Urban building energy modeling – A review of a nascent field
Open accessAbstract
Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.
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UBEMs have yet to be adopted at scale but are expected to become fundamental for localized action by city planners.
“While UBEMs have yet to be adopted at scale, they are expected to become fundamental for enabling localized action by city planners [677].”
From Tackling climate change with machine learning (2023) · cites this paper as [677]
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Cites
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
Swan, L. G., & Ugursal, V. I. (2009) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.09.033 · Cited by 1,954 · see citing works
A review of bottom-up building stock models for energy consumption in the residential sector
Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A. J., Stevanovic, Z. M., & Djurovic-Petrovic, M. (2010) · Building and Environment · doi:10.1016/j.buildenv.2010.01.021 · Cited by 943 · see citing works
A review of modelling approaches and tools for the simulation of district-scale energy systems
Allegrini, J., Orehounig, K., Mavromatidis, G., Ruesch, F., Dorer, V., & Evins, R. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.07.123 · Cited by 523 · see citing works
Ballarini, I., Corgnati, S. P., & Corrado, V. (2014) · Energy Policy · doi:10.1016/j.enpol.2014.01.027 · Cited by 522 · see citing works
OGC City Geography Markup Language (CityGML) Encoding Standard
(2012) · doi:10.62973/12-019 · Cited by 520 · see citing works
Spatial distribution of urban building energy consumption by end use
Howard, B., Parshall, L., Thompson, J. W., Hammer, S. A., Dickinson, J., & Modi, V. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.10.061 · Cited by 429 · see citing works
Jentsch, M. F., James, P. A. B., Bourikas, L., & Bahaj, A. S. (2013) · Renewable Energy · doi:10.1016/j.renene.2012.12.049 · Cited by 355 · see citing works
Fonseca, J. A., & Schlueter, A. (2015) · Applied Energy · doi:10.1016/j.apenergy.2014.12.068 · Cited by 348 · see citing works
CFD simulation and validation of urban microclimate: A case study for Bergpolder Zuid, Rotterdam
Toparlar, Y., Blocken, B. J. E., Vos, P., van Heijst, G. J. F., Janssen, W. D., van Hooff, T., Montazeri, H., & Timmermans, H. J. P. (2014) · Building and Environment · doi:10.1016/j.buildenv.2014.08.004 · Cited by 343 · see citing works
Heiple, S., & Sailor, D. J. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2008.01.005 · Cited by 329 · see citing works
CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning
Robinson, D., Haldi, F., Leroux, P., Perez, D., Rasheed, A., & Wilke, U. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.26868/25222708.2009.1083-1090 · Cited by 329 · see citing works
Building age as an indicator for energy consumption
Aksözen, M. H., Daniel, M., Hassler, U., & Kohler, N. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.10.074 · Cited by 288 · see citing works
Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK
Mata, É., Kalagasidis, A. S., & Johnsson, F. (2014) · Building and Environment · doi:10.1016/j.buildenv.2014.06.013 · Cited by 278 · see citing works
A supporting method for defining energy strategies in the building sector at urban scale
Caputo, P., Costa, G., & Ferrari, S. (2013) · Energy Policy · doi:10.1016/j.enpol.2012.12.006 · Cited by 237 · see citing works
Dall’O’, G., Galante, A., & Torri, M. D. C. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.01.034 · Cited by 236 · see citing works
Mastrucci, A., Baume, O., Stazi, F., & Leopold, U. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.02.032 · Cited by 226 · see citing works
Dascalaki, E. G., Droutsa, K. G., Balaras, C. A., et al. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.09.002 · Cited by 216 · see citing works
A bottom-up stochastic model to predict building occupants' time-dependent activities
Wilke, U., Haldi, F., Scartezzini, J., et al. (2012) · Building and Environment · doi:10.1016/j.buildenv.2012.10.021 · Cited by 193 · see citing works
Bueno, B., Norford, L. K., Pigeon, G., & Britter, R. E. (2012) · Building and Environment · doi:10.1016/j.buildenv.2012.01.023 · Cited by 191 · see citing works
Residential end-use energy simulation at city scale
Shimoda, Y., Fujii, T., Morikawa, T., & Mizuno, M. (2004) · Building and Environment · doi:10.1016/j.buildenv.2004.01.020 · Cited by 182 · see citing works
History and development of validation with the ESP-r simulation program
Strachan, P. A., Kokogiannakis, G., & Macdonald, I. (2006) · Building and Environment · doi:10.1016/j.buildenv.2006.06.025 · Cited by 177 · see citing works
SUNtool – A new modelling paradigm for simulating and optimising urban sustainability
Robinson, D., Campbell, N., Gaiser, W., et al. (2007) · Solar Energy · doi:10.1016/j.solener.2007.06.002 · Cited by 171 · see citing works
Filogamo, L., Peri, G., Rizzo, G., et al. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.04.002 · Cited by 158 · see citing works
Developing archetypes for domestic dwellings—An Irish case study
Famuyibo, A. A., Duffy, A. P., & Strachan, P. A. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.03.033 · Cited by 149 · see citing works
A typological classification of the Greek residential building stock
Theodoridou, I., Papadopoulos, A. M., & Hegger, M. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.06.036 · Cited by 146 · see citing works
Fracastoro, G. V., & Serraino, M. (2010) · Energy and Buildings · doi:10.1016/j.enbuild.2010.12.004 · Cited by 143 · see citing works
Umi – an urban simulation environment for building energy use, daylighting and walkability
Jakubiec, J. A. (2013) · Cited by 141 · see citing works
Low order thermal network models for dynamic simulations of buildings on city district scale
Lauster, M., Teichmann, J., Fuchs, M., Streblow, R., & Mueller, D. (2014) · Building and Environment · doi:10.1016/j.buildenv.2013.12.016 · Cited by 139 · see citing works
Liu, J., Heidarinejad, M., Gracik, S., & Srebric, J. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.10.062 · Cited by 129 · see citing works
Analytical and comparative testing of EnergyPlus using IEA HVAC BESTEST E100–E200 test suite
Henninger, R. H., Witte, M. J., & Crawley, D. B. (2004) · Energy and Buildings · doi:10.1016/j.enbuild.2004.01.025 · Cited by 119 · see citing works
Ascione, F., Masi, R. F. D., de Rossi, F., et al. (2013) · Cities · doi:10.1016/j.cities.2013.04.012 · Cited by 103 · see citing works
3D City modeling for urban scale heating energy demand forecasting
Strzałka, A., Bogdahn, J., Coors, V., & Eicker, U. (2011) · Science and Technology for the Built Environment · doi:10.1080/10789669.2011.582920 · Cited by 102 · see citing works
Kaden, R., & Kolbe, T. H. (2013) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-2-w1-163-2013 · Cited by 100 · see citing works
Calculation method and tool for assessing energy consumption in the building stock
Tuominen, P., Holopainen, R., Eskola, L., et al. (2014) · Building and Environment · doi:10.1016/j.buildenv.2014.02.001 · Cited by 98 · see citing works
Citygml-based 3d City Model For Energy Diagnostics And Urban Energy Policy Support
Nouvel, R., SCHULTE, C., Eicker, U., et al. (2013) · Building Simulation Conference proceedings · doi:10.26868/25222708.2013.989 · Cited by 92 · see citing works
Dogan, T., Reinhart, C. F., & Michalatos, P. (2015) · Journal of Building Performance Simulation · doi:10.1080/19401493.2015.1006527 · Cited by 86 · see citing works
Self-citationEffect of urban neighborhoods on the performance of building cooling systems
Gracik, S., Heidarinejad, M., Liu, J., & Srebric, J. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.02.037 · Cited by 81 · see citing works
Urban energy simulation: Simplification and reduction of building envelope models
Kim, E.-J., Plessis, G., Hubert, J.-L., & Roux, J.-J. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.07.066 · Cited by 74 · see citing works
TOWARDS A 3D SPATIAL URBAN ENERGY MODELLING APPROACH
Bahu, J.-M., Koch, A., Kremers, E., & Murshed, S. M. (2013) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-2-w1-33-2013 · Cited by 64 · see citing works
Assessing Passive and Active Solar Energy Resources in Cities Using 3D City Models
Eicker, U., Nouvel, R., Duminil, É., et al. (2014) · Energy Procedia · doi:10.1016/j.egypro.2014.10.299 · Cited by 49 · see citing works
Urban Daylight Simulation Calculating the Daylit Area of Urban Designs
Dogan, T., Reinhart, C. F., & Michalatos, P. (2012) · IBPSA-USA Building Simulation Conference (SimBuild) · doi:10.26868/30680611.2012.10a_1_dogan · Cited by 41 · see citing works
Self-citationA Large-Scale Study on Predicting and Contextualizing Building Energy Usage
Kolter, J. Z., & Ferreira, J. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.7806 · Cited by 37 · see citing works
Automated Conversion Of Architectural Massing Models Into Thermal ‘shoebox’ Models
Dogan, T., & Reinhart, C. F. (2013) · Building Simulation Conference proceedings · doi:10.26868/25222708.2013.1123 · Cited by 31 · see citing works
Self-citationDavila, C. C., & REINHART, C. (2013) · Building Simulation Conference proceedings · doi:10.26868/25222708.2013.1351 · Cited by 29 · see citing works
Self-citationGiovannini, L., Pezzi, S., Staso, U. D., et al. (2014) · doi:10.5220/0004997001700177 · Cited by 17 · see citing works
Urban Energy Flow Modelling: A Data-Aware Approach
Perez, D., & Robinson, D. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-29758-8_11 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- L.M.J. Hensen, R. Lamberts (Eds.), IBPSA: Building Performance Simulation for Design and Operation, Spon Press, New York NY, 2011.
- D. Crawley, J. Hand, L. Lawry, Improving the weather information available to simulation programs, in: Proceedings of Building Simulation 1999: Kyoto, Japan, 1999.
- C. Cerezo, T. Dogan, C. Reinhart, Towards standardized building properties template files for early design energy model generation, in: Proceedings of ASHRAE/IBPSA Conference 2014: Atlanta GA, 2014.
- S.K. Firth, K.J. Lomas, Investigating CO 2 emission reductions in existing urban housing using a community domestic energy model, in: Proceedings of Building Simulation, 2009 (Glasgow, Scotland).
- A. Marston, P. Garforth, G. Fleichammer, O. Baumann, Urban scale modeling: how generalized models can help communities halve their energy use in thirty years, in: Proceedings of ASHRAE/IBPSA Conference 2014: Atlanta GA, 2014.
- A. Mastrucci, O. Baume, F. Stazi, S. Salvucci, U. Leopold, A GIS based approach to estimate energy savings and indoor thermal comfort for urban housing stock retrofitting, in: Proceedings of BauSIM 2014: Aachen, Germany, 2014.
- J. Huber, C. Nytsch-Heusen, Development and modeling simulation strategies for large scale urban districts, in: Proceedings of Building Simulation 2011: Sydney, Australia, 2011.
- P. Sehrawat, K. Kensek, Urban energy modeling: GIS as an alternative to BIM, in: Proceedings of ASHRAE/IBPSA Conference 2014: Atlanta GA, 2014.
- F.G. Koene, L.G. Bakker, D. Lanceta, S. Narmsara, Simplified building models of districts, in: Proceedings of BauSIM 2014: Aachen, Germany, 2014.
- L. Giovannini, S. Pezzi, U. Di Staso, F. Prandi, R. De Amicis, Large-scale assessment and visualization of the energy performance of buildings with ecomaps, in: Proceedings of 3rd International Conference DATA 2014: Vienna, Austria, 2014.
- K. Orehounig, G. Mavromatidis, R. Evins, V. Dorer, J. Carmeliet, Predicting energy consumption of a neighborhood using building performance simu- lations, in: Proceedings of Building Simulation and Optimization 2014: London, England, 2014.
Cited by
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Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.023 · Cited by 223 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Learning from urban form to predict building heights
Milojevic-Dupont, N., Hans, N., Kaack, L. H., Zumwald, M., Andrieux, F., de Barros Soares, D., Lohrey, S., Pichler, P.-P., & Creutzig, F. (2020) · PLOS ONE · doi:10.1371/journal.pone.0242010 · Cited by 93 · see citing works
Data-driven strategic planning of building energy retrofitting: the case of Stockholm
Pasichnyi, O., Levihn, F., Shahrokni, H., Wallin, J., & Kordas, O. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.05.373 · Cited by 92 · see citing works
Zhang, L., Fu, X., Li, Y., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106244 · Cited by 40 · see citing works
- Authors
- Reinhart, C. F., & Cerezo Davila, C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Reinhart, Christoph F.
- Venue
- Building and Environment
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 97
- Pages
- 196-202
- ISSN
- 0360-1323 (ISSN-L)
- OpenAlex
- W2183740711
- Cited by
- 1,081
- References
- 46
- Citations by year
- 2026: 140 · 2025: 131 · 2024: 122 · 2023: 118 · 2022: 96 · 2021: 126 · 2020: 126 · 2019: 87 · 2018: 70 · 2017: 55 · 2016: 8 · 2015: 2
- FWCI
- 61.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Building and Environment (publishedVersion, cc-by-nc-nd)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Elsevier eBooks
- Funders
- King Abdulaziz City for Science and Technology
- Massachusetts Institute of Technology
- Environment and Natural Resources
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- urban building energy modeling, bottom-up energy model, building energy simulation, result validation
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