal represents
space heating, therefore spikes signiﬁcantly higher than the kernel smoother
estimate needs to be identiﬁed.

3

01020304050Mar 01Mar 06Mar 12Mar 18Mar 24Mar 30Consumption [MJ/h]3.1 Ordinary kernel smoother

A kernel smoother is a method to estimate the underlying function of some
relation given noisy measurements. Kernel estimation is a non-parametric
estimation technique, where no explicit description of the true function is
needed and only a bandwidth parameter needs to be set [16]. The kernel
smoother is

ˆg(t) =

(cid:1)

(cid:80)N

i=1 Qik (cid:0) t−i
h
(cid:80)N
i=1 k( t−i
h )

(1)

2π exp{− u2

where ˆg(t) is the kernel estimate for a given time t and h is the band-
width parameter. From the formula it is seen that the kernel smoother is
a local weighted average around the given time t, hence a zero order lo-
cal estimate. The function k(·) is the kernel, which determines how the
weight should be put on the neighboring data points. The Gaussian kernel
k(u) = 1
2 } is chosen. The bandwidth h is a smoothing parameter
which determine the width of the kernel. As h → ∞ the estimate will go
towards the overall mean value ˆg(x) = ¯Y . Therefore for large values of h
the kernel estimate will be biased. As h → 0 the kernel estimate would just
be equal to the nearest data points and there will be no bias, but a large
variance. Hence the bandwidth needs to be tuned for the particular data
and in the present case a bandwidth equal to h = 12 (Corresponding to 2
hours) is found adequate.

The kernel smoother estimate, ˆg(t), is used to separate the total heat load
into DHW and space heating. The DHW heating is found by

ˆQwater
t

= I (Qt > qthres · ˆg(t)) · (Qt − ˆg(t))

(2)

where I(·) is the indicator function. Hence spikes above qthres · ˆg(t) are
identiﬁed as DHW heating and the DHW is found by subtracting the kernel
estimate. The separation threshold qthres needs to be tuned according to
the local variance of the space heating signal, and such that the DHW usage
In the present case
spikes are signiﬁcantly higher than this noise level.
qthres = 1.3. Since only one time series is available a scheme for tuning of
qthres is left for future studies where many diﬀerent series are included. The
space heating is found simply by subtracting DHW heating from the total
heat load

ˆQspace
t

= Qt − ˆQwater

t

(3)

4

The result of the separation is shown in Figure 2. The uppermost plot shows
the original data together with 1.3 · ˆg(t). The middle plot shows the DHW
heating. The lower plot shows the space heating. Therefore, the sum of
the two lower plots is equivalent to the uppermost plot. It is seen that the

Figure 2: Separating with kernel smoother. The red dashed line is 1.3 · ˆg(t).

estimated space heating has many short-lived spikes, which is not good. The
reason is that the kernel estimate, ˆg(t), is too aﬀected by the spikes, which
cause that some spikes are below 1.3 · ˆg(t). The next section is dedicated to
show how a modiﬁcation by using a robust kernel smoother can reduce the
inﬂuence from the high spikes.

3.2 Robust kernel smoother

The idea behind robust estimation is to make the estimation method robust
against outliers or extremes[17]. Optimization methods generally try to min-
imize some function ρ(ε) of the residuals ε. In this case the kernel estimator
in (1) is a zero order local regression model [6] and can be formulated as

k (cid:0) t−i
h

(cid:1) (Qi − θ)2

(4)

ˆg(t) = arg min

θ

1
N

N
(cid:88)

i=1

5

Time [GMT][MJ/h]510152025Mar 01Mar 08Mar 15Mar 22Mar 29Hot water heating510152025Space heating510152025Total heatingwhere the residuals minimized are εi = Qi − θ, hence the least squares
method minimizes the sum of the squared residuals ρLS(ε) = ε2. The es-
timation is made robust by replacing the quadratic function with Tukey’s
biweight function also known as the bisquare function, see [9]. The biweight
estimation minimizes the following function

ρbiweight(ε) =

ε2(ε4−3 ε2γ2+3 γ4)
γ4

(cid:40) 1
6
1
6 γ2

if |ε| ≤ γ

if |ε| > γ

(5)

The biweight function is approximately quadratic for small residuals and
constant for residuals larger than γ. A plot of ρbiweight(ε) and a scaled
version of ρLS(ε), together with their derivatives is shown in Figure 3. The
derivative is also also known as the inﬂuence function. The biweight function

Figure 3: Left: Tukey’s biweight and a square function. Right: The deriva-
tives also known as the inﬂuence function.

induces that outliers do not cause displacement of the resulting estimate. For
residuals further away than the chosen γ threshold the inﬂuence function
ρ(cid:48)(ε) = 0, hence the estimate is independent of how much further away the
observation is. For the actual heating data a reasonable value was found to
be γ = 7 Mj/h. For a given time t the robust kernel estimate is found by
solving the optimization problem

ˆg(t) = arg min

θ

1
N

N
(cid:88)

i=1

k (cid:0) t−i
h

(cid:1) · ρbiweight

(cid:0)Qi − θ(cid:1)

(6)

The result of the kernel estimation with a biweight function is shown in
Figure 4. It is seen that almost all of the spikes in the space heating are

6

ρLS(ε)ρbiweight(ε)−γγ0ρ(ε)ε0ρLS(ε)ρbiweight(ε)−γγ0ρ(ε)εFigure 4: Separating with robust kernel smoother. The red dashed line is
1.3 · ˆg(t).

removed compared to the ordinary kernel estimate. So using the robust
kernel reduces the problem that the kernel estimate was too aﬀected by the
large spikes.

4 Discussion

Certainly, it is needed to apply the method on many series with diﬀerent
patterns, and where the DHW and space heating is measured separately,
in order to validate the performance more thoroughly. Then also a scheme
for automatically tuning the parameters: the kernel bandwidth h, the sep-
aration threshold qthres and the γ threshold for the biweight function in the
robust estimation scheme, can be developed. Furthermore, for the present
heating series a relative value of kernel estimate instead of ﬁxed value as
the separation threshold instead was used. However, how this threshold is
constructed should be studied in further details.

The use of a robust kernel smoother can be extended in many ways.

In

7

Time [GMT][MJ/h]510152025Mar 01Mar 08Mar 15Mar 22Mar 29Hot water heating510152025Space heating510152025Total heatingthe current study a local mean value is estimated this can be extended to
robust local polynomial smoothing. Other explanatory variables can also
be included, e.g. ambient temperature, solar radiation, and electrical con-
sumption. However, one should not expect to explain all the variation in the
data in this way as most houses represent a relative large thermal mass that
combined with thermostats creates dynamics in the response. Furthermore,
many houses has reduced temperature setpoints during the night which will
also give a dynamic response in the total heat consumption.

The proportion of the total heat load that is used for space heating obviously
depends on the outdoor climate and therefore on the season. This can
be considered as a signal to noise ratio and it would also be of interest
to investigate how this approach works for diﬀerent periods of the year.
Another important consideration is the sampling frequency. In this study
a sample is recorded every 10 minutes which is on the same time scale as
typical DHW usage for dish washing and showering while at the same time
being frequent relative to the dynamics of the space heating.

5 Conclusion

A method for splitting the total consumption into DHW and space heating
is presented. The method is constructed as a generic method for separating
spikes from low frequency variations in time series data. It is assumed that
the DHW heating are spikes added to the space heating in the total heat
load. First a local average kernel is applied, however the spikes aﬀect the
estimates and the results were not acceptable. Therefore a robust estimation
is suggested and it is shown that this enables a much better separation of
the DHW and space heating. It is found that the method is very promising
and that it can be concluded that the method can be used for separating
the DHW heating from space heating. However, it is also suggested that
a study, where the method is applied on many diﬀerent heating series and
where the DHW and space heating are measured separately, is needed for a
more in-depth development and veriﬁcation of the method.

The described method can be used in other ﬁeld of operation as well. It is
not locked to the application of heating. In any problem, where noisy data
include spikes, the described method could possibly provide a technique for
removing the spikes.

8

6 Acknowledgments

We would like to acknowledge the Danish iPower project and CITIES (DSF
1305-00027B) for providing the ﬁnancial support and Sønderborg Fjern-
varme for providing the measurements, which enabled us to carry out this
research.

7 References

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[8] G.W. Hart. Nonintrusive appliance load monitoring. Proceedings of the

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[10] M.J. Jim´enez, H. Madsen, and K.K. Andersen.

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10

Buildings’ energy efficiency and RES potential in urban contexts 
Main results of the project Cities on Power, www.citiesonpower.eu 

Guglielmina Mutani, DENERG – Politecnico di Torino 

Abstract 

The research project Cities on Power, co-financed by the Central Europe program, was focused on the 
promotion of renewable energy sources (RES) in urban contexts, involving citizens in finding the best 
solutions  to  reduce  their  energy  bills.  Starting  from  this  objective,  the  study  was  extended  to  the 
analysis  of  energy  savings  solutions  for  residential  buildings.  To  encourage  citizens  participation  in 
energy issues,  a web-based interactive tool was created. This paper describes a methodology based 
on an open source GIS tool to estimate the energy consumption, energy saving potential and the use 
of renewable energy sources in the Metropolitan City of Turin. 

The estimation of thermal and electric energy-use for residential buildings 

The models used for the energy assessment of existing  residential buildings can be divided  into the 
following  categories: 
-  top-down  models:  energy-use  data  at  urban  scale  are  compared  with  climate  variables,  census 
results and statistical surveys to determine an average energy consumption for existing buildings. 
These models can compare different variables, but cannot distinguish  spatial variations in  energy 
consumption of a Municipality or a territory; 

-  bottom-up models: these models, at a building scale, are used to evaluate the energy balance of a 
single building with high detail; together, a set of energy consumption models, can be combined to 
evaluate the energy consumption of blocks of buildings, districts or cities; for good results at urban 
scale a large number of data is required. These models can be also used to evaluate energy savings 
model after building retrofits. 

In the project Cities on Power an hybrid approach has been applied with:  
-  single  building  models,  derived  by  the  bottom-up  approach,  were  used  to  represent  the  energy 
consumption of residential buildings in a district or a city through a detailed spatial representation 
considering the percentage of heated volume, the period of construction, and the compactness of 
residential buildings;  

-  statistical  models  at  urban  scale,  derived  by  the  top-down  approach,  were  used  to  validate  the 
above energy buildings’ models taking into account the spatial variability of the urban contest, the 
socio-economic  level,  the  type  of  users,  the  buildings’  retrofit  level  and  other  important  factors 
influencing the energy-use of buildings. 

For this research, the open source tools Quantum GIS (Lisboa version 1.8.0) and GRASS GIS (version 
6.4.2)  were  used.  The  analysis  considers  the  Metropolitan  City  of  Torino  with  about  fifty 
Municipalities representing approximately a million and a half  of inhabitants, in the North-West part 
of Italy on a territory of  6,821 km2.  

 
 
The data used to define the energy-use models were the following:  
Energy-use data: Sustainable Energy Action Plan (SEAP-Covenant of Mayor), considering for example 
the 2005 as reference year for the city of Torino, and a buildings’ survey on energy consumptions of 
2,000 typical residential buildings in Turin connected with the district heating network, 
Buildings’, population and territorial contest  data: Technical Map of the Metropolitan City of Torino 
(CTP),  Municipal  Technical  Map  for  the  City  of  Torino  (CTC),  other  information  as  the  height  of  the 
buildings and the type of roof obtained from Lidar datasets, the 2011 ISTAT census at census section 
scale, Digital Terrain Model of Piedmont Region (DTM), and 
Climate data: outdoor air temperature and the Heating Degree Days (HDD) by climatic ARPA database 
(http://www.regione.piemonte.it/ambiente/aria/rilev/ariaday/annali/meteorologici). 

Considering  the  spatial  distribution  of  the  heated  volumes,  for  residential  buildings  with  different 
characteristics, the energy-use of the single buildings’ models was applied on urban scale. The 2011 
ISTAT census data were also used to improve the models considering the average percentage of the 
heated volume, the type of buildings’ envelope,  systems’ efficiency, period of  construction,  and the 
number of inhabitants on census section scale. 
Comparing the bottom-up and top-down results, correction factors can be determined to have good 
results with the  overall approach. The simplified models of space heating and hot water production 
energy-use developed in the bottom-up approach do not take into account important factors such as 
the  spatial  variability  in:  solar  gains,  indoor/outdoor  air  temperatures  and,  mainly,  the  retrofit  of 
buildings  that  may  have  changed  their  energy  consumptions  over  the  years.  To  consider  these 
variables and to adapt the model to real energy consumption data, the model of the specific energy-
use  of  buildings  was  multiplied  by  a  correction  factor  as  function  of  a  typical  built  environment 
analysed. In  Table 1 the data about Turin and other 5 cities in Turin’s Province are reported.  

In Figure 1 the linear models on energy-use for space heating and hot water production applied to the 
Municipality  of  Turin  and  others  cities  near  Turin  (normalized  on  the  average  value  of  the  Heating 
Degree  Days  of  the  last  10  years)  are  represented.  The  correction  factors  were  used  to  limit  the 
differences between the  model at building and at municipality scale; higher values were founded  in 
the Municipalities with low details GIS database.  

More  adjustments  are  necessary  to  calibrate  the  models  to  the  real  built  environment  considering 
that in the municipalities of the Metropolitan City of Torino, the consumption data are much higher 
than  the  data  recorded  by  the  Sustainable  Energy  Action  Plans.  This  gap  can  be  explained  by  an 
analysis  of  demographic  data,  which  highlights  that  the  real  percentage  of  heated  volume  is  lower 
than the one  registered by ISTAT census data as the real  number of inhabitants are  lower than the 
theoretical one (considering  an average value of available gross floor area  for  inhabitant  of 30 m2  - 
Regional Piedmont Law 56/1977).  

The  energy-use  models  describe  correctly  the  energy  consumption  of  heated  buildings  but  they  do 
not fully understand the spatial variability of the heated and unheated spaces and the real extension 
of  the  indoor  spaces,  especially  in  mountain  areas.  Therefore  a  demographic  factor  was  adopted, 
taking into account the relationship between theoretical and real population or heated and unheated 
buildings’ spaces. For Torino the demographic factor is 67% and in the Metropolitan City of Torino it 
varies from 7% to 151%, with higher values on the mountains (with an high percentage of holiday’s 
houses). 

Municipality 

Turin 
Ivrea 
Strambino 
None 
Carmagnola 
Pianezza 

Volume of buildings  
(at 2005) Mm3 
142.24 
3.66 
3.37 
2.36 
6.96 
3.07 

HDD  
(UNI 10349) 
2617 
2737 
2720 
2623 
2717 
2735 

HDD  
(2005) 
2703 
3247 
3174 
3171 
3299 
3220 

HDD  
(last 10 years) 
2462 
2752 
2850 
3001 
3076 
3043 

Space 
heated % 
0.94 
0.83 
0.95 
0.95 
0.94 
0.96 

Correction 
factor 
1.04 
1.05 
1.02 
0.93 
1.30 
1.31 

Table 1. Volume of residential buildings, HDD values, percentage of space heated and the correction factor. 

Figure 1. The specific energy-use for space heating and hot water production as function of the building construction period 
and the surface to volume ratio S/V for residential buildings for cities in the Metropolitan City of Torino: Torino, Strambino, 
None, Carmagnola and Pianezza (considering an average value of heating degree days of the last 10 years). 

For  the  electricity  consumptions  in  residential  buildings,  a  similar  approach  has  been  adopted 
considering  that  the  electric  energy-use  depends  mainly  by  the  number  of  inhabitants;  using  the 
average  percentage  of  heated space  from  2011  ISTAT  census  and  then  considering  the  uninhabited 
dwellings per building, the number of inhabitants was estimated. On a Municipality scale, the electric 
consumption in the residential sector for the City of Torino refers to the reference year 2005 and it 
has  been  collected  from  the  SEAP:  1,092,488  MWh/year.  For  the  calculation  of  per  capita 
consumption, the real number of inhabitants in 2005 of 900,748 have been used and the annual per 
capita consumption is therefore equal to about 1,213 kWh/inhabitant.  To validate this value for the 
electric  consumption  in the Metropolitan City of Torino, the  report “Eighth report on the energy of 
the Province of Turin” was also used with an average annual consumption of 1,215 kWh/inhabitant. 
By applying the per capita electric consumption to the number of inhabitants in each municipality and 
in each building, the relative electrical energy use is calculated. 

 
 
In Figure 2 and 3 the thermal and electric specific energy-use for each municipalities in the Province of 
Turin  are  represented.  These  results  have  been  obtained  from  the  data  at  buildings  scale.  About 
electric energy-use value in the north-west side of the province, some adjustments have been made 
considering the high percentage of holiday’s houses. 

Figure  2.  The  specific  energy-use  EPgl  [kWh/m2/y]  for
space  heating  and  hot  water  production  in  residential 
buildings in the Province of Turin. 

Figure 3. The specific electric energy-use [kWh/inhabitant] 
for residential buildings in the Province of Turin. 

The estimation of energy savings potential 

The evaluation of the energy savings  potential has been carried  out simulating the different sets of 
renovation  actions  considering  the  national  (D.M.  December  28th  2012)  and  regional  (D.G.R. 
4/08/2009,  n.  46-11968)  regulative  framework.  Starting  from  the  energy-use  data  of  residential 
buildings,  it  is  crucial  to  understand  the  feasibility  of  buildings’  retrofit  interventions  for  energy 
savings  in  a  low,  medium  and  long  terms.  To  evaluate  the  retrofit  feasibility,  the  following  socio-
economic variables of 2011 ISTAT census have been used, as the energy savings rate depends on the 
type of building but also on the interest and possibility of citizens to invest in renovation:  

1. The age factor (fa): the ratio between population with an age in the range of 25 - 69 years and

the total population.

2. The employment factor (fe): the ratio between the employed part of population and the total

active population.

3. The property factor (fp): the ratio between the number of families that own their apartment

and the total number of families.

4. The family factor (ff): the ratio between the number of families composed by 1-2 components

and the total number of families.

5. The  education  factor  (fed):  the  ratio  between  the  population  with  high  school  diploma  or

higher instruction level and the total population.

6. The building's occupation factor (fo): the percentage of buildings that are occupied during the

whole year.

7. The  period  of  construction  (fpc):    the  number  of  buildings  built  before  1945  over  the  total

number of buildings.

Each  of  the  analyzed  factors  represents  some  aspects  of  the  socio-economic  condition  of  the 
community, influencing the feasibility of buildings’ renovation interventions. Each factor has a specific 
weight, depending on how much its influence is strong;  bigger is a factor, higher is the feasibility of 
renovation interventions on residential buildings.  
To calculate the global feasibility index F for each census section of the Metropolitan City of Torino a 
multiple linear regression model can be apply, normalizing all the socio-economic factors to a medium 
value of 0.5: 
𝐹 = 𝛼1 ∙ 𝑓𝑎,𝑛 + 𝛼2 ∙ 𝑓𝑒,𝑛 + 𝛼3 ∙ 𝑓p,n + 𝛼4 ∙ 𝑓𝑓,𝑛 + 𝛼5 ∙ 𝑓𝑒𝑑,𝑛 + 𝛼6 ∙ 𝑓𝑜,𝑛 + 𝛼5 ∙ 𝑓𝑝𝑐,𝑛. 

Considering the global feasibility index as the number of energy performance certificates APE due to 
retrofit interventions for building, the following multiple linear regression models have been founded: 
𝐹𝐴𝑃𝐸/𝑏 =   0.421.𝑓𝑝𝑐,𝑛 +   0.137.𝑓𝑒𝑑,𝑛 +   0.078.𝑓𝑒,𝑛 +   0.024.𝑓𝑜,𝑛. 

It seems that the more influencing variables are: the buildings’ period of construction, the education 
level, the employment rate and the occupied percentage of the buildings. 
Applying  different  levels  of  renovation  to  the  buildings  as  indicated  in  Table  2,  different  results  in 
energy  savings  can  be  reached.  The  four  classes  were  defined  from  the  average  value  of  feasibility 
index F (0.5) ± its standard deviation (0.08). 

Feasibility index 
Number of buildings in the 
Metropolitan City of Torino 
Number of buildings in Torino 

Renovation level 

First class 
<0.42 

Second class 
0.42 - 0.50 

13% 

42% 

Third class 
0.50 - 0.58 

39% 

Fourth class 
> 0.58 

6% 

20% 
windows 
substitution 

54% 
+ boiler 
substitution 

23% 
+ thermal insulation of 
slab and roof 

3% 
+ thermal insulation of 
facades 

Table 2.  The number of buildings for each renovation feasibility class and level of renovation. 

Some results in energy savings are reported Figure 4 and 5 considering a short-long term perspective. 

Figure  4.  Energy  saving  potential 
efficiency in residential buildings’ at short-medium term 

improving  energy 

Figure  6.  Energy  saving  potential 
efficiency in residential buildings’ at medium-long term 

improving  energy 

In Turin the energy saving potential is quite low (from 19 to 27%) but in the South-East municipalities 
can  reach  42-68%.  Different  objectives  can  be  reached 
in  energy  savings  for  the  various 
municipalities. 

 
 
 
 
 
The estimation of RES potential with solar roof integrated technologies 

The objective of this analysis was to use the current solar irradiation models to calculate the incident 
solar  irradiation,  taking  into  account  the  shadowing  effects  caused  by  the  terrain  model  and  the 
buildings, but also the exposure, the slope and the available area of the rooftops. 
The  Geographical  Information  Systems  (GIS)  were  used  to  represent  data,  but  also  to  produce  new 
information, enabling the public administrations to evaluate their energy policies and providing to the 
citizens a powerful  instrument to obtain a preliminary indication  of the  feasibility and the potential 
energy production of a solar plant on the roof of their building.  
The procedure used in this analysis can be synthesized in:  
• defining  the  digital  surface  model  (DSM)  of  the  built  environment  to  evaluate  the  obstructions

•
•

and the rooftops on which the solar technologies could be installed,
evaluating the incident solar irradiation and,
calculating the potential energy production for each building and then for each Municipality with
solar technologies integrated in the rooftop of existing buildings’.

The potential of rooftop integrated solar energy sources was analyzed taking into account the urban 
context and the thermal and electric energy-uses. In Figure 6, the results for the Metropolitan City of 
Torino  are  represented.  Only  the  10%  of  the  solar  energy  captured  by  the  rooftops  can  be  directly 
used by the citizen to produce thermal and electric energy; this result is typical for high density urban 
areas  and  varies  from  5  to  23%,  influenced  principally  by  the  buildings’  roofs’  orientation  and  type 
and by the shadowing effects.  

Figure 6 - The contribution of solar renewables in energy-use in the residential sector. 

Conclusions 

The energy saving potential and the renewable solar energy sources are differently distributed on the 
territory  and  the  energy  policies  should  consider  this  feature  to  improve  their  effects.  Every 
Municipality  can  then  establish  different  objectives  in  term  of  buildings’  energy  savings  and 
renewable energy production. 

More results of the project Cities on Power Project can be found in: 
• “Analisi del fabbisogno di energia termica degli edifici con software geografico libero. Il caso studio di

Torino”, G. Mutani, G. Vicentini, Journal: LA TERMOTECNICA, vol. 6, July-August 2013, pp. 63-67, ISSN: 0040-
3725 (http://www.latermotecnica.net/pdf_riv/201307/20130715004_1.pdf).

• “L’applicazione delle tecnologie fotovoltaiche integrate sulle coperture degli edifici con software GIS”, S.
Bonesso, G. Mutani, T. Hubina, A. Ramassotto, Journal: GEOmedia, vol.17, n.2, 2013, pp.6-11, ISSN 1128-
8132 (http://www.mediageo.it/ojs/index.php/GEOmedia/article/view/268).

• “Gli open data per l’analisi del fabbisogno di energia primaria degli edifici residenziali e del potenziale

risparmio energetico”, G. Mutani, G. Vicentini, Smart City Exhibition, Bologna, October 16-18th 2013, Ed.
Forumpa, pp. 15-37, ISBN 9788897169260.

• “Evaluating the potential of roof-integrated photovoltaic technologies using an open geographic

information system - La potenzialità dei sistemi solari fotovoltaici integrati nelle coperture degli edifici con
le tecniche GIS open source”, G. Mutani, G. Vicentini, 8th ENERGY FORUM on Advanced Building Skins, EF
ECONOMIC FORUM, November 5-6th, 2013, Bressanone (BZ), pp.87-92, ISBN 978-3-9812053-6-7
(http://www.energy-forum.com/fileadmin/content-2013.pdf).

• “La fattibilità degli interventi di riqualificazione energetica per gli edifici residenziali. L’applicazione ed i
risultati di un’analisi socio-economica nella Provincia di Torino”, G. Mutani, G. Vicentini, Journal: IL
PROGETTO SOSTENIBILE, December 2013, Edicom Ed., pp. 80-87, ISSN: 1974-3327
(http://www.ilprogettosostenibile.it/custom/sez_magazine.php?browse_id=1996).

• “Un modello per valutare il consumo energetico per la climatizzazione invernale degli edifici residenziali. Il
caso studio di Torino. A model to evaluate the heating energy consumption for residential buildings in
Turin”, G. Mutani, M. Pairona, Journal: L’Ufficio Tecnico, Ed. Maggioli, Italy, May 2014, pp.21-36, ISSN 0394-
8293 (http://www.preview.periodicimaggioli.it/browse.do?nr=2014_5&id=11).

• “Il consumo energetico per la climatizzazione invernale degli edifici residenziali. Un modello per Torino, A
model to evaluate the heating energy-use for residential buildings in Turin”, G. Mutani, Journal: NEO
EUBIOS, vol. 50, December 2014, pp. 16-23, ISSN: 1825-5515 (http://www.anit.it/neo-eubios-rivista).

• “Building energy assessment and urban form”, C. Delmastro, G. Mutani, L. Schranz, G. Vicentini, IX National
Congress AIGE - Associazione Italiana Gestione dell’Energia, Catania, 17-18 Settembre 2015, ISBN: 978-88-
940975-0-4.

• “Urban morphology and energy consumption in Italian residential buildings”, C. Delmastro, G. Mutani, M.
Pastorelli, G. Vicentini, International Conference on Renewable Energy Research and Applications ICRERA-
2015, Palermo, 22-25 November 2015, in press.

• “Buildings' energy consumption, energy savings and the availability of renewable energy sources in urban
contexts: the potential of GIS tools”, G. Mutani, G. Vicentini, Journal of Civil Engineering and Architecture
Research, Volume 2, Number 11, November 2015, ISSN: 2333-9128, in press.

• “The role of urban form and socio-economic variables for estimating the building energy savings potential at

the urban scale”, C. Delmastro, G. Mutani, L. Schranz, G. Vicentini, International Journal of Heat and
Technology, in press.

• Solar web portal:  http://energia.sistemapiemonte.it/ittb-torino.

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doi:10.2790/89997
ISBN 978-92-79-55082-9