EnergyPlus-Towards the Selection of Right Simulation Tool for Building Energy and Power Systems Research
University of Calgary, Canada
Academic Editor: Mohammad Jafari
Received: February 05, 2021 | Accepted: May 19, 2021 | Published: July 19, 2021
Journal of Energy and Power Technology 2021, Volume 3, Issue 3, doi:10.21926/jept.2103034
Recommended citation: Ali Muslim S. EnergyPlus-Towards the Selection of Right Simulation Tool for Building Energy and Power Systems Research. Journal of Energy and Power Technology 2021;3(3):14; doi:10.21926/jept.2103034.
© 2021 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.
Energy simulation tools facilitate investigation of new ideas and analysis for determining the impact of energy conservation measures on buildings, processes and energy systems. This feature of simulation tools allows building energy analysis from the earliest conceptual design stages, doing comparative studies on building form, build geometry, system types, lighting etc. without building different design options and doing experimental observations .
Energy simulations are conducted using software tools that calculate the thermal loads and energy use of residential, commercial and industrial buildings [2,3,4]. Using energy simulation makes it is possible to predict the output variables of a specified model with known structure and parameters when subject to specified input variables [1,4]. Hence, energy simulation provides an efficient method of predicting the future energy use of buildings . With increase in the computational power, powerful building energy models are available for use on the personal computers and are used by a diverse group of users ranging from architects, engineers to policy makers [2,5]. As per ASHRAE , energy simulation tools have the following three main components:
Input Variables: Variables that interact with the HVAC and other buildings systems (e.g., internal gain, thermostat set points and climate).
System Parameters: Parameters that define physical description of the system e.g., properties of the heating and cooling medium, insulation properties of the building envelope, thermal and mechanical efficiencies of heating and cooling plants, pumps, fans etc.
Output: Output consists of the response of the system parameters as they interact with the input variables.
2. Choosing Right Simulation Tool
The most important step in considering a simulation tool is matching it capabilities with project requirements [1,6]. ASHRAE  and ASHRAE 140  have listed factors, which are critical, in choosing a simulation tool, some of which are below:
Sufficiently accurate to predict energy use
Simulation tool should be compliant with ASHRAE Standard 140: Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs
Simulation tool should be sensitive to the design option being considered
Simulation tool should allow analyses of all options under consideration
Simulation tool should balance the speed of analysis and the reproducibility of results
Simulation tool should be able to perform envelope modelling
Simulation tool should be able to model internal heat gains like peak heat rate, time variation of heat rates, latent vs. sensible heat fraction, radiant/convection split, and fraction of heat gain to space
Simulation tool should be able to model occupant behavior
Simulation tool should be able to model building heating, cooling and energy transfer systems
Meeting with all the above requirements and compliant with the ASHRAE Standard 140 , the following simulation tools are currently approved by the Canada Green Building Council (CaGBC) to perform energy simulations for Leadership in Energy and Environmental Design (LEED) certification . These tools are widely used due to their high-resolution simulation capabilities, wide range of users, availability of reference literature and having been extensively validated:
IES Virtual Environment
Hourly Analysis Program (HAP)
The summary of simulation tool along with their strengths and weaknesses is provided in Table 1.
3. Comparative Analysis of Energy Simulation Tools
Though there are several studies on the performance or validation of individual energy simulation software [2,5,9,10,11,12], to date the most comprehensive comparison between different energy performance simulation programs is Crawley et al. .
Crawley et al.  has provided an exhaustive comparison on dozens of individual features of twenty major building energy simulation tools along the following criteria:
General modeling features
Building electrical systems
Day lighting and solar
Ventilation and multi-zone airflow
Renewable energy systems
HVAC systems, HVAC equipment
Climate data availability
User interface, links to other programs and availability
Crawley et al.  conclude the overwhelming superiority in terms of capabilities and features in favor of EnergyPlus. Similarly, Lasley , Sousa  and Sola et al.  have provided an exhaustive comparison between different modeling features of several simulation tools. Zhou et al.  from Lawrence Berkeley National Laboratory concluded that EnergyPlus is a powerful simulation program with the most complete component models and the most adjustable parameters among other simulation programs.
Wide scale adoption by the U.S. Department of Energy
Open-source, programmable in C++
Cross-platform builds (able to work on Windows and Mac)
Enhanced urban context simulation
Automated ASHRAE Standard 140 testing
Most widely used engine for published research
Preferred platform for American Institute of Architects (AIA) 2030 reporting
Integrated, simultaneous solution of thermal zone conditions and HVAC system response that does not assume that the HVAC system can meet zone loads and can simulate un-conditioned and under-conditioned spaces
Heat balance-based solution of radiant and convective effects that produce surface temperatures thermal comfort and condensation calculations
Sub-hourly, user-definable time steps for interaction between thermal zones and the environment; with automatically varied time steps for interactions between thermal zones and HVAC systems. These allow EnergyPlus to model systems with fast dynamics while also trading off simulation speed for precision
Combined heat and mass transfer model that accounts for air movement between zones
Advanced fenestration models including controllable window blinds, electrochromic glazing, and layer-by-layer heat balances that calculate solar energy absorbed by window panes
Illuminance and glare calculations for reporting visual comfort and driving lighting controls
Component-based HVAC that supports both standard and novel system configurations
Advanced HVAC and lighting control strategies and an extensible runtime scripting system for user-defined control
Functional Mockup Interface import and export for co-simulation with other engines
Standard summary and detailed output reports as well as user definable reports with selectable time-resolution from annual to sub-hourly, all with energy source multipliers
Seamless integration with CAD and SketchUp for easy geometrical modeling and model importing
Compatibility with Building Information Modeling (BIM) applications like OpenStudio.
4. Simulation Tool Ranking in Research Publications
Alwisy et al.  has summarized the impact of each simulation tool in terms of the number of publications reflecting the importance of acceptance metrics of evidence-based research conducted in the energy simulation. The summary of simulation tool rankings is provided in Table 2. As evidenced by the Alwisy et al. , majority of the research publications in building systems, mechanical and electrical design requirements, weather simulations and renewable power systems analysis is EnergyPlus, followed by TRNSYS.
5. Contextualizing EnergyPus for District Scale Energy Analysis
As per Sola et al. , for district scale building stock, ideal strategy is to use integrated or co-simulation tools. This is attributed to the fact that most energy simulation tools are limited to specific purposes or sub-parts of district scale analyses . In addition, with the integration or coupling of different simulation tools, researchers can link outputs of one tool to the inputs of other tools to overcome the limitations of individual simulation tool. Sola et al.  have analyzed fifteen capabilities of district scale energy modelling tools, ranging from individual building characterization, building stock modelling to on-site energy generation, optimization analysis and time of modelling required for these energy modelling tools. Sola et al.  has identified numerous co-simulation computational modules using a combination of EnergyPlus with Matlab, Simulink, Radiance, Excel VBA, City-Sim, Dymola, Modelica etc. using different model exchange interfaces, e.g., Ptolemy II, Functional Mock-up Interface and OpenStudio etc. to simulate complex, multivariable models typical of the neighborhood scale built structure.
Zhao et al.  have explored the integration of EnergyPlus with Matlab-Simulink for predictive control of the building. Torre and Yousif  have investigated natural ventilation using a co-simulation using DesignBuilder and EnergyPlus. Wang et al.  have explored synchronization and interaction between EnergyPlus and No-MASS for complex and multi-domain simulation requirements of district or neighborhood energy systems. Nouidui et al.  have described the link between EnergyPlus, Modelica and Functional Mock-up Unit (FMU) to simulate a complex numerical model of shading device on window for a room model. Wang et al.  have studied the optimization of the occupancy-based thermostatic schedule using a co-simulation of EnergPlus and Java-based control sequence. Building Controls Virtual Test Bed (BCVTB) has been used to integrate Matlab, Modelica and Dymola with EnergyPlus to overcome limitations of EnergyPlus for complex neighborhood level building system modelling by Wetter . Few other examples are Michalak –EnergyPlus integrated with Matlab-Simulink, Lassandro and Di Turi –envelope optimization using EnergyPlus co-simulated with DesignBuilder and ENVI-met, Booten and Tabares-Velasco –optimization of residential cooling strategies by co-simulating EnergyPlus with Building Energy Optimization (BEopt) and Li –optimization of district heating networks by co-simulating EnergyPlus and Simulink. Singer at al. –EnergyPlus co-simulated with FMU and Java Federate to improve residential energy savings and Christina and Dounis –EnergyPlus co-simulated with Simulink (for HVAC controls) and TRNSYS (for HVAC plants) using BCVTB interface, Willy et al. – EnergyPlus co-simulated with Matlab-Simulink to improve campus-wide integrated energy modelling, Willy et al. –MLE + used to co-simulated EnergyPlus with Simulink for energy-efficient building controls.
Hence, it has been demonstrated by several studies that EnergyPlus has a lot of potential to be integrated to different energy simulation tools for expanding its capabilities to act as a primary simulation platform for district level energy analysis.
This article summarizes the findings of extensive literature review and analysis and concludes that due to its prominent use in validated research publications, advanced features, flexibility to modify the source code and compliance with validation protocols like ASHRAE 140 , EnergyPlus is the most widely used and validated simulation tool for building energy and power systems research. A novel area of the integration of EnergyPlus to amplify its strength for multi-domain, co-simulation platform has been analyzed. It has been demonstrated that in order to overcome technical limitations faced for district, city or municipal level energy simulations EnergyPlus offers huge flexibility for integration with other simulation tools like Matlab, Simulink, Radiance, Excel VBA, City-Sim, Dymola, Modelica, Open Studio, DesignBuilder etc.
Hence, this article concludes that EnergyPlus is preferred choice for energy and power system simulation tool for building systems and is used extensively in energy research and consulting design owing to its powerful capabilities, multi-platform integration and extensive validation of its simulation algorithm.
The author did all the research work of this study.
The author has declared that no competing interests exist.
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