A data-driven predictive model of city-scale energy use in buildings

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Classification and Regression by randomForest

Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

Prasad, A. M., Iverson, L. R., & Liaw, A. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 1,987

A review on the prediction of building energy consumption

Zhao, H., & Magoulès, F. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.049 · Cited by 1,947

Analysis of a Random Forests Model

Biau, G. (2010) · Journal of machine learning research · doi:10.5555/2503308.2343682 · Cited by 1,690

Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

Tso, K. F., & Yau, K. K. W. (2007) · Energy · doi:10.1016/j.energy.2006.11.010 · Cited by 939

Machine Learning in Urban Building Energy Modeling

Abbasabadi, N., & Ashayeri, M. (2024) · Wiley eBooks · doi:10.1002/9781394172092.ch2 · Cited by 6

An indicator for discriminating annual changes in energy use patterns: Enhancing disclosure policies

Jeon, S., Kang, H., & Kim, S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115619 · Cited by 4

Optimizing sustainable building retrofits with Emperor Penguin Optimization: a machine-learning approach for energy consumption prediction

Shihadeh, J., Abu-Shaikha, M. F., & Zghoul, N. (2024) · Asian Journal of Civil Engineering · doi:10.1007/s42107-024-00985-2 · Cited by 4

UBEM calibration method by energy consumption disaggregation using a change-point model

Kim, H.-G., & Kim, S. S. (2024) · Journal of Building Performance Simulation · doi:10.1080/19401493.2024.2412135 · Cited by 4

Urban scale climate change adaptation through smart technologies

Bazazzadeh, H., Pourahmadi, B., Pilechiha, P., Safaei, S. S. H., & Berardi, U. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85552-5.00011-7 · Cited by 4

Data-Driven Residence Energy Consumption Prediction Model Considering Water Use Data and Socio-Demographic Data

Shu, L., & Zhao, D. F. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485262.051 · Cited by 4

Investigating Energy Use in a City District in Nordic Climate Using Energy Signature

Eriksson, M., Akander, J., & Moshfegh, B. (2022) · Energies · doi:10.3390/en15051907 · Cited by 4

A Comparative Analysis Among Standard Load Profiles for Natural Gas Consumption Simulation at Urban Scale

Canale, L., Cortellessa, G., Dell’Isola, M., Ficco, G., Frattolillo, A., & Zuena, F. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210602 · Cited by 4

Energy Cost Burdens for Low-Income and Minority Households in Six U.S. Cities: Evidence from Energy Benchmarking and Audit DataPreprint

Kontokosta, C. E., Reina, V. J., & Bonczak, B. J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3295367 · Cited by 4

Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County

Begić, H., & Krstić, H. (2025) · Buildings · doi:10.3390/buildings15091511 · Cited by 3

Urban form as a lever: Reducing residential natural gas dependency

Abar, S. N., Schulwitz, M., & Faulstich, M. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106891 · Cited by 3

Energy consumption prediction of residential area in cold regions: a deep transfer learning approach with limited data

Du, Z., Han, Y., Zhuang, D., Kang, X., & Shao, T. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116795 · Cited by 3

The relationship between building and urban mobility-based energy consumption and urban form: A machine learning approach

karlı, M., & Terzi, F. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107319 · Cited by 3

Bias prediction vs. parameter Estimation: Calibration Framework comparison for urban Building energy modeling considering application scenarios

Xiao, J., Zhou, H. M., Chen, S., Zhao, Y., & Lin, B. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146663 · Cited by 3

Classifying building energy efficiency from street view and aerial images in Denmark

Mayer, K., Heilborn, G., & Fischer, M. A. (2023) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3600100.3623723 · Cited by 3

A Data-driven System for City-wide Energy Footprinting and Apportionment

Wei, P., & Jiang, X. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3433639 · Cited by 3

Study on the Improvement of Expected Energy Savings and Actual Energy Savings in Apartments

Park, W.-J., & Kim, H.-M. (2018) · Sustainability · doi:10.3390/su10041089 · Cited by 3

Building Automation for Energy Efficiency

(2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812817-6.00008-5 · Cited by 3

Predicting CO2 Emissions for Buildings Using Regression and Classification

Avramidou, A., & Tjortjis, C. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-79150-6_43 · Cited by 3

Synthetic Minority Oversampling Based Machine Learning Method for Urban Level Building Eui Prediction and BenchmarkingPreprint

Jin, X., & Xiao, F. (2021) · doi:10.46855/energy-proceedings-8459 · Cited by 3

The need of explainability in low-carbon urban system design using AI: A systematic review

Chen, T., & Debnath, R. (2025) · Machine Learning Earth · doi:10.1088/3049-4753/adde60 · Cited by 3

Building energy consumption and urban form: A multi-temporal empirical investigation

Ho, C. O., Miotti, M., & Jain, R. K. (2025) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083251364079 · Cited by 2

From tweets to energy trends (TwEn2): social sensing–informed urban building energy modeling

Abbasabadi, N., & Ashayeri, M. (2025) · Frontiers in Energy Research · doi:10.3389/fenrg.2025.1688348 · Cited by 2

Urban Buildings Energy Consumption Estimation Leveraging High-Performance Computing: A Case Study of Bologna

Canfora, A., Bergamaschi, E., Mioli, R., Battini, F., Esposti, M. D. D., Pedrazzi, G., & Dellacasa, C. M. (2025) · Urban Science · doi:10.3390/urbansci10010004 · Cited by 2

A review of explainable data-driven building energy benchmarking at multiple time scales

Lu, Y., & Li, T. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117240 · Cited by 2

Detecting Floors in Residential Buildings

Kramm, A., Friske, J., & Peukert, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42608-7_11 · Cited by 2

A data-driven system for city-scale personal energy footprint estimations

Wei, P., & Jiang, X. (2018) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3276774.3281018 · Cited by 2

Predicting Building Primary Energy Use Based on Machine Learning: Evidence from Portland

Junjia, Y., Alias, A. H., Haron, N. A., & Bakar, N. A. (2024) · International Journal of Architectural Engineering Technology · doi:10.15377/2409-9821.2024.11.7 · Cited by 1

Predicting Water Consumption for School Buildings in Osijek-Baranja County, Croatia, Using MLR and ANN

Begić, H., & Krstić, H. (2025) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-6943 · Cited by 1

Machine Learning for Sustainable Urban Energy Planning: A Comparative Model Analysis

Tiwari, A., Kukreja, R., Subramanian, S., Devkar, A., Mahabir, R., Gkountouna, O., & Croitoru, A. (2025) · Energies · doi:10.3390/en19010176 · Cited by 1

Minimum Viable Data (MVD) for digital twinning: Underserved community energy representation in data scarcity scenarios

Hu, D., & Rakha, T. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115164 · Cited by 1

The potential of geospatial technologies in urban building energy modeling: a review and future perspectives

Liu, Q., He, X., Zhou, W., Peng, C., & Zhou, Y. (2026) · All Earth · doi:10.1080/27669645.2026.2677266 · Cited by 1

Review of emerging smart techniques and their role in enhancing energy efficiency in residential and commercial buildings

Agyekum, E. B., Tarawneh, B., Praveenkumar, S., Odoi-Yorke, F., & Rashid, F. L. (2026) · Energy Exploration & Exploitation · doi:10.1177/01445987261470169 · Cited by 1

A systematic review of machine learning based methods for applications in urban building energy modelling

Liang, S., Maskarenj, M. S., Llaguno-Munitxa, M., & Altomonte, S. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118045 · Cited by 1

Data-Driven Urban Energy Modeling: Global Trends and India

Deb, C. (2022) · Design science and innovation · doi:10.1007/978-981-19-0412-7_9 · Cited by 1

Datasheets for Energy Datasets: An Ethically-Minded Approach to Documentation

Heintz, I. (2023) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3599733.3600249 · Cited by 1

Residential building energy performance prediction at an urban scale using ensemble machine learning algorithms

Ali, U., Bano, S., Shamsi, M. H., Sood, D., Hoare, C., & O’Donnell, J. (2023) · Computing in construction · doi:10.35490/ec3.2023.200 · Cited by 1

Development of a methodology of validation for urban building energy models and application to French residential consumption

Lefort, L., de Rougé, R. B., Schetelat, P., Berthou, T., Riederer, P. F., Duplessis, B., & Peirano, E. (2020) · HAL · Cited by 1

Advancing urban analytics for energy transitions: Data-driven strategic planning for citywide building retrofitting

Pasichnyi, O. (2020) · doi:10.30746/trita-abe-dlt-2042 · Cited by 1

Identifying the Need for an Energy Urban Planning RolePreprint

Boria, E. S., Rozhkov, A., & Seyrfar, A. (2020) · doi:10.46855/energy-proceedings-6879 · Cited by 1

Dynamic Modeling of Energy Demand in Educational Institutions Affected by Covid-19 and Energy CrisisPreprint

Alisoltani, N., Nefzaoui, E., & Oukhellou, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4834255 · Cited by 1

Building Energy Graphs (BEGs): How Graph Representation Design Impacts GNN-Based Building Energy PredictionPreprint

Dalach, A., Wang, Z., Nousias, S., & Borrmann, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6361640 · Cited by 1

Linked data for enhanced energy management at district and building levels : an indicator-based methodology

Li, Y. (2017) · doi:10.20868/upm.thesis.49150

Do Mandatory Energy Audits Reduce Building Energy Use?: A Bayesian Analysis of New York City's Local Law 87Preprint

Kontokosta, C. E., Spiegel-Feld, D., & Papadopoulos, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3275809

A Data-Driven Approach for Long-Term Building Energy Demand Prediction

Wang, L., & El-Gohary, N. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482865.123

Developing a Building-by-Building Estimate of City-wide Electricity Savings Potential: An Early Trial for the City of Wilmington, DelawarePreprint

Byrne, J., Taminiau, J., Carretero, D. S., & Shin, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3914143

Investigating Thermal Performance of Residential Buildings in Cold Climates

Doma, A. (2021) · Spectrum Research Repository (Concordia University)

References

(2018) · Wiley eBooks · doi:10.1002/9781119341901.refs

Performance analysis of demand forecasting in energy consumption based on ensemble model

Jaganathan, D., & Ayyanathan, N. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i4.3649

The nonlinear influence of urban and building factors on residential building energy use: An empirical study with quantile regression in SeoulPreprint

Lee, S., & Quan, S. J. (2022) · doi:10.46855/energy-proceedings-9382

Predictions of Energy Consumption of Buildings' Life Cycle to Mitigate the Effects of Climate Change with a Focus on Energy Efficiency

Patil, P. P., & Sondkar, S. (2022) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon55413.2022.10014935

Towards a future sustainable housing stock

van der Bent, H. S. (2022) · Architecture and the Built Environment · doi:10.59490/abe.2022.16.6768

A Note on the Practical Efficiency of Using Wavelet Transform for Short-term Load Forecasting in Smart Grids

Karkhaneh, M., & Ozgoli, S. (2023) · Journal of Control · doi:10.61186/joc.17.3.55

Machine Learning for Sustainable Urban Energy Planning: A Comparative Model AnalysisPreprint

Tiwari, A., Kukreja, R., Subramanian, S., Devkar, A., Mahabir, R., Gkountouna, O., & Croitoru, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5258801

Enhanced Building Electric Power Demand Forecasting Using IoT and Machine Learning

Bapaume, T., Alisoltani, N., Nefzaoui, E., & Oukhellou, L. (2025) · IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) · doi:10.1109/metrolivenv64961.2025.11107141

Development of an Overall Thermal Transfer Value (Ottv) Based Energy Benchmarking System for Commercial BuildingsPreprint

Chan, A. L. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5369266

Building Energy Performance Modelling and Simulation

Ferdyn-Grygierek, J., Grygierek, K., & Psikuta, A. (2025) · Energies · doi:10.3390/en18195295

Electricity demand dynamics in educational buildings during crises: A case study of COVID-19 and the energy crisis

Alisoltani, N., Nefzaoui, E., & Oukhellou, L. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e44369

Multi-Objective Optimisation for National Residential Retrofits: Scalable Bottom-Up Impact AssessmentsPreprint

Oliver, S., Seyedzadeh, S., & Rahimian, F. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6222322

Advancing Sustainability in Buildings through the Water–Energy–Quality Nexus

Lee, J., Murphy, E., Fathi, M., & Younos, T. (2026) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-7313

Human-Centered Building Energy Modeling and Simulation for Retrofit

Zhao, W., & Tao, Y. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00040

Unveiling latent frontiers of urban and rural sustainability: a semi-automatic overview with LDA topic model

Pan, J., & Shen, Y. (2026) · Applied Geography · doi:10.1016/j.apgeog.2026.103956

The Effect of Remittances on Import Demand in Ghana

Arthur, L., Amoako, S., Kulu, E., & Sackey, F. G. (2026) · The International Trade Journal · doi:10.1080/08853908.2026.2644242

Optimizing ANFIS for heating load forecasting: a hybrid approach for enhanced accuracy in district heating systems

Wang, X., Han, L., & Wei, X. (2026) · Journal of Engineering and Applied Science · doi:10.1186/s44147-026-00937-4

Developing and Validating Heating Energy Consumption Models for Schools in Osijek-Baranja County, Croatia

Begić, H., Krstić, H., & Obradović, D. (2026) · Urban Science · doi:10.3390/urbansci10040187

Reducing residential electricity demand in a hot, arid city: the role of urban form in preventing blackouts

Abar, S. N., Schulwitz, M., & Faulstich, M. (2026) · City and Built Environment · doi:10.1007/s44213-026-00076-9

Energy Waste Detection in Smart Buildings Using Open Energy Data

Thanh, P. M. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-21944-2_1

Diagnosing Peer-Group Misfit in Registry-Based Urban Building Energy BenchmarksPreprint

Jang, Y., Park, J., Kim, Y., & Yu, K.-H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6853614

Diagnosing Peer-Group Misfit in Registry-Based Urban Building Energy BenchmarksPreprint

Jang, Y., Park, J., Kim, Y., & Yu, K.-H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6853615

AI-Enhanced Urban Building Energy Modeling for Health-Driven Decarbonization in Vulnerable Communities

Abbasabadi, N., Moroseos, T., Ashayeri, M., & Meek, C. (2026) · Architecture · doi:10.3390/architecture6020084

The Environment in the City and Social-Ecological Uncertainty

Cartaxo, T. D. M. (2026) · Springer eBooks · doi:10.1007/978-3-032-23840-5_2

An integrated machine learning approach to economically prioritize energy conservation measures for multifamily buildings under New York City’s Local Law 97

Zhang, B., & Crovella, P. L. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.116461

Building Energy Performance Evaluation of Public Institutions Using the Korean Green Button System

고현아Ko, H.-A., & Lee, J.-W. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671610006

Transforming urban energy systems with scalable non-invasive building thermal sensing

Wei, H., Wu, J., Liu, J. H., Xiao, F., & Yan, J. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103394

XAI energy benchmarking with multi-scale satellite and tabular fusion

Li, T., Newton, D., Lu, Y., & Saraf, S. (2026) · Building Simulation · doi:10.1007/s12273-026-1437-9

Urban Multi-energy Consumption Prediction and Carbon Emission Collaborative Monitoring Model Based on GAN and Random Forest

Zhang, C., Liu, S., Xu, X., Li, J., & Liu, S. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-5304-4_59

Beyond archetypes: Creating behavior-aware synthetic building populations through probabilistic multi-source data integration for multi-domain urban analysis

Latifi, M., Zucca, C., & Willemsen, M. C. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114872

A database of building energy use determinants across spatial scales: Patterns, policies, and a multi-metric framework for retrofitting

Korsavi, S. S., Iulo, L. D., & Azari, R. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107618

Operational Energy Performance of LEED-Certified Buildings: A City-Scale Benchmarking Analysis in Philadelphia

Vosoughkhosravi, S., & Ozcan-Deniz, G. (2026) · Sustainability · doi:10.3390/su18147086

An AI-augmented urban building energy Modeling framework integrating physics-based simulation, machine learning, and computer vision: The CityEnerTwin UBEM module

Abbasabadi, N., Worthy, A., Zhang, E. Z., Zhao, Y., & Liu, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117984

Operational carbon emissions in regional buildings: a review of drivers, baselines, forecasting, and mitigation

Zuo, C., Chen, Z., Yang, P., Yi, D., Huang, Z., Feng, S., & Li, Z. (2026) · Journal of Urban Management · doi:10.1016/j.jum.2026.100543

Residual-based district scale vulnerability screening and GHG reduction prioritization for building stock decarbonization in high-density cities

Seo, B., Choi, S., Lee, B., Lee, S., & Jeong, S. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130612

AI-Enabled Emerging Technologies for High-Performance Building Benchmarking: Balance Energy, Comfort, and Health Resilience

Li, T., Ye, Y., & Lu, Y. (2026) · Urban sustainability · doi:10.1007/978-981-92-2297-1_8

Recognition of Daily Room Temperature Fluctuation Patterns Based on DBSCAN Clustering and Its Dynamic Response Study

Zhou, E., Liang, R., Zuo, T., Liu, Y., & Du, M. (2026) · Buildings · doi:10.3390/buildings16173350

Diagnosing peer-group misfit in registry-based urban building energy benchmarks

Jang, Y., Park, J., Kim, Y., & Yu, K. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107903

An AI-Driven Situational Awareness Architecture for Citywide Energy Governance with Risk-Prioritized Multi-Drone Verification

Hermanus, D. R., Supangkat, S. H., & Hidayat, F. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715120

DELFT-UBEM: An automated database-driven pipeline for district-scale building energy modeling, demonstrated on Dutch district retrofit

Jalilzadeh, A., Rafiee, A., Konstantinou, T., de Graaf, S., Scholten, H. J., & van Oosterom, P. J. M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118345

Air conditioner ownership and usage in high-rise residences in the Klang Valley, Malaysia: results of an online survey

Tan, X. Y., Zaid, S. M., & Mahyuddin, N. (2026) · Energy Efficiency · doi:10.1007/s12053-026-10482-x

Workpiece gripping mechanism for industrial robots

(1984) · Mechanism and Machine Theory · doi:10.1016/0094-114x(84)90075-2

Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data

Kontokosta, C. E., & Tull, C. (2015) · Cited by 13

Grids: Graph-Based Regional Insights for Dynamic Spatialization in Building Energy PredictionPreprint

Dougherty, T. R., Excell, L. E., & Jain, R. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5226426

A Data-Driven Approach for Long-Term Building Energy Demand Prediction

Wang, L., & El-Gohary, N. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482865.123

Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level

Tardioli, G., Kerrigan, R., Oates, M., O’Donnell, J., & Finn, D. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.11.754 · Cited by 110