N

November 2014 

NASA/TM–2014-218544 

RETScreen® Plus Software Tutorial 

Rene E. Ganoe 

Science Systems and Applications, Inc., Hampton, Virginia 

Paul W. Stackhouse, Jr. and Russell J. DeYoung 

Langley Research Center, Hampton, Virginia 
NASA STI Program . . . in Profile

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA scientific and technical information (STI) 

program plays a key part in helping NASA maintain 

this important role. 

The NASA STI program operates under the 

auspices of the Agency Chief Information Officer. 

It collects, organizes, provides for archiving, and 

disseminates NASA’s STI. The NASA STI 

program provides access to the NASA Aeronautics 

and Space Database and its public interface, the 

NASA Technical Report Server, thus providing one 

of the largest collections of aeronautical and space 

science STI in the world. Results are published in 

both non-NASA channels and by NASA in the 

NASA STI Report Series, which includes the 

following report types: 

• TECHNICAL PUBLICATION. Reports of 

completed research or a major significant phase 

of research that present the results of NASA 

Programs and include extensive data or 

theoretical analysis. Includes compilations of 

significant scientific and technical data and 

information deemed to be of continuing 

reference value. NASA counterpart of peerreviewed formal professional papers, but 

having less stringent limitations on manuscript 

length and extent of graphic presentations. 

• TECHNICAL MEMORANDUM. Scientific 

and technical findings that are preliminary or of 

specialized interest, e.g., quick release reports, 

working papers, and bibliographies that contain 

minimal annotation. Does not contain extensive 

analysis. 

• CONTRACTOR REPORT. Scientific and 

technical findings by NASA-sponsored 

contractors and grantees. 

• CONFERENCE PUBLICATION. 

Collected papers from scientific and 

technical conferences, symposia, seminars, 

or other meetings sponsored or cosponsored by NASA. 

• SPECIAL PUBLICATION. Scientific, 

technical, or historical information from 

NASA programs, projects, and missions, 

often concerned with subjects having 

substantial public interest. 

• TECHNICAL TRANSLATION. 

English-language translations of foreign 

scientific and technical material pertinent to 

NASA’s mission. 

Specialized services also include organizing 

and publishing research results, distributing 

specialized research announcements and feeds, 

providing information desk and personal search 

support, and enabling data exchange services. 

For more information about the NASA STI 

program, see the following: 

• Access the NASA STI program home page 

at http://www.sti.nasa.gov

• E-mail your question to help@sti.nasa.gov

• Fax your question to the NASA STI 

Information Desk at 443-757-5803 

• Phone the NASA STI Information Desk at 

443-757-5802 

• Write to: 

 STI Information Desk 

 NASA Center for AeroSpace Information 

 7115 Standard Drive 

 Hanover, MD 21076-1320
National Aeronautics and 

Space Administration 

Langley Research Center 

Hampton, Virginia 23681-2199 

 N

November 2014 

NASA/TM–2014-218544 

RETScreen® Plus Software Tutorial 

Rene E. Ganoe 

Science Systems and Applications, Inc., Hampton, Virginia 

Paul W. Stackhouse, Jr. and Russell J. DeYoung 

Langley Research Center, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

This work was performed in collaboration between the NASA Langley Research Center 

Science Directorate and the RETScreen® International team of CanmetENERGY Natural 

Resources Canada. Mr. Greg Leng and Mr. Urban Ziegler of RETScreen were very 

helpful in providing guidance and advice for how to use the tools for buildings at the 

NASA Langley Research Center. We also acknowledge collaboration with the Research 

Directorate including the NASA LaRC energy manager Mr. Robert Charles and Ms. Joan 

Hughes. NASA Headquarters funded this project through the Climate Adaptation 

Science Investigator Program. 

The use of trademarks or names of manufacturers in this report is for accurate re porting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics 

and Space Administration
Table of Contents 

Section Page 

 Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i 

 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 

1. Background and Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

 1.1 Tutorial scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

2. Getting Started. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

 2.1 System requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 2.2 Start a new project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3. Data Inputs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

 3.1 Consumption/production inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 3.2 Factors of Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

 3.2.1 Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 3.2.2 Production level. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 3.2.3 Occupancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 3.3 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 3.3.1 Degree days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 3.3.2 Solar insolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 3.3.3 Merge data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

 3.3.4 Apply filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

 3.3.5 Column options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4. Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

 4.1 Establishing a Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

i
 4.2 Comparison Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

 4.2.1 Cumulative sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

 4.2.2 Control chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

 4.2.3 Measurement and verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 4.3 Analytics Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 4.3.1 Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 4.3.2 RETScreen 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 4.4 Analytics options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5. Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 

 5.1 Create report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

 5.2 Edit report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

 5.3 Reporting tools and options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

 5.3.1 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

 5.3.2 Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 5.4 Create Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

 5.4.1 Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6. User Help and Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

 6.1 RETScreen Plus information directory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

 6.2 Templates and case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

7. General Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

 7.1 Prefeasibility analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

 7.1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

 7.1.2 Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

ii
 7.1.3 Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

 7.2 Performance assessment of new RET or energy efficiency measure . . . . . . . . . . . . . . . . 29 

 7.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 7.2.2 Electricity consumption analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 7.2.3 Electricity production analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

 7.2.4 Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

9. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

iii
Abstract

Greater emphasis is being placed on reducing both the carbon footprint and energy cost of 

buildings. A building’s energy usage depends upon many factors one of the most important 

is the local weather and climate conditions to which it’s electrical, heating and air 

conditioning systems must respond. Incorporating renewable energy systems, including 

solar systems, to supplement energy supplies and increase energy efficiency is important to 

saving costs and reducing emissions. Also retrofitting technologies to buildings requires 

knowledge of building performance in its current state, potential future climate state, 

projection of potential savings with capital investment, and then monitoring the 

performance once the improvements are made. RETScreen Plus is a performance analysis 

software module that supplies the needed functions of monitoring current building 

performance, targeting projected energy efficiency improvements and verifying 

improvements once completed. This tutorial defines the functions of RETScreen Plus as well 

as outlines the general procedure for monitoring and reporting building energy performance. 

 

iv
1. Background and Purpose 

Development of strategies to mitigate the impacts associated with climate change has become an 

important topic of study [1]. One facet of climate change mitigation involves minimizing the 

carbon footprint associated with anthropogenic energy consumption [2]. Studies have shown 

that the building sector is responsible for approximately 40% of the total energy consumed 

globally; thus, the improvement of building energy efficiency is especially important [3]. 

Reducing the carbon footprint and thus the energy associated with a building allows more to be 

spent on a given building’s operations and infrastructure, even with higher energy cost. Both 

energy and carbon reduction can be achieved through introduction of renewable energy 

technologies (RETs), also through retrofit alternatives and alteration of operational procedures 

[4]. Additionally, on-going monitoring of energy usage in new and existing buildings is a key to 

reducing primary energy intensity, as demonstrated in the IPCC (Intergovernmental Panel on 

Climate Change) Fifth Assessment Report [5]. A comprehensive decision support analysis 

program (as described in the tutorial) to manage building energy usage near real-time and 

evaluate the energy performance of new energy technologies on the current building sector 

would facilitate the integration of RETs into current buildings resulting in decreased overall 

energy usage. Furthermore, this would provide management with the decision support tools 

necessary to strategically develop projects for future efficiency improvements. 

RETScreen® International was developed by Natural Resources Canada’s CanmetEnergy 

Diversification Research Laboratory (CEDRL) in collaboration with partners such as Renewable 

Energy and Energy Efficiency Partnership (REEEP), National Aeronautics and Space 

Administration (NASA), the United Nations Environment Programme (UNEP), and the Global 

Environment Facility (GEF). The RETScreen Suite is a comprehensive software tool that 

provides users with decision support to model and analyze clean energy projects and energy 

efficiency improvements. RETScreen is well-established with over 420,000 users in 222 

countries, and software available in 35 languages. To date, RETScreen is directly responsible for 

an estimated $8 billion in user energy savings; furthermore, use of the software indirectly 

contributes to a conservative estimate of 20 million ton per year reduction in greenhouse gas 

emissions [6]. 

The RETScreen Suite is comprised of two modules, RETScreen 4 and RETScreen Plus. 

RETScreen 4, an Excel-based software package facilitates analysis of clean energy projects and 

allows users to develop energy efficient project models and generate a five-step analysis report 

including 1) energy usage, 2) cost, 3) emissions, 4) financial benefits, and 5) risk associated with 

the project. Prior to the development of energy efficiency analysis, the initial state of the 

building must be well established, thus creating a base case to evaluate future energy usage 

improvements. To this end, RETScreen developed the Performance Analysis Module called 

RETScreen Plus, which employs the monitoring, targeting, and reporting (MT&R) of current 

building performance with integration of meteorological and solar insolation data. This module 

facilitates mapping energy usage over time to demonstrate performance changes due to 

improvements or building alterations; furthermore, it provides an approach to quantitatively 

measure building efficiency. RETScreen Plus also utilizes measurement and verification (M&V) 

1
functions which provide initial decision support by gauging financial feasibility prior to 

implementation of energy efficiency measures or renewable technology systems [7]. 

The utility of RETScreen Plus is that it addresses a host of issues associated with retrofitting 

RETs into existing buildings: 

• energy use when weather parameters are changing 

• efficacies of capital investments, such as system refurbishments or new installations 

• predominant influences driving energy usage of a particular building 

• impact of changes in personnel schedules or equipment to the overall energy efficiency of 

the building 

• changes in yearly energy usage trends 

• impact of small-scale retrofitting (i.e. lighting) or building envelope (i.e. insulation) on 

energy usage 

• energy and financial savings associated with implementation of an onsite renewable 

energy technology 

RETScreen Plus answers these issues quantitatively, in terms of energy savings or losses and 

financial cost, to authenticate the validity or potential of energy improvement measures. Energy 

monitoring using RETScreen Plus has previously been demonstrated as a valuable instrument in 

managing energy usage in commercial, residential and industrial applications [8, 9]. 

1.1 Tutorial scope

This tutorial will provide a brief overview of the basic user functions of RETScreen Plus 

software. RETScreen 4, the Excel based clean energy analysis component of the software suite is 

not covered in this tutorial. RETScreen Plus provides the necessary prefeasibility analysis prior 

to implementation of clean energy projects. Following the software walkthrough, the tutorial will 

provide specific procedures outlining the methodologies for setting up building energy 

monitoring and energy performance analyses. 

2. Getting Started

2.1 System requirements

The RETScreen Software Suite is available as a free download from the RETScreen 

International website (www.retscreen.net) with a valid email address. System requirements for 

RETScreen Plus include Microsoft Windows XP or higher, and Microsoft .NET Framework 4 or 

higher (Full Profile version) [3]. 

2
2.2 Start a new project 

To begin, open RETScreen Plus and a blank project will be created by default. The program 

opens on the ‘Start’ tab, located on the top bar. Complete the fields within the ‘Project 

Information’ section, and then press the ‘Select climate data location’ button, to open the climate 

database for site location selection, as shown in Figure 1. 

Figure 1. Start page for project information and site location inputs. 

Within the climate database window, shown in Figure 2, the climate data location can be 

designated via the ‘Map’ or ‘Data’ tabs. From the ‘Map’ tab, drag the project location icon to the 

appropriate geographical location, i.e. the latitude and longitude coordinates where the 

performance analysis is being conducted. The dropdown list provides climate data locations 

arranged by proximity to the project location, which are either ‘Ground’ or ‘NASA’ climate data 

locations. Generally, the NASA station nearest to the project location is selected as the climate 

data location in order to calculate a “normal year” based on 20 years historical data. The daily 

weather parameters generated by the "download NASA satellite data" function are provided 

according to the geographical coordinates of the project location, and are therefore independent 

from the climate data location. Double click on the desired station in the drop down list, so the 

climate data location icon should now be on the chosen location. Submit these changes by 

pressing the green check command at the bottom of the panel. 

3
Figure 2. RETScreen Plus database for selection of project site and climate data locations. 

3. Data Inputs 

Go to the ‘Data’ tab on the top bar (see Fig 1), to open the section to input which information is 

related to the project. There are 3 basic steps to input and manipulate the data: (1) 

Consumption/Production, (2) Factors of influence, and (3) Data Processing. 

4
3.1 Consumption/production inputs 

Data input Step 1 involves retrieval and tabulation of the metered consumption and/or production 

data relevant to the building or system undergoing energy performance analysis or monitoring. 

The available functions for data input include a user defined table and pre-defined tables for 

electricity consumption, fuel consumption, water consumption, electricity production, shown in 

Figure 3. 

Figure 3. Data Step 1 functions for input of various types of energy data. 

The recommended and most efficient method of data input in RETScreen Plus is to import a file 

directly as a new data table. To import metered data, choose ‘Import from file’ under the ‘Userdefined’ drop down tab. Using this method, the file format must be Microsoft Excel or comma 

delimited. The imported data must include a field indicating the Date (Day, Month, Year). Click 

‘Import from file’ under the ‘Data’ tab in the top bar and select the file to be imported. This 

prompts a dialogue box to format the imported data, shown in Figure 4. 

5
Figure 4. Dialogue box to format data imported from a file. 

The drop-down tabs above each column indicate the data type (pre-defined options or userdefined) or mark columns not to include in the import. After confirming these options, a second 

dialogue box will appear with fields to indicate parameters of the data set, shown in Figure 5. 

The dialogue box sorts each field into individual columns, and provides boxes to specify Name, 

Units, and Aggregation method. 

6
 Figure 5. Dialogue box to indicate name, units, and aggregation method for user- defined data 

sets. 

The aggregation method is an important parameter, which indicates how values are grouped 

when merging data (Section 3.3) over a different time period. Refer to Table 1 for aggregation 

methods for various data parameters. 

Aggregation 

model Model method Parameter 

Maximum The maximum value for the 

corresponding period will be 

calculated. 

-Maximum production level 

-Power demand 

Minimum The minimum value for the 

corresponding period will be 

calculated. 

-Minimum occupancy 

Average The average value for the 

corresponding period will be 

calculated. 

-Solar insolation 

-Relative humidity 

-Temperature 

Sum The sum of values for the 

corresponding period will be 

calculated. 

-Energy production 

-Energy consumption 

-Heating and cooling degree days 

None Data will not be aggregated. -Text 

Table 1. Description of aggregation method types and recommended use for each type. 

An example of a ‘User-defined’ table, in this case daily metered photovoltaic electric production, 

is shown in Figure 6. 

7
Figure 6. Electricity production data set imported using the ‘User-defined’ option. 

3.2 Factors of influence 

Data Step 2 involves creating tables for possible energy drivers, or factors that influence the 

consumption/ production energy data. The available options for energy driver input include 

‘Weather’, ‘Production level’ and ‘Occupancy’. 

3.2.1 Weather 

Solar irradiance and meteorological parameters are typically the primary influencing 

factors impacting electricity consumption and production, as well as other types of 

fuel consumption. Accordingly, for most applications weather is the first driver to 

be addressed. Selecting the ‘Weather’ command on the toolbar adds a new data table to the 

project set, and simultaneously adds a new option to the toolbar. 

8
Selecting ‘Download NASA satellite data’ will open a prompt, shown in 

Figure 7, to specify the period of climate data desired. The climate data is 

automatically returned to the weather table as daily measurements and 

individual parameters can be merged to tables containing 

consumption/production data. If the consumption/production data is aggregated weekly or 

monthly, then the weather parameters are automatically adjusted to match that time period. 

Figure 7. Dialogue box for selecting time period of NASA satellite data. 

With the weather data table selected in the side bar, choose the ‘Filter’ option on the toolbar, 

choose ‘Group by’ and aggregate accordingly. With the weather data available, the Degree-days 

option on the toolbar can be used to calculate heating degree-days (HDD) and cooling degreedays (CDD), defined as the difference between a set temperature and the average daily 

temperature (reference temperature 16°C and 18°C, respectively). HDD and CDD are the 

primary drivers for electric usage in most applications, as these are the largest factors driving 

heating, ventilation, and air conditioning (HVAC) usage for management of ambient indoor 

temperature needs. Relative humidity, min/max temperature and windspeed are other 

meteorological factors of influence that can be applied to the analysis, when applicable. These 

data parameters are derived from a NASA data assimilation model that uses satellite and other 

measurements to optimize solutions for global surface and atmospheric profile weather 

information. Daily solar radiation (or insolation) is an important driver when assessing a 

photovoltaic system. The insolation data pulled from the NASA data set is derived from satellite 

observations and given as the insolation incident on a horizontal surface; however, RETScreen 

has a function to calculate the specific system insolation based on the PV system configurationstilt angle (slope), azimuth angle, and solar tracking mode. 

3.2.2 Production level 

In addition to meteorological and solar data, the building’s production level 

(such as manufactured products or energy production from a solar photovoltaic 

panel system) is an influencing driver that can be factored into the analysis. Any 

building that produces quantifiable output metered by a fixed unit of measure can be utilized for 

production level input. Production level data, when available, provides another level of detail to 

explain variance in building energy usage trends. 

9
3.2.3 Occupancy 

Building occupancy is another key driver of energy usage. The addition or 

removal of any persons in small office buildings, or highly fluctuating 

numbers of personnel due to various schedules or seasonal employment, can 

affect the building electric load. Changes in occupancy can account for energy fluctuations 

unexplained by weather conditions or production level. By choosing ‘Occupancy’ a new data 

table is created to account for occupancy over any given time period. The specific unit used to 

measure occupancy can be customized as necessary. 

3.3 Data processing 

Step 3 involves data processing using the options shown in Figure 8, most of which have been 

discussed in previous sections. 

Figure 8. Available functions for data processing. 

The functions include the ‘Degree-days’ command for appending degree-day data to a specified 

data table, and the ‘Daily solar radiation-tilted’ command for converting horizontal insolation to 

insolation on a tilted surface (i.e. solar panel). Other functions of the data processing step include 

the ‘Merge’ command, which enables data columns from one table to be migrated to another 

table, and the ‘filter’ function to group data by specific time scales. 

3.3.1 Degree days 

Heating and cooling degree days are important drivers of energy usage, particularly in buildings 

with heating and air conditioning systems. There are two methods for calculating degree days, 

which both use the air temperatures given by the weather data set. Method 1 uses average air 

temperature; Method 2 uses min/max air temperatures and is more complex. Method 2 provides 

better results for daily metered data; however, for weekly, monthly or yearly data sets, method 1 

is sufficient. The increased complexity of Method 2 calculations translates to increased 

resolution detail; therefore, it is useful when the actual air temperatures oscillate around the 

reference temperature. 

10
Figure 9. Dialogue box to specify degree-day calculation methods and reference temperatures. 

The reference temperatures for calculating heating and cooling degree days vary among climates, 

building types, and construction types; therefore, it is important to ensure the reference 

temperature is appropriate. Initially, set the heating and cooling degree day reference 

temperatures based on best guess, or charts can be found through a web search. Once a 

regression analysis has been performed (see section 4, Analytics) the reference temperatures can 

be adjusted to optimize the R2

 value. To adjust the degree day calculations, select the ‘Degree 

days’ icon. In the dialogue box, shown in Figure 9, choose ‘heating degree-days’, ‘cooling 

degree-days’, or both; select the data table containing weather data; select the desired calculation 

method; and set reference temperatures. 

3.3.2 Solar insolation 

The solar data provided in the weather data set is daily insolation estimated on a horizontal 

surface. The conversion from horizontal surface to tilted surface is complex, but there is a 

function in RETScreen to do the calculations. 

Figure 10. Dialogue box for converting horizontal insolation to insolation on a tilted surface. 

Most solar arrays perform at a tilt, and sometimes have tracking modes. To convert the 

RETScreen insolation data to account for the specific PV system characteristics, click the 

11
‘Daily solar radiation-tilted’ icon and complete the ‘Solar tracking mode’, ‘Slope’ and ‘Azimuth’ 

dialogue box entries, shown in Figure 10. 

3.3.3 Merge data 

Prior to generating graphs in the analytics section, all data utilized in each graph must be 

available within the same data table. For example, all meteorological parameters that act as 

energy consumption drivers must be merged into the energy consumption table. To merge data 

the two sets must have the same time step, with the exception of daily data, which can be merged 

into data sets aggregated weekly, monthly or yearly. To merge data, click ‘Merge’ on the Data 

toolbar, select data array to be merged, and indicate the destination data table, shown in Figure 

11. 

Figure 11. Dialogue box to merge data sets. 

3.3.4 Apply filters 

Any data table can be aggregated on various time scales (daily, weekly, monthly, yearly) 

depending on the type of data and/or the required temporal resolution of the analysis; however, it 

is recommended that filtering is only done after all merging has been performed. For example, if 

the metered energy data was daily electricity, but the goal was to set up weekly monitoring, then 

the original data table could be aggregated (by average, sum, min/max) to produce a new table 

with weekly values. It is recommended to first perform the analytics using the full data set and 

then filter to perform a secondary analysis at a more coarse time scale, if desired. The method of 

aggregation is the method that was specified for each column when the original data was 

imported. To filter data, click the ‘Filter’ drop-down command , click ‘Group by’ and select the 

desired time scale. 

Additionally, the ‘Filter’ command can be used to indicate how to deal with missing data. Click 

the ‘Filter’ drop-down and select ‘Missing data’ . The dialogue box brings up the data table and 

12
highlights any missing cell values. The options are as follows: ‘ignore’, ‘average’, ‘interpolate’, 

‘repeat’, ‘delete’. These options can be applied to ‘All’ or chosen cells can be handled 

individually. 

3.3.5 Column options 

Data columns can be added to an existing data tab if manipulation of the data is needed. This 

feature for data manipulation can be utilized for a number of operations, such as performing unit 

conversions/algebraic calculations on given parameters, generating sub metering if it is not 

available, or adding a hypothetical baseline to predict performance given a fixed set of 

conditions. For example, if a building utilizes electric and steam, but the two consumption 

sources have been imported separately, then a new column could be created for total energy 

usage. With the appropriate data tab highlighted, click the ‘Insert column’ drop down list and 

select ‘User defined’. In the dialog box, enter the new column name, units, aggregation method, 

and then select the ‘Calculator’ option. Data column names are provided in a list, which upon 

selection are incorporated into the formula bar and should be used in conjunction with the 

calculator functions. For this example, the formula would simply be: {Electric usage}+{Steam 

usage}. Click the green check to initiate the command, and RETScreen Plus generates the new 

column and data values. 

4. Analytics 

The first step in data analytics is to identify the factors of influence that drive energy production 

or consumption using a technique known as regression analysis. 

4.1 Establishing a baseline 

To begin data analytics, a baseline must be established by 

generating a regression analysis. The regression analysis 

establishes a relationship between the dependent variable and 

the independent variables through a mathematical function 

and creates a regression line representing the baseline 

predicted value. The fit of the regression line indicates how well the independent variables 

explain the outcome of the dependent variable. Modeling this relationship using multiple factors 

of influence provides a multivariate regression analysis and generates a more complex 

mathematical equation. Generally, there are multiple factors of influence driving energy usage or 

production, so establishing a mathematical relationship with all relevant drivers decreases the 

unexplained variance in the analysis. 

13
Figure 12. Dialogue box to indicate baseline parameters, including data set, variables, and 

methods to use. 

To generate a baseline, from the ‘Analytics’ tab in the top bar, click the ‘Baseline’ drop down 

command and choose ‘regression analysis’. A new window, shown in Figure 12, will prompt to 

choose a name, and choose the data table to be analyzed. The dependent variable (y) is be the 

measured parameter such as energy consumption or production; the independent variables (x) are 

any factors of influence that drive the electricity consumption. 

14
Figure 13. X-Y scatter plot for bivariate regression analysis of electricity production vs. daily 

solar radiation. 

After choosing the data set and variables for analysis, the analyzed data set will be added in the 

‘Analytics’ section sidebar, named ‘Baseline – Regression’ by default. Within this set there are 

multiple tabs available with various information, including ‘Time series’, ‘X-Y scatter’, ‘Line’, 

and ‘Details’. The ‘X-Y scatter’ tab is only available for bivariate regression analyses (linear) as 

shown in Figure 13. 

Generating a baseline with 2 or more independent variables (i.e. multivariate) produces a nonlinear regression line. Examples of a multivariate regression analysis under the ‘Time series’, 

‘Line’, and ‘Details’ tabs are shown in Figure 14, 15 and 16, respectively. 

15
Figure 14. Screenshot of the ‘Time series’ tab for a nonlinear regression analysis of daily PV 

energy production vs. daily solar radiation and daily module temperature. Note that the time 

series for the dependent variable is placed on the left axis and the time series for the independent 

variables stack on the right axis. 

16
Figure 15. Screenshot of the ‘Line’ tab for a nonlinear regression analysis of daily PV energy 

production vs. daily solar radiation and daily module temperature. 

17
Figure 16. Screenshot of the ‘Details’ tab for a nonlinear regression analysis of daily PV energy 

production vs. daily solar radiation and daily module temperature. 

The ‘Details’ tab of the regression analysis includes the information about the mathematical 

equation generated to model the relationship between energy consumption/production and the 

factors of influence. The ‘Details’ tab provides the equation chosen, the regression result 

statistics, and the coefficient results. The ‘Select equation’ function provides a number of 

equations to model the regression; however, it is recommended to use the default equation 

chosen by the program. The coefficient results include the coefficient Value, standard error, and 

Prob(t) for each variable in the equation. The Prob(t) is a 2-tailed p-value that indicates the level 

of confidence that the coefficient is statistically significant, where ݌ > 0.05 indicates 95% 

confidence. In the Figure 16 example, the coefficient ‘a’ is a constant; therefore, the coefficient 

results of ‘a’ do not hold critical meaning to the regression. 

4.2 Comparison functions 

After the regression analysis determines the baseline energy usage or production, there are 

several comparison functions to quantify energy usage. 

18
4.2.1 Cumulative sum 

Choosing the ‘Cumulative sum’ function generates a running sum of the 

difference between the actual energy and the baseline predicted energy over a 

time series, shown in Figure 17. For energy consumption analysis, positive 

deviation from baseline indicates efficiency loss (e.g. energy usage greater than 

predicted energy production) and negative deviation indicates efficiency gain. (e.g. energy 

production greater than energy usage). For energy production analysis (i.e. PV system), positive 

deviation from baseline indicates efficiency gain and negative deviation indicates efficiency loss. 

A linear trend in the CUSUM indicates a period of minimal deviation in the difference between 

actual energy and predicted energy; thus, providing a high correlation between energy usage and 

energy drivers, and a minimal amount of unexplained variance in the analysis. 

Figure 17. Cumulative sum (CUSUM) plot using a baseline generated by the regression analysis 

of PV electricity production vs. daily solar radiation over the time series from February 2012-

March 2014. 

19
4.2.2 Control chart 

The control chart shows the variance (% or actual difference) between predicted 

and actual energy consumption or production. Specific target values other than the 

baseline values can be indicated to verify how close the energy performance comes 

to a desired target. This allows periods of poor or superior performance to be identified. To 

generate the control chart, select the desired regression analysis in the ‘Analytics’ sidebar and 

choose ‘Control chart’ under the ‘Analytics’ tab on the top bar. An example control chart is 

shown in Figure 18. Variance provided as a percentage is often misleading; therefore, viewing 

the control chart as ‘Actual – baseline’ is recommended in order to quantify the variance in unit 

value. 

Figure 18. Example of a control chart using the ‘Actual- Baseline’ method. 

20
4.2.3 Measurement and verification 

The ‘Measurement and verification’ function shows the actual energy 

consumption side by side with the generated baseline. This allows ongoing 

energy monitoring to track real-time net savings from energy efficiency 

measure implementation.

4.3 Analytics tools 

The Analytics tools provide various graphs and the option to incorporate the project into 

RETScreen 4 databases. 

4.3.1 Graphs 

Click the ‘Graph’ drop-down under the ‘Analytics’ tab to create any of 

the following graphs: annual bar graph, monthly bar graph, bar and pie 

charts, time series graph, climate data graph, and moving graph. 

 4.3.2 RETScreen 4 

After using RETScreen Plus to analyze building performance and establish a baseline, 

RETScreen 4 allows users to develop project models and generate a five-step analysis report 

including energy usage, cost, emissions, financial benefits, and risk associated with the project. 

To link a RETScreen Plus project with RETScreen 4 or access databases, click ‘RETScreen 4’ 

under the ‘Analytics’ tab and choose from the drop-down list: Project database, benchmark 

database, or Start RETScreen 4. 

4.4 Analytics options 

The influencing drivers of energy consumption and 

production often change by seasons or over specified 

periods of time. The ‘Define season’ function allows 

different baselines to be established throughout the 

year to coincide with the corresponding driver. For example, cooling degree days are often the 

key energy driver in the summer and likewise, heating degree days are the key energy driver 

during winter. The seasons can be defined such that different regression analysis are performed 

depending on the time of year. To use this feature, select the regression analysis to be adjusted, 

click the ‘Define season’ button, and set the dates which maximize each drivers influence. 

21
5. Reporting 

Perhaps the most important part of an energy monitoring routine is the effective communication 

of results to decision-makers in order to faciliate changes for optimizing performance. Within 

most organizations, the type of information needed for monitoring building energy performance 

varies with level and responsibility of individuals. The Reporting Worksheet within the 

RETScreen Plus provides a selection of high-level standardized reports, as well as detailed 

customizable reports, shown in Figure 19. This customization of reporting enables the intended 

audience to have only relevant information, which in turn facilitates decision support and 

ultimately results in taking appropriate actions toward energy efficiency. 

Figure 19. Cover page of RETScreen Plus report generated for facility operators.

5.1 Create report 

To generate a report, select the ‘Reporting’ tab on the top bar and choose the applicable template. 

The available templates are designated for: Senior decision-makers, Managers, Facility 

operators, or Custom. Upon choosing a template, RETScreen automatically renders all relevant 

figures/data into a report format.

5.2 Edit report 

The specific items included by default in the report are determined by the 

chosen template, however there are options to insert notes, images, maps, 

22
and individually specify the figures and data to include. Choosing the ‘Insert analysis’ command 

allows any of the graphs generated in the project to be included in the report. The pages of the 

report can be sorted in the side panel by dragging the page to the desired location. To change title 

of each page, right-click on that page within the side panel and chose ‘Rename’. Additional 

pages for text, images, or maps can be added to the report by clicking ‘Insert page’ and making 

the selection. 

5.3 Reporting tools and options 

Page customization and viewing can be selected using the ‘Reporting’ tab tools and options, 

shown in Figure 20. 

Figure 20. Reporting toolbar and available options for report customization.

5.3.1 Tools 

Page magnification and page selection options are available for report viewing preferences, 

shown in Figure 20. Additionally, the report can include headers and footers, shown in Figure 

21. To insert headers or footers, select the ‘Header/footer’ icon under the ‘Reporting’ tab. The 

header and footer can each include up to (3) of the following: project name, project location, 

date, prepared for, prepared by, page number, title, custom, or an image. The header and/or 

footer is applied to all pages of the report with the exception of the title page. The page 

orientation option in ‘Tools’ enables the report to be created in Landscape or Portrait orientation. 

23
Figure 21. Dialogue box for customization of report headers and footers.

5.3.2 Options 

Location maps are included in all reports. The default map is Road view, but can also be 

displayed in Satellite view by choosing the ‘Satellite’ icon in the Options toolbar. The map can 

also be zoomed in and out to a show a desired aerial view. 

5.4 Create output 

Once the report is complete, it can be reviewed and distributed 

using the Output features. Prior to printing, select the ‘Print 

preview’ option to see the final report. To save the entire report 

as a file, select the ‘Export’ drop-down, choose ‘Report’, select 

the file location, and the report will be saved in PDF format. 

The figures within the report can be exported independently in PNG format by selecting 

‘Figures’ under the ‘Export’ drop-down. The generated output can also be distributed via email 

by clicking the ‘Send email’ drop-down, selecting the desired file type, and adding the outgoing 

email address(es) to the ‘Email to’ list. This will open the default email client and generate an 

outgoing message draft including the file as an attachment By choosing a RETScreen Plus file, 

the entire project will be exported in (.ret5) format and must be opened within RETScreen Plus 

program. 

24
5.4.1 Updata data 

Once the initial building assessment has been setup, the user can continue on-going monitoring 

by updating data in the existing project. Click the ‘Update data’ option on the Data toolbar, select 

the file to import, and indicate the data table destination. 

Figure 22. Screenshot of the update form function. 

The updated data is automatically incorporated into the previously defined report, which can then 

be distributed in PDF format via email, as shown in Figure 22. 

6. User Help and Support 

6.1 RETScreen Plus information directory

For RETScreen Plus general information, user guides, and more in-depth explanations of 

RETScreen features, click the information directory icon located in the upper right corner of 

the program. The ‘Contents’ tab includes the following sections: 

25
• Centre Overview 

• Software & Data 

• Training Institute 

• Training Material 

• RETScreen Help & Contact Us 

The help pop-up window also features index and general topic searches, as well as favorites for 

quick reference. 

To speak directly to a representative via phone or email, choose ‘RETScreen Help & Contact Us’ 

and then choose ‘Questions and Comments Form’. A RETScreen webpage will open within the 

help window, and contact information is located at the bottom of the page. 

6.2 Templates and case studies

Prior to beginning a new project in RETScreen Plus, take note that within the ‘File’ tab on the 

Menu bar there are several templates and case studies, shown in Figure 23, which serve as guides 

for various applications. 

The templates lay out the formats suggested to determine energy savings for applications with 

variant energy sources/systems. The case studies offer examples of energy performance 

assessment of real projects. 

Figure 23. RETScreen Plus project template and case study directory. 

26
7. General Procedures for RETScreen Performance Plus 

7.1 Pre-feasibility analysis 

Assessing the current energy performance of an existing building enables decision support prior 

to implementation of energy efficicency measures. The following procedure is used: 

7.1.1 Data 

1. Select climate data for building location. 

2. Import or manually enter metered electricity consumption data into new data table. 

3. Download NASA weather data from approximately 20 years prior to the analyzed data set 

through the present. (Historic climate data is needed to calculate consumption/production based 

on a ‘normal year’.) 

4. In the weather data table, add new columns and calculate heating degree days and cooling 

degree days. 

5. Create data table for any additional factors of influence other than weather parameters (i.e. 

occupancy) for the same time series used in Step 2. The factors of influence contributing to 

electricity consumption vary depending on the specific processes at work in the building. 

6. Merge relevant factors of influence into data table containing electricity consumption data. 

7. Export the downloaded NASA weather data to an Excel file and calculate a ‘normal year’ for 

the parameters that are considered significant drivers by the regression analysis results. Average 

the 20 years of historical data to create monthly averages of each weather parameter. These 20 

year monthly averages can be used with the regression equation to calculate normal consumption 

for monthly electric data. 

7.1.2 Analytics 

1. Establish a baseline using a regression analysis over the entire time series of electricity 

consumption data. The electricity consumption data is the independent variable, and factors of 

influence are the dependent variables. 

2. Generate a cumulative sum (CUSUM) graph using the regression analysis from the previous 

step. This provides the running sum of differences between actual and predicted energy 

consumption over the entire time series. It is important to note the scale of the y-axis to ensure 

the apparent magnitude of inflection points are not misleading. A linear trend in the CUSUM 

indicates a period of minimal deviation between the difference of the actual energy consumption 

27
and predicted energy consumption. For example, in Figure 24 the initial CUSUM indicates a 

linear trend from July 2011- July 2012, thus providing a good baseline year for further analysis. 

Figure 24. Cumulative sum (CUSUM) of daily electric consumption for a NASA Langley 

building using the entire data set, time series from February 2011- March 2013, as the 

baseline.10 

3. Establish a second baseline by generating a regression analysis using the new time series, July 

2011- July 2012, identified in Step 2. 

4. Generate a cumulative sum (CUSUM) graph using the regression analysis from Step 3, as 

shown in Figure 25. Investigate points of inflection in order to quantify and identify changes in 

energy usage/production. Energy savings or losses for the building are quantitatively measured 

by comparison with the energy consumption predicted using the baseline year. 

28
Figure 25. Cumulative sum (CUSUM) of daily electric consumption for a NASA Langley 

building using the time series from July 2011- July 2012 as the baseline. This CUSUM 

indicates a net energy savings of approximately 5,500 kWh over the period from August 

2012-March 2013.10

7.1.3 Reporting 

1. Generate a report for facility managers. 

2. Insert any additional figures or text pages to customize the report. 

3. Export the report as a PDF document or RETScreen file. 

4. Update the report as needed. 

7.2 Performance assessment of new renewable energy technology or energy efficiency 

measure 

Retrofitting new technologies such as photovoltaic (PV) systems into existing buildings is an 

effective method of energy and carbon reduction. RETScreen Plus can be used to quantitatively 

assess and provide ongoing tracking of the performance of newly implemented renewable energy 

technologies or energy efficiency measures. The following procedure is used: 

29
7.2.1 Data 

1. Select climate data for building location. 

2. Import or manually enter metered electricity consumption data into new data table. 

3. Download NASA weather data for the same time series used in the previous step. 

4. In the weather data table, add new columns and calculate heating degree days and cooling 

degree days. 

5. Import or manually enter electricity production (i.e. PV production) into new data table. 

6. Merge electricity production factors of influence (i.e. daily insolation, maximum temperature, 

etc.) into the data table created in Step 5. 

7. Merge electricity production data with the electricity consumption data table and create a new 

column for net electricity consumption. The net electricity consumption is the metered electricity 

consumption – electricity production. 

8. Create data table for any additional factors of influence other than weather parameters (i.e. 

occupancy). The factors of influence, or drivers, contributing to electricity consumption vary 

depending on the specific processes at work in the building. 

9. Merge relevant electricity consumption factors of influence into data table containing net 

electricity consumption data. 

7.2.2 Electricity consumption analytics 

1. Establish a baseline using a regression analysis over the entire time series of net electricity 

consumption data. The net electricity consumption data is the independent variable, and all 

factors of influence are the dependent variables. 

2. Generate a cumulative sum (CUSUM) graph using the regression analysis from the previous 

step. 

3. Establish a second baseline using a regression analysis over the time series of net electricity 

consumption PRIOR to implementation of the energy efficiency measure. 

4. Generate a cumulative sum (CUSUM) graph using the regression analysis from Step 3. 

5. Establish a third baseline using a regression analysis over the time series of net electricity 

consumption AFTER implementation of the new energy efficiency measure. 

30
6. Generate a cumulative sum (CUSUM) graph using the regression analysis from Step 5. Energy 

savings is quantitatively measured by comparison of electric usage CUSUM graphs prior to and 

after implementation of a renewable energy technology or energy efficiency measure. 

7. Generate a control chart using the regression analysis from Step 5. The control chart shows the 

variance (% difference) between predicted and actual consumption. 

8. Generate a measurement and verification chart using the regression analysis from Step 3. This 

chart shows the actual energy consumption side by side with the energy consumption predicted 

prior to commissioning the renewable energy technology or energy efficiency measure. 

7.2.3 Electricity production analytics 

1. Establish a baseline using a regression analysis over the entire time series of electricity 

production data, shown in Figure 26. The electricity production data is the independent variable 

and electricity production drivers are the dependent variables. 

Figure 26. Regression analysis of analysis of daily PV energy production vs. daily solar 

insolation for a NASA Langley building.10 The bivariate regression line fit (R2

= 0.87) at 

nearly 45°, indicates excellent correlation between the system output and solar 

observations. 

31
2. Generate a cumulative sum (CUSUM) graph using the regression analysis from the previous 

step. 

3. Generate a measurement and verification chart using the regression analysis from the previous 

step, shown in Figure 27. 

Figure 27. Regression analysis of analysis of daily PV energy production vs. daily solar 

insolation for a NASA Langley building.10 The bivariate regression line fit (R2= 0.87) at 

nearly 45°, indicates excellent correlation between the system output and solar 

observations. 

7.2.4 Reporting 

1. Generate a report for facility managers. 

2. Insert any additional figures or text pages to customize the report 

3. Export the report as a PDF document or RETScreen file. 

4. The report can be updated on an ongoing basis using the ‘Update’ function, and distributed 

using the predefined report. 

8. Conclusion 

A tutorial defining the functions of RETScreen Plus and an outline of the general procedure for 

monitoring and reporting energy performance in buildings has been described. Creating a 

baseline for predicted future energy usage based on energy drivers such as occupancy, 

meteorological and solar parameters indicate if the system is performing above or below 

expectancy. Once monitoring for the building has been set up and properly analyzed through 

multivariate analysis, a report can be crafted specifically for managers, facility coordinators, or 

senior decision makers to facilitate energy efficiency decision support. 

32
9. References 

[1] Betsill, Michele M. "Mitigating climate change in US cities: opportunities and obstacles." 

Local environment 6.4 (2001): 393-406. 

[2] Mitigation, Climate Change. "IPCC special report on renewable energy sources and climate 

change mitigation." (2011).

[3] Omer, Abdeen Mustafa. "Energy, environment and sustainable development." Renewable 

and Sustainable Energy Reviews 12.9 (2008): 2265-2300. 

[4] Diakaki, Christina, et al. "A multi-objective decision model for the improvement of energy 

efficiency in buildings." Energy 35.12 (2010): 5483-5496. 

[5] Lucon, O., D. Urge-Vorsatz, A. Ahmed, H. Akbari, P. Bertoldi, L.F. Cabeza, N. Eyre, A. 

Gadgil, L.D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C. Pyke, and 

M.V. Vilarino, 2014: Buildings. In: Climate Change 2014: Mitigation of Climate Change. 

Contribution of Working Group III in the Fifth Assessment Report of the Intergovernmental 

Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. 

Boschung, A. Nauels, Y. Xia, V. Bex (eds)]. Cambridge University Press, Cambridge, United 

Kingdom and New York, NY, USA. 

[6] RETScreen International webpage: http://www.retscreen.net 

[7] Leng, Gregory J. "RETScreenTM International: a decision support and capacity building tool 

for assessing potential renewable energy projects." INDUSTRY AND ENVIRONMENT-PARIS23.3 (2000): 22-23. 

[8] Thevenard, Didier, Gregory Leng, and Sylvain Martel. "The RETScreen model for assessing 

potential PV projects." Photovoltaic Specialists Conference, 2000. Conference Record of the 

Twenty-Eighth IEEE. IEEE, 2000. 

[9] Lee, Kyoung-Ho, et al. "Preliminary determination of optimal size for renewable energy 

resources in buildings using RETScreen." Energy (2012). 

[10] Ganoe, R., Stackhouse, P., De Young, R. “Energy Assessment of NASA LaRC Buildings 

Using RETScreen® International Performance Software and NASA’s Near Real-Time Solar and 

Meteorological Data”, NASA-TM-2014-218552, November 2014. 

33
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RETScreen® Plus Software Tutorial 

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14. ABSTRACT

The monitoring, targeting and verification of a building’s energy usage depends upon many factors one of the most important 

of which is the local weather and climate conditions to which it’s electrical, heating and air conditioning systems must 

respond. Incorporating renewable energy systems, including solar systems, to supplement energy supplies and increase energy 

efficiency is important to saving costs and reducing emissions. Retrofitting technologies to buildings requires knowledge of 

building performance in its current state and potential future climate state, projection of potential savings with capital 

investment, and then monitoring the performance once the improvements are made. RETScreen Plus is a performance analysis 

module that supplies the needed functions of monitoring current building performance, targeting projected energy efficiency 

improvements and verifying improvements once completed. A tutorial defining the functions of RETScreen Plus and an 

outline of the general procedure for monitoring and reporting energy performance in buildings is described. 

15. SUBJECT TERMS

Building energy; Energy; Energy efficiency; RETScreen

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