Journal Home > Volume 16 , Issue 11

Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified.


menu
Abstract
Full text
Outline
About this article

A review of validation methods for building energy modeling programs

Show Author's information Xin Zhou1Ruoxi Liu1Shuai Tian2,3Xiaohan Shen1Xinyu Yang1Jingjing An4Da Yan5( )
School of Architecture, Southeast University, Nanjing, Jiangsu Province 210096, China
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Key Laboratory of Ecology and Energy-saving Study of Dense Habitat, Ministry of Education, Shanghai 200092, China
School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Building Energy Research Center, School of Architecture, Tsinghua University, Beijing 100084, China

Abstract

Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified.

Keywords: building energy simulation, empirical validation, performance validation, analytical validation, comparative validation

References(118)

Al-Najjar HMT, Mahdi JM (2022). Novel mathematical modeling, performance analysis, and design charts for the typical hybrid photovoltaic/phase-change material (PV/PCM) system. Applied Energy, 315: 119027.

ANSI/ASHRAE. (2014). ASHRAE Guideline 14-2014, Measurement of Energy, Demand, and Water Savings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Azar E, O’Brien W, Carlucci S, et al. (2020). Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications. Energy and Buildings, 224: 110292.

Baldinelli G, Bianchi F (2014). Windows thermal resistance: Infrared thermography aided comparative analysis among finite volumes simulations and experimental methods. Applied Energy, 136: 250–258.

Balduzzi F, Bianchini A, Carnevale EA, et al. (2012). Feasibility analysis of a Darrieus vertical-axis wind turbine installation in the rooftop of a building. Applied Energy, 97: 921–929.

Beausoleil-Morrison I (2000). The adaptive coupling of heat and air flow modelling within dynamic whole-building simulation. PhD Thesis, University of Strathclyde, UK.

Belmans B, Aerts D, Verbeke S, et al. (2019). Set-up and evaluation of a virtual test bed for simulating and comparing single- and mixed-mode ventilation strategies. Building and Environment, 151: 97–111.

Ben-Nakhi AE, Aasem EO (2003). Development and integration of a user friendly validation module within whole building dynamic simulation. Energy Conversion and Management, 44: 53–64.

Bertagnolio S, Bernier M, Kummert M (2012). Comparing vertical ground heat exchanger models. Journal of Building Performance Simulation, 5: 369–383.

Bizoňová S, Katunský D, Bagoňa M (2020). Comparative analysis of selected glass systems by dynamic simulation using measured real environmental conditions. In: Proceedings of the 12th Nordic Symposium on Building Physics (NSB 2020).

Cattarin G, Pagliano L, Causone F, et al. (2018). Empirical and comparative validation of an original model to simulate the thermal behaviour of outdoor test cells. Energy and Buildings, 158: 1711–1723.

Chen X, Yang H, Sun K (2017). Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings. Applied Energy, 194: 422–439.

Chen Y, Hong T, Luo X (2018). An agent-based stochastic occupancy simulator. Building Simulation, 11: 37–49.

Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.

De Jaeger I, Lago J, Saelens D (2021). A probabilistic building characterization method for district energy simulations. Energy and Buildings, 230: 110566.

DOE (2019). Building Performance Database. Available at https://www.energy.gov/eere/buildings/building-performance-database-bpd
EIA (2018). Commercial Buildings Energy Consumption Survey (CBECS). US Energy Information Administration. Available at https://www.eia.gov/consumption/commercial/
EIA (2020). Residential Energy Consumption Survey (RECS). US Energy Information Administration. Available at https://www.eia.gov/consumption/residential/data/2020/

Evola G, Costanzo V, Magrì C, et al. (2020). A novel comprehensive workflow for modelling outdoor thermal comfort and energy demand in urban canyons: results and critical issues. Energy and Buildings, 216: 109946.

Fantucci S, Goia F, Perino M, et al. (2019). Sinusoidal response measurement procedure for the thermal performance assessment of PCM by means of dynamic heat flow meter apparatus. Energy and Buildings, 183: 297–310.

Ferguson A, Kelly N, Weber A, et al. (2009). Modelling residential-scale combustion-based cogeneration in building simulation. Journal of Building Performance Simulation, 2: 1–14.

Gerlich V, Sulovská K, Zálešák M (2013). COMSOL Multiphysics validation as simulation software for heat transfer calculation in buildings: Building simulation software validation. Measurement, 46: 2003–2012.

Gonçalves JE, van Hooff T, Saelens D (2021). Simulating building integrated photovoltaic facades: comparison to experimental data and evaluation of modelling complexity. Applied Energy, 281: 116032.

Guichard S, Miranville F, Bigot D, et al. (2016). Empirical validation of a thermal model of a complex roof including phase change materials. Energies, 9: 9.

Guo S, Yan D, Gui C (2020). The typical hot year and typical cold year for modeling extreme events impacts on indoor environment: A generation method and case study. Building Simulation, 13: 543–558.

Haddad KH (2004). An air-conditioning model validation and implementation into a building energy analysis software. ASHRAE Transactions, 110(2): 46–54.

Haldi F, Robinson D (2009). Interactions with window openings by office occupants. Building and Environment, 44: 2378–2395.

Haldi F, Robinson D (2011). The impact of occupants’ behaviour on building energy demand. Journal of Building Performance Simulation, 4: 323–338.

Han JM, Choi ES, Malkawi A (2022). CoolVox: advanced 3D convolutional neural network models for predicting solar radiation on building facades. Building Simulation, 15: 755–768.

Helton JC, Johnson JD, Sallaberry CJ, et al. (2006). Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering & System Safety, 91: 1175–1209.

Henninger RH, Witte MJ, Crawley DB (2004). Analytical and comparative testing of energyplus using IEA HVAC BESTEST E100-#200 test suite. Energy and Buildings, 36: 855–863.

Henninger RH, Witte MJ (2010). EnergyPlus Testing with Building Thermal Envelope and Fabric Load Tests from ANSI/ASHRAE Standard 140-2007.
Henninger HR, Witte MJ (2013). EnergyPlus Testing with HVAC Equipment Component Tests. U.S. Department of Energy.

Herkel S, Knapp U, Pfafferott J (2008). Towards a model of user behaviour regarding the manual control of windows in office buildings. Building and Environment, 43: 588–600.

Hittle DC (1977). The building loads analysis and system thermodynamics (BLAST) program. Volume Ⅰ. User’s manual. Final report. Army Construction Engineering Research Lab, USA.
Hong T, D’Oca S, Turner WJN, et al. (2015a). An ontology to represent energy-related occupant behavior in buildings. Part Ⅰ: Introduction to the DNAs framework. Building and Environment, 92: 764–777.
DOI

Hong T, D’Oca S, Taylor-Lange SC, et al. (2015b). An ontology to represent energy-related occupant behavior in buildings. Part Ⅱ: Implementation of the DNAS framework using an XML schema. Building and Environment, 94: 196–205.

Hong T, Chen Y, Luo X, et al. (2020). Ten questions on urban building energy modeling. Building and Environment, 168: 106508.

Huang J, Bourassa N, Buhl F, et al. (2006). Using EnergyPlus for California Title-24 compliance calculations. Lawrence Berkeley National Laboratory, USA.
IEA (2019). Global Status Report for Buildings and Construction 2019. International Energy Agency. Available at https://www.iea.org/reports/global-status-report-for-buildings-and-construction-2019

Ismail KAR, Castro JNC (1997). PCM thermal insulation in buildings. International Journal of Energy Research, 21: 1281–1296.

Jia M, Srinivasan RS, Ries R, et al. (2019). A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings. Energy and Buildings, 199: 352–367.

Jin Y, Yan D, Kang X, et al. (2021). Forecasting building occupancy: A temporal-sequential analysis and machine learning integrated approach. Energy and Buildings, 252: 111362.

Jin X, Zhang C, Xiao F, et al. (2023). A review and reflection on open datasets of city-level building energy use and their applications. Energy and Buildings, 285: 112911.

Judkoff RD (1988). Validation of building energy analysis simulation programs at the solar energy research institute. Energy and Buildings, 10: 221–239.

Judkoff R, Wortman D, O’Doherty B, et al. (2008). Methodology for validating building energy analysis simulations. Technical Report NREL/TP-550-42059. National Renewable Energy Lab. (NREL), USA.
DOI

Kamel E (2022). A systematic literature review of physics-based urban building energy modeling (UBEM) tools, data sources, and challenges for energy conservation. Energies, 15: 8649.

Le H-T, Knabe G (2000). HVAC BESTEST Modeler Report TRNSYS14.2 (A Transient System Simulation Program), Technische Univeristät Dresden, Germany.

Li J, Zhang C, Zhao Y, et al. (2022a). Federated learning-based short-term building energy consumption prediction method for solving the data silos problem. Building Simulation, 15: 1145–1159.

Li X, Xie W, Xu L, et al. (2022b). Holistic life-cycle accounting of carbon emissions of prefabricated buildings using LCA and BIM. Energy and Buildings, 266: 112136.

Lienhard JH V, Lienhard JH IV (2013). A Heat Transfer Textbook, the 4th edn. Dover Publications.

Liu B, Duan S, Cai T (2011). Photovoltaic DC-building-module-based BIPV system—concept and design considerations. IEEE Transactions on Power Electronics, 26: 1418–1429.

Liu Y, Tian W, Zhou X (2021). Energy and carbon performance of urban buildings using metamodeling variable importance techniques. Building Simulation, 14: 535–547.

Lomas K, Epple H, Martin C, et al. (1994). Empirical validation of thermal building simulation programs using test room data: Volume Ⅰ: Final report. IEA Annex 21/Task 12 Project Final Report.

Lomas KJ, Eppel H, Martin CJ, et al. (1997). Empirical validation of building energy simulation programs. Energy and Buildings, 26: 253–275.

Loutzenhiser P, Manz H, Strachan P, et al. (2006). An empirical validation of modeling solar gains through a glazing unit using building energy simulation programs. HVAC&R Research, 12: 1097–1116.

Loutzenhiser PG, Manz H, Moosberger S, et al. (2009). An empirical validation of window solar gain models and the associated interactions. International Journal of Thermal Sciences, 48: 85–95.

Luo X, Lam KP, Chen Y, et al. (2017). Performance evaluation of an agent-based occupancy simulation model. Building and Environment, 115: 42–53.

Luo X, Hong T, Tang Y (2020). Modeling thermal interactions between buildings in an urban context. Energies, 13: 2382.

MacDonald IA (2002). Quantifying the effects of uncertainty in building simulation. PhD Thesis, University of Strathclyde, UK.

Marinosci C, Strachan PA, Semprini G, et al (2011). Empirical validation and modelling of a naturally ventilated rainscreen façade building. Energy and Buildings, 43: 853–863.

Meldem R, Winkelmann F (1998). Comparison of DOE-2 with temperature measurements in the Pala test houses. Energy and Buildings, 27: 69–81.

Mousavi S, Rismanchi B, Brey S, et al. (2023). Development and validation of a transient simulation model of a full-scale PCM embedded radiant chilled ceiling. Building Simulation, 16: 813–829.

Nageler P, Schweiger G, Pichler M, et al. (2018). Validation of dynamic building energy simulation tools based on a real test-box with thermally activated building systems (TABS). Energy and Buildings, 168: 42–55.

Neymark J, Judkoff R, Knabe G, et al. (2002). Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy and Buildings, 34: 917–931.

Neymark J, Judkoff R, Beausoleil-Morrison I, et al. (2008). International energy agency building energy simulation test and diagnostic method (IEA BESTEST) In-depth diagnostic cases for ground coupled heat transfer related to slab-on-grade construction. Technical Report NREL/TP-550-43388. National Renewable Energy Lab. (NREL), USA.
DOI

O’Donovan A, O’Sullivan PD, Murphy MD (2019). Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches. Applied Energy, 250: 991–1010.

Oraiopoulos A, Howard B (2022). On the accuracy of urban building energy modelling. Renewable and Sustainable Energy Reviews, 158: 111976.

Parkinson T, Tartarini F, Földváry Ličina V, et al. (2022). ASHRAE global database of thermal comfort field measurements [Dataset]. Available at https://open.library.ubc.ca/collections/researchdata/items/1.0397701

Peng Y, Rysanek A, Nagy Z, et al. (2018). Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied Energy, 211: 1343–1358.

Piratheepan M, Anderson TN (2017). Performance of a building integrated photovoltaic/thermal concentrator for facade applications. Solar Energy, 153: 562–573.

Pourghorban A, Kari BM (2019). Evaluation of reflective insulation systems in wall application by guarded hot box apparatus, and comparative investigation with ASHRAE and ISO 15099. Construction and Building Materials, 207: 84–97.

Purdy J, Beausoleil-Morrison I (2003). Building energy simulation test and diagnostic method for heating, ventilation, and air-conditioning equipment models (HVAC BESTEST): Fuel-fired furnace test cases. A Report of Task 22, Subtask C, Building Energy Analysis Tools, Project C.2 Comparative Evaluation

Qiao Q, Yunusa-Kaltungo A (2023). A hybrid agent-based machine learning method for human-centred energy consumption prediction. Energy and Buildings, 283: 112797.

Reinhart C, Dogan T, Jakubiec A, et al. (2013). Umi—An urban simulation environment for building energy use, daylighting and walkability. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.
DOI

Remmen P, Lauster M, Mans M, et al. (2018). TEASER: an open tool for urban energy modelling of building stocks. Journal of Building Performance Simulation, 11: 84–98.

Roberts BM, Allinson D, Diamond S, et al. (2019). Predictions of summertime overheating: Comparison of dynamic thermal models and measurements in synthetically occupied test houses. Building Services Engineering Research & Technology, 40: 512–552.

Safa AA, Fung AS, Kumar R (2015). Comparative thermal performances of a ground source heat pump and a variable capacity air source heat pump systems for sustainable houses. Applied Thermal Engineering, 81: 279–287.

Socolow RH (1978). The twin rivers program on energy conservation in housing: Highlights and conclusions. Energy and Buildings, 1: 207–242.

Sokol J, Cerezo Davila C, Reinhart CF (2017). Validation of a Bayesian-based method for defining residential archetypes in urban building energy models. Energy and Buildings, 134: 11–24.

Soto A, Martínez P, Soto VM, et al. (2021). Analysis of the performance of a passive downdraught evaporative cooling system driven by solar chimneys in a residential building by using an experimentally validated TRNSYS model. Energies, 14: 3486.

Strachan PA, Kokogiannakis G, MacDonald IA (2008). History and development of validation with the ESP-r simulation program. Building and Environment, 43: 601–609.

Susman G, Dehouche Z, Cheechern T, et al. (2011). Tests of prototype PCM ‘sails’ for office cooling. Applied Thermal Engineering, 31: 717–726.

Tabares-Velasco PC, Srebric J (2012). A heat transfer model for assessment of plant based roofing systems in summer conditions. Building and Environment, 49: 310–323.

Tahmasebi F, O’Brien W, Wang Y, et al. (2019). Inter-occupant diversity in occupant behaviour models: exploring potential benefits for predicting light switch-on actions. In: Proceedings of the 16th International IBPSA Building Simulation Conference, Rome, Italy.

Tian W, Song J, Li Z, et al. (2014). Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis. Applied Energy, 135: 320–328.

Tian W, Heo Y, de Wilde P, et al. (2018). A review of uncertainty analysis in building energy assessment. Renewable and Sustainable Energy Reviews, 93: 285–301.

Turrin M, von Buelow P, Stouffs R (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics, 25: 656–675.

Verbruggen S, Delghust M, Laverge J, et al. (2021). Habitual window opening behaviour in residential buildings. Energy and Buildings, 252: 111454.

Waddell C, Kaserekar S (2010). Solar gain and cooling load comparison using energy modeling software. In: Proceedings of the 4th National Conference of IBPSA-USA, New York City, USA.

Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.

Wang M, Peng J, Li N, et al. (2017). Comparison of energy performance between PV double skin facades and PV insulating glass units. Applied Energy, 194: 148–160.

Wang W, Chen J, Hong T, et al. (2018). Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology. Building and Environment, 138: 160–170.

Wang H, Zhang H, Hou K, et al. (2021a). Carbon emissions factor evaluation for assembled building during prefabricated component transportation phase. Energy Exploration & Exploitation, 39: 385–408.

Wang X, Jin X, Yin Y, et al. (2021b). A transient heat and moisture transfer model for building materials based on phase change criterion under isothermal and non-isothermal conditions. Energy, 224: 120112.

Wang C, Ferrando M, Causone F, et al. (2022a). Data acquisition for urban building energy modeling: A review. Building and Environment, 217: 109056.

Wang C, Ferrando M, Causone F, et al. (2022b). An innovative method to predict the thermal parameters of construction assemblies for urban building energy models. Building and Environment, 224: 109541.

Wijesuriya S, Tabares-Velasco PC, Biswas K, et al. (2020). Empirical validation and comparison of PCM modeling algorithms commonly used in building energy and hygrothermal software. Building and Environment, 173: 106750.

Wu J, Tremeac B, Terrier MF, et al. (2016). Experimental investigation of the dynamic behavior of a large-scale refrigeration - PCM energy storage system. Validation of a complete model. Energy, 116: 32–42.

Wu X, Peng B, Lin B (2017). A dynamic life cycle carbon emission assessment on green and non-green buildings in China. Energy and Buildings, 149: 272–281.

Xiong Y, Chen H (2022). Impacts of uneven surface heating of an ideal street canyon on airflows and indoor ventilation: numerical study using OpenFOAM coupled with EnergyPlus. Building Simulation, 15: 265–280.

Xu J, Kang X, Chen Z, et al. (2021). Clustering-based probability distribution model for monthly residential building electricity consumption analysis. Building Simulation, 14: 149–164.

Yan D, Xia J, Tang W, et al. (2008). DeST—An integrated building simulation toolkit Part Ⅰ: Fundamentals. Building Simulation, 1: 95–110.

Yan D, O’Brien W, Hong T, et al. (2015). Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 107: 264–278.

Yan D, Feng X, Jin Y, et al. (2018). The evaluation of stochastic occupant behavior models from an application-oriented perspective: Using the lighting behavior model as a case study. Energy and Buildings, 176: 151–162.

Yan L, Liu M (2020). A simplified prediction model for energy use of air conditioner in residential buildings based on monitoring data from the cloud platform. Sustainable Cities and Society, 60: 102194.

Yan D, Zhou X, An J, et al. (2022). DeST 3.0: a new-generation building performance simulation platform. Building Simulation, 15: 1849–1868.

Yi YK, Feng N (2013). Dynamic integration between building energy simulation (BES) and computational fluid dynamics (CFD) simulation for building exterior surface. Building Simulation, 6: 297–308.

Yim S, Ng S, Hossain M, et al. (2018). Comprehensive evaluation of carbon emissions for the development of high-rise residential building. Buildings, 8: 147.

York D, Cappiello C (1981). DOE-2 engineers manual (Version 2.1A). Lawrence Berkeley Lab.; Los Alamos National Lab, USA.

Zhang X, Xia J, Jiang Z, et al. (2008). DeST—An integrated building simulation toolkit Part Ⅱ: Applications. Building Simulation, 1: 193–209.

Zhang L, Deng Z, Liang L, et al. (2019). Thermal behavior of a vertical green facade and its impact on the indoor and outdoor thermal environment. Energy and Buildings, 204: 109502.

Zhou X, Hong T, Yan D (2014). Comparison of HVAC system modeling in EnergyPlus, DeST and DOE-2.1E. Building Simulation, 7: 21–33.

Zhou X, Liu T, Yan D, et al. (2021a). An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces. Building Simulation, 14: 301–315.

Zhou X, Ren J, An J, et al. (2021b). Predicting open-plan office window operating behavior using the random forest algorithm. Journal of Building Engineering, 42: 102514.

Zhou X, Tian S, An J, et al. (2021c). Comparison of different machine learning algorithms for predicting air-conditioning operating behavior in open-plan offices. Energy and Buildings, 251: 111347.

Zhu D, Hong T, Yan D, et al. (2013). A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1E. Building Simulation, 6: 323–335.

Zhu S, Du S, Li Y, et al. (2021). A 3D spatiotemporal morphological database for urban green infrastructure and its applications. Urban Forestry & Urban Greening, 58: 126935.

Publication history
Copyright
Acknowledgements

Publication history

Received: 04 April 2023
Revised: 11 May 2023
Accepted: 25 May 2023
Published: 03 October 2023
Issue date: November 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (52078117), the National Natural Science Foundation of China (52108068), the National Natural Science Foundation of China (52225801), and the “Zhishan” Scholars Programs of Southeast University (2242021R41145).

Return