bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven short-term load forecasting for heating and cooling demand in office buildings
Authors: Ashouri Araz , Shi Zixiao , Gunay H. Burak 
Published: November 2019

Abstract
Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings.
Exploratory study on clustering methods to identify electricity use patterns in building sector
Authors: Yilmaz Selin , Chambers Jonathan , Cozza Stefano , Patel Martin K. 
Published: November 2019

Abstract
In this paper, we perform a cluster analysis using smart meter electricity demand data from 656 households in Switzerland, collected during one year. First, we present the silhouette analysis to determine the optimum number of clusters for a k-means clustering approach. Secondly, we try different distance functions used in the k-means clustering to partition the samples into different categories. We find that the choice of distance function has no effect on the clustering performance. Finally, we investigate the “dimensionality curse” and find that low dimensions should be preferred to increase the quality of the clustering outcome.
Wind profile prediction in an urban canyon: a machine learning approach
Authors: Mauree Dasaraden , Castello Roberto , Mancini Gianluca , Nutta Tullio , Zhang Tianchu , Scartezzini Jean-Louis 
Published: November 2019

Abstract
Resolving the wind profile in an urban canyon environment means dealing with the turbulent nature of the stream and the presence of non-negligible flux exchanges with the atmosphere inside the canopy, making any deterministic model solution computationally very intensive. In this paper, a learning-from-data method is explored, which is able to predict the wind speed in an urban canyon at different heights, given a minimal set of input features. The experimental location is provided by a street canyon located at the Swiss Federal Institute of Technology campus in Lausanne, equipped with several measuring stations to record data at high temporal resolution. Different machine learning approaches are compared in order to predict the wind speed in two directions and at different heights inside the urban canyon: an optimized Ridge Regression outperforms the Random Forest algorithm. We find particularly high accuracy in predicting the wind speed in the highest part of the canyon. None of the proposed algorithms however is able to model in an accurate way the variation of the wind speed close to the ground.
Analysis of solar PV potential for roofs and façades in high dense residential urban scenario of Singapore
Authors: Ramkumar Thambiraj , Gamage Hansika , Xiao Erwan W. , Cassat Maxime 
Published: November 2019

Abstract
This paper aims to understand the potential of solar photovoltaics (PV) on building’s roof and façades in the dense residential neighborhoods of Singapore. The physical urban morphological parameters such as height, built form, distance between the buildings, orientation, plot ratio, building volume ratio and site coverage have impacts on the solar irradiation on the roof and façades at various intensities. With these parameters in consideration, solar irradiation has been simulated and analyzed for 12 existing neighborhoods of Singapore along with 36 additional modified neighborhoods.
Data-driven statistical analysis of energy performance and energy saving potential in the Flemish public building sector
Authors: Ma Yixiao , Reynders Glenn 
Published: November 2019

Abstract
This paper will focus on the energy performance of the public building sector in Flanders (Belgium) by analysing the recently published EPC database. The main aim of this paper is to firstly have a qualitive and quantitative overview of the current energy performance of different building categories in the Flemish public building sector. The non-residential building types (office, educational, healthcare etc.) will be categorized. Within each category, a set of typologically representative non-residential buildings will be further identified, while a data driven cluster analysis will be carried out in order to define these non-residential archetypes. Moreover, the less energy efficient building sets will be identified by benchmarking the energy performance within the defined building categories, and the relative potential energy saving targets and pathways will be quantified and evaluated in order to provide policy support in improving energy efficiency of the poorly performing public buildings.
Location-based data driven model for real estate market value analysis based on energy performance certification
Authors: Moretti Nicola , Tagliabue Lavinia Chiara , Dejaco Mario Claudio , Cecconi Fulvio Re 
Published: November 2019

Abstract
The market value of physical assets varies over time as a function of different factors. These factors are related, for instance, to the correct execution of construction works and installation of systems, the procedures for the operation of systems and equipment, the continuous implementation of maintenance operations and to the location and the perceived value. However, there is no clear evidence, in Italy, of how buildings’ market value is affected by the energy performance measured by the Energy Performance Certification (EPC), promoted thanks to the European Energy Performance of Buildings Directive in order to drive the assets’ energy efficiency. The aim of the research is the definition of a methodology for the identification of the impact of the energy performance on the market value of the assets. An analysis of the market values of assets has been carried out based on a public database. On this basis, an investigation of homogeneous assets’ price areas has been performed. The results have been geolocated and further processed, which allowed to associate differences in the market values to the energy performance retrieved from the Certificazione Energetica degli EDifici (CENED) database (the Italian regional EPC database). The buildings included in the analysis are residential buildings distributed in the regional territory (Lombardy Region). The methodology has been tested at national level but is potentially scalable in different contexts. It has been developed and applied in two case studies, cities with different size and characteristics located in the northern Italy.
Demonstration of heating demand peak shaving in smart homes
Authors: Beltram Lucas , Christensen Morten Herget , Li Rongling 
Published: November 2019

Abstract
Flexible heating demand in buildings plays an important role in achieving a carbon neutral society. For the district heating system of Copenhagen, heating demand flexibility can help to eliminate the use of fossil-fueled boilers that are used during peak-load periods. In this project, field tests in 16 apartments were conducted, aiming to gain insights into the use of information and communication technologies (ICT) to manage heating systems operation for flexible demand. The apartments are equipped with sensors and devices interfaced to an ICT system composed of blocks responsible for data storage, monitoring and control. In the experiments, we controlled temperature setpoints of individual rooms during defined periods of the day, and continuously developed control strategies throughout the tests in the heating season 2018/19. The final algorithm was implemented with features to reduce the rebound effect and include residents feedback. All the algorithms are generic and can be applied in other smart homes where heating supply to rooms is controlled using thermostats. The ICT system architecture used in the experiments showed to be a feasible way to implement demand side management (DSM) in the heating system, and the learning process of the experiments resulted in improvements on the control strategies, leading to a better system performance.
Multi-agent reinforcement learning for adaptive demand response in smart cities
Authors: Vazquez-Canteli Jose , Detjeen Thomas , Henze Gregor , Kämpf Jérôme , Nagy Zoltan 
Published: November 2019

Abstract
Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such that they react to price signals and coordinate with each other to shave the peaks of electricity consumption. We explore the use of multi-agent deep deterministic policy gradient (DDPG), an adaptive and model-free reinforcement learning control algorithm, for coordination of several buildings in a demand response scenario. We conduct our experiment in a simulated environment with 10 buildings.
An algorithm for the optimal management of air-source heat pumps and PV systems
Authors: Vivian Jacopo , Mazzi Nicolò 
Published: November 2019

Abstract
The progressive decentralization and decarbonisation of the energy system calls for an increased flexibility of the demand side. Model predictive control techniques can help users consume more efficiently by shifting their energy demand toward most convenient time windows. The present work proposes an algorithm to optimally shift the heating demand of a residential building equipped with an air-source heat pump and PV panels by exploiting the inertia provided by its thermal mass. Results show that the proposed look-ahead control strategy is able to achieve significant cost savings for the users without affecting the thermal comfort in the indoor environment.
Upscaling the flexibility potential of space heating in single-family houses
Authors: Dréau Jérome Le , Mellas Ilyasse , Vellei Marika , Meulemans Johann 
Published: November 2019

Abstract
Energy flexibility from residential buildings is foreseen as a solution to facilitate the integration of variable energy sources in the grid. In fact, the energy use in residential buildings is still high despite the different building regulations and some of their usages can be shifted in time. Among these usages, space heating showed an interesting potential for flexibility, especially in France, where 45% of the heating systems is electrical. The objective of this study is to evaluate the flexibility potential in typical French houses, looking at both the energy shifted and thermal comfort. Large differences are highlighted, mainly due to the thermal properties of the envelope and the type of emitter. The temperature drops are quick in poorly insulated building. Underfloor heating systems show a good flexibility potential due to the inertia of this type of emitter. Finally, quantitative data, such as the mean power decrease and power profiles, is provided to help the integration of flexible buildings in future energy grids.
The SMART sensor: fully characterizing radiant heat transfer in the built environment
Authors: Houchois Nicholas , Teitelbaum Eric , Chen Kian Wee , Rucewicz Sean , Meggers Forrest 
Published: November 2019

Abstract
In a conventional indoor environment, thermal comfort is supplied by an air based distribution system. This system is controlled by an air temperature (and occasionally humidity) sensor, and the role of radiation in thermal comfort is often overlooked. In a typical indoor environment, slightly less than half of the heat occupants shed to maintain thermal comfort is lost to convection. The other portion is lost to radiation. We have developed the Scanning Mean Radiant Temperature (SMART) Sensor to fully characterize radiant heat transfer in the built environment. Combining surface temperatures and geometry allows us to produce 3D thermal point clouds which may be meshed to produce watertight surfaces. The view factor between occupants and environmental surfaces may be calculated, allowing us to accurately model radiative heat transfer. Additionally, this may be calculated for any location in the space, allowing us to map spatial variation of the mean radiant temperature from a single scan. In this paper, we use the SMART sensor to calculate the spatial distribution of mean radiant temperature over a range of environmental conditions. Its performance is validated using a net radiometer. The sensor demonstrates that there is frequently significant spatial variation of mean radiant temperature in typical indoor environments up to 4 °C.
Distributed model predictive control of building energy systems coupled to geothermal fields
Authors: Baranski Marc , Stoffel Phillip , Kümpel Alexander , Stiller Martin , Storek Thomas , Schumacher Markus , Müller Dirk 
Published: November 2019

Abstract
In building energy systems (BES), operation strategies are a key factor to exploit the full potential of renewable energy sources and thus to reduce the CO2 emission and energy costs. However, advanced operation strategies like model predictive control require high engineering effort, which limits the applicability. To ease the implementation of model predictive control in BES, we present a flexible and robust distributed control framework. The framework supports the use of different model types and timescales for the various subsystems, allowing the user to tailor models from white to black modelling according to the available system knowledge. In a case study, we apply this framework to control a real-life industrial hall. We further simulate a case in which the hall is supplied by a geothermal field to investigate the sustainable long-term control of the field. We vary the temperature setpoint of the hall and adjust the thermostats in the office rooms. Since the set temperature of the hall and offices differs, the model predictive control framework finds an economic trade-off between the requirements of those two zones. Judging from these results, the approach and our implementation could be an important contribution to energy efficient operation of buildings with renewable energy sources.
Air to water heat pumps for heating system retrofit in urban areas: understanding the multi-faceted challenge
Authors: Calame Nicole , Freyre Alisa , Rognon Fabrice , Callegari Simon , Rüetschi Matthias 
Published: November 2019

Abstract
Air has an important role to play as a renewable resource to supply heat in urban areas. The implementation of heat pumps in multifamily buildings represents an important and underexploited development potential. Financial comparison of heating costs depending on building size and heat production technology shows how retrofit projects with air-to-water heat pumps compare to gas. Feedback from experience based on pilot projects presents the various challenges faced and proposes solutions.
The impact of battery storage technologies in residential buildings with sub-daily autonomy and EV contribution
Authors: Chatzivasileiadi Aikaterini , Ampatzi Eleni , Knight Ian P. 
Published: November 2019

Abstract
This study has been undertaken to gain a better understanding regarding the choice and impact of battery storage technologies in a use case with contribution of an electric vehicle to the overall domestic consumption. The study assessed the storage requirements of nine battery technologies for different residential building scales at the distribution level considering sub-daily autonomy periods. The use case explored in this paper assumed that the battery from an electric vehicle could contribute to the overall domestic consumption during the required hours of storage based on a scenario addressing demand response through peak shifting in 2030 (DR 2030) from an earlier study. After deriving the nominal capacity for each battery technology, the spatial requirements, including footprint, volume and mass, as well as the cost, for the scales of interest were estimated. The study showed that space and cost savings of up to 90% compared to a use case that do not consider EV contribution could be achieved. The choice of the most suitable technology according to its applicability in different building scales and different use cases should be carefully assessed.
Building integrated photovoltaic at NEST – Preliminary test bedding results
Authors: Wittkopf Stephen 
Published: November 2019

Abstract
This paper presents preliminary results on the visual and energy assessment of colored photovoltaics modules installed at the Meet2Create unit of the NEST pilot- and demonstration site at Empa in Dübendorf. The installation contains mono-crystalline PV modules with three different types of front glass (float, silk and satinated) and three different patterns printed on them (shutter, curve and ornament). Each printed PV module and three unprinted reference modules are monitored separately over a period of one year. With float glass, the visual appearance is dominated by reflections of the environment, an disturbing effect that does not appear with the matte and brighter satinated glass, where the colored print pattern remains visible across all viewing angles. Interestingly, the energy output is relatively similar. Compared to the unprinted reference PV modules, the losses with float, silk and satinated glass are 68, 75, and 72% respectively. While the different print patterns do not yield a significant difference. Satinated glass is thus an interesting alternative to the common float and structured front glass when visual appearance and energy efficiency matters.
Optimization method for photovoltaic integration in residential houses
Authors: Kichou Sofiane , Skandalos Nikolaos , Wolf Petr 
Published: November 2019

Abstract
Good design and sizing of photovoltaic (PV) systems is very important in order to effectively harvest energy and minimize the investment cost. The optimum tilt and azimuth angles at which a PV system should be installed are often debated. This paper evaluates the trade-off between annual energy generation and payback period reduction through the analysis of a small house with pitched roof integrated PVs in both East and West sides. Validated irradiance and PV models were used for the analysis. The optimum tilt and azimuth angles are found to be 35° and 10° respectively. Finally, a contour map plot illustrating all possible tilt and orientation angles, corresponding to a payback period less than 20 years, is provided. The results are valid for different building typologies and locations with similar climate conditions as Prague.
Simulation tool for planning smart urban districts in a sustainable energy supply – integrating several sectors in high resolution
Authors: Stickel Matthias , Marx Simon , Mayer Felix , Fisch Manfred Norbert 
Published: November 2019

Abstract
A tool for practical planning of smart urban districts has been developed, considering integration into sustainable energy supply. It is based on dynamic energy simulation in high resolution, focusing on sector coupling and individual control strategies in a simple workflow. It provides energy flows and climate balances of individual scenarios. The model includes district grids, all essential supply systems, energy storages and electrolysis (power to gas). The tool was successfully applied to implement an innovative energy concept and to realize one of the first climate neutral operated urban districts in Esslingen Germany.
Estimating time constants for underfloor heating control
Authors: Kull Tuule Mall , Thalfeldt Martin , Kurnitski Jarek 
Published: November 2019

Abstract
Time constants are used for tuning PID control parameters and should be correctly estimated to achieve a well-performing controller. However, the estimation of time constants for a very slowly reacting system such as underfloor heating is not easy in practice. In this work, different methods were used to estimate the time constants for underfloor heating. Graphical estimation and transfer function fitting were the core approaches. Measurement data and simulations on a calibrated model were used for the estimations. The resulting time constant values for the graphical method depended clearly on the setback duration and the values differed up to a factor of 100. For the transfer function method, time constant values did not vary significantly. For light construction, the time constant was around 10 hours and for massive, around 30 hours for the otherwise identical room.
Demand response potential of aggregated high-temperature storage radiators
Authors: Cai Hanmin , You Shi , Klyapovskiy Sergey , Bindner Henrik W. 
Published: November 2019

Abstract
This study experimentally evaluates the demand response potential of aggregated high-temperature storage radiators to support intermittent solar photovoltaic (PV) production. Nine storage radiators on one floor of an office building located in Copenhagen’s Nordhavn area are aggregated. Their demand response potential to compensate for the forecast errors of production from a large-scale PV installation in the same area is analysed. From experimental results, it is found out that the aggregated storage radiators have an activation time of 15 seconds and could provide a significant amount of energy storage while maintaining end-users’ comfort.
Sensitivity analysis of a simplified office building
Authors: Zeferina Vasco , Wood Ruth , Xia Jianjun , Edwards Rodger 
Published: November 2019

Abstract
The increase of thermal discomfort and the associated energy demand for the cooling of buildings further exacerbate the ongoing challenges for the design of new buildings and the adaptation measures of the current building stock to climate change. Dynamic, detailed and tailored building simulation methodologies are necessary to better understand and quantify the energy demand of buildings and consequences for supporting energy networks. The aim of this is to analyze the sensitivity of peak electricity demand for the provision of space cooling to different office building model parameters. In the research, two sensitivity methods, Morris Elementary Effect and Sobol, are used to evaluate the sensitivity of eight input model parameters. Relative to peak power consumption for cooling, the most important factor is the cooling set-point, followed by ventilation rate and internal heat gains, which are estimated to account for 38%, 26% and 25% of the variation. Regarding the annual cooling demand for the HVAC systems, these factors account for 35%, 33% and 58%, respectively.
Which sensor type at which location should offices with south orientated window choose to improve comfort and reduce energy consumption?
Authors: Borier O Minam , Kazanci O B , Olesen B W , Khovalyg D 
Published: November 2019

Abstract
The common placement of thermostats in offices might not reflect the actual thermal environment around occupants and influence the energy use. In this research, it is questioned which type of thermostat at which location better represents the conditions in the occupied zone and what is the effect on energy use. To answer this, simulations of indoor thermal environment, experiments in a climatic chamber and calculation of the energy use are performed for a two-person office room with a south orientated window. The results obtained allow to provide recommendations about the thermostat type and its placement in the office space.