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 · see citing works

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 · see citing works

The Nature of Statistical Learning Theory

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

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 · see citing works

Classification and Regression by randomForest

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

Bagging predictors

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

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

Prasad, A., Iverson, L. R., & Liaw, A. I. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 2,297 · see citing works

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 1,961 · see citing works

Analysis of a Random Forests Model

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

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 · see citing works

The Nature Of Statistical Learning Theory~

Cherkassky, V. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1997.641482 · Cited by 919 · see citing works

Urban energy use and carbon emissions from cities in China and policy implications

Dhakal, S. (2009) · Energy Policy · doi:10.1016/j.enpol.2009.05.020 · Cited by 831 · see citing works

Applying support vector machines to predict building energy consumption in tropical region

Dong, B., Cao, C., & Lee, S. E. (2004) · Energy and Buildings · doi:10.1016/j.enbuild.2004.09.009 · Cited by 803 · see citing works

The impact of urban form on U.S. residential energy use

Ewing, R., & Rong, F. (2008) · Housing Policy Debate · doi:10.1080/10511482.2008.9521624 · Cited by 712 · see citing works

Analysis of a Random Forests ModelPreprint

Gérard Biau, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1005.0208 · Cited by 672 · see citing works

A review of urban energy system models: Approaches, challenges and opportunities

Keirstead, J. E., Jennings, M., & Sivakumar, A. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.047 · Cited by 644 · see citing works

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.02.057 · Cited by 602 · see citing works

Applying support vector machine to predict hourly cooling load in the building

Li, Q., Meng, Q., Cai, J., Yoshino, H., & Mochida, A. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.035 · Cited by 485 · see citing works

A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes

Pérez‐Lombard, L., Ortiz, J., González, R., & Maestre, I. R. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2008.10.004 · Cited by 459 · see citing works

A better measure of relative prediction accuracy for model selection and model estimation

Tofallis, C. (2014) · Journal of the Operational Research Society · doi:10.1057/jors.2014.103 · Cited by 447 · 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

State and Municipal Climate Change Plans: The First Generation

Wheeler, S. M. (2008) · Journal of the American Planning Association · doi:10.1080/01944360802377973 · Cited by 382 · see citing works

Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles

Heiple, S., & Sailor, D. J. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2008.01.005 · Cited by 329 · see citing works

Innovation and Climate Action Planning

Bassett, E. M., & Shandas, V. (2010) · Journal of the American Planning Association · doi:10.1080/01944363.2010.509703 · Cited by 284 · see citing works

Erratum: A better measure of relative prediction accuracy for model selection and model estimation

Tofallis, C. (2014) · Journal of the Operational Research Society · doi:10.1057/jors.2014.124 · Cited by 210 · see citing works

Improving the prediction of UK domestic energy-demand using annual consumption-data

Baker, K. J., & Rylatt, R. M. (2007) · Applied Energy · doi:10.1016/j.apenergy.2007.09.004 · Cited by 178 · see citing works

Evaluating the effectiveness of urban energy conservation and GHG mitigation measures: The case of Xiamen city, China

Lin, J., Cao, B., Cui, S., Wang, W., & Bai, X. (2010) · Energy Policy · doi:10.1016/j.enpol.2010.04.042 · Cited by 106 · see citing works

How much information disclosure of building energy performance is necessary?

Hsu, D. (2013) · Energy Policy · doi:10.1016/j.enpol.2013.08.094 · Cited by 101 · see citing works

Applications of machine learning methods to identifying and predicting building retrofit opportunities

Marasco, D. E., & Kontokosta, C. E. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.06.092 · Cited by 96 · see citing works

Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy

Kontokosta, C. E., & Jain, R. K. (2015) · Sustainable Cities and Society · doi:10.1016/j.scs.2015.05.007 · Cited by 85 · see citing works

Sustainable cities – modelling urban energy supply and demand

Brownsword, R. A., Fleming, P., Powell, J. C., & Pearsall, N. M. (2004) · Applied Energy · doi:10.1016/j.apenergy.2004.10.005 · Cited by 69 · see citing works

A Market-Specific Methodology for a Commercial Building Energy Performance Index

Kontokosta, C. E. (2014) · The Journal of Real Estate Finance and Economics · doi:10.1007/s11146-014-9481-0 · Cited by 60 · see citing works

Auditing energy use in cities

Bennett, M., & Newborough, M. (2001) · Energy Policy · doi:10.1016/s0301-4215(00)00108-7 · Cited by 59 · see citing works

Energy disclosure, market behavior, and the building data ecosystem

Kontokosta, C. E. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12163 · Cited by 56 · see citing works

Uncertainty and modeling energy consumption: Sensitivity analysis for a city-scale domestic energy model

Kavgic, M., Mumovic, D., Summerfield, A. J., Stevanovic, Z. M., & Ećim-Đurić, O. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.01.005 · Cited by 56 · see citing works

Greening the Regulatory Landscape: The Spatial and Temporal Diffusion of Green Building Policies in U.S. Cities

Kontokosta, C. E. (2011) · Journal of Sustainable Real Estate · doi:10.1080/10835547.2011.12091821 · Cited by 53 · see citing works

Correlating energy consumption with multi-unit residential building characteristics in the city of Toronto

Touchie, M. F., Binkley, C., & Pressnail, K. D. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.07.068 · Cited by 42 · see citing works

Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory SP]

Jain, R. K., Moura, J. M. F., & Kontokosta, C. E. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2330357 · Cited by 41 · see citing works

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Paudel, S., Nguyen, P. H., Kling, W. L., Elmitri, M., Lacarrière, B., & Corre, O. L. (2015) · TU/e Research Portal · doi:10.48550/arxiv.1507.05019 · Cited by 36 · see citing works

New York City Local Law 84 Benchmarking Report, August 2012 (Provided data analysis with David Hsu)

Kontokosta, C. E. (2012) · Cited by 8 · see citing works

Ten questions on urban building energy modeling

Hong, T., Chen, Y., Luo, X., Luo, N., & Lee, S. H. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106508 · Cited by 587 · see citing works

Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis

Chen, Y., Hong, T., & Piette, M. A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.128 · Cited by 408 · see citing works

Electricity load forecasting: a systematic review

Nti, I. K., Teimeh, M., Nyarko‐Boateng, O., & Adekoya, A. F. (2020) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-020-00021-8 · Cited by 400 · see citing works

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

Pan, Y., Zhu, M., Lv, Y. A., Yang, Y., Liang, Y., Yin, R., Yang, Y., Jia, X., Wang, X., Zeng, F., et al. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100135 · Cited by 371 · see citing works

How can artificial intelligence impact sustainability: A systematic literature review

Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134120 · Cited by 369 · 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

Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis

Ali, U., Shamsi, M. H., Hoare, C., Mangina, E., & O’Donnell, J. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111073 · Cited by 327 · see citing works

Machine learning applications in urban building energy performance forecasting: A systematic review

Fathi, S., Srinivasan, R. S., Fenner, A. E., & Fathi, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110287 · Cited by 317 · see citing works

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Runge, J., & Zmeureanu, R. (2019) · Energies · doi:10.3390/en12173254 · Cited by 269 · see citing works

Urban building energy modeling: State of the art and future prospects

Johari, F., Peronato, G., Sadeghian, P., Zhao, X., & Widén, J. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109902 · Cited by 266 · see citing works

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.023 · Cited by 223 · see citing works

Machine learning-based thermal response time ahead energy demand prediction for building heating systems

Guo, Y., Wang, J., Chen, H., Li, G., Liu, J., Xu, C., Huang, R., & Huang, Y. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.125 · Cited by 207 · see citing works

Machine learning for spatial analyses in urban areas: a scoping review

Casali, Y., Aydin, N. Y., & Comes, T. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104050 · Cited by 207 · see citing works

Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate

Javanroodi, K., Mahdavinejad, M., & Nik, V. M. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.116 · Cited by 193 · see citing works

Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings

Walker, S. S. W., Khan, W. A., Katić, K., Maassen, W., & Zeiler, W. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109705 · Cited by 187 · see citing works

Urban energy use modeling methods and tools: A review and an outlook

Abbasabadi, N., & Ashayeri, M. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106270 · Cited by 182 · see citing works

Grading buildings on energy performance using city benchmarking data

Papadopoulos, S., & Kontokosta, C. E. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.10.053 · Cited by 177 · see citing works

A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

Ali, U., Shamsi, M. H., Bohacek, M., Purcell, K. G. R., Hoare, C., Mangina, E., & O’Donnell, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115834 · Cited by 171 · see citing works

Evaluation of tree-based ensemble learning algorithms for building energy performance estimation

Papadopoulos, S., Azar, E., Woon, W. L., & Kontokosta, C. E. (2017) · Journal of Building Performance Simulation · doi:10.1080/19401493.2017.1354919 · Cited by 162 · see citing works

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont, N., & Creutzig, F. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102526 · Cited by 160 · see citing works

Exploration of the mechanism of chemical looping steam methane reforming using double perovskite-type oxides La1.6Sr0.4FeCoO6

Zhao, K., Zheng, A., Li, H., He, F., Huang, Z., Wei, G., Shen, Y., & Zhao, Z. (2017) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2017.08.027 · Cited by 156 · see citing works

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Ali, U., Bano, S., Shamsi, M. H., Sood, D., Hoare, C., Zuo, W., Hewitt, N., & O’Donnell, J. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113768 · Cited by 156 · see citing works

Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities

Kontokosta, C. E., Hong, B., Johnson, N. E., & Starobin, D. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.03.004 · Cited by 159 · see citing works

Modelling heating and cooling energy demand for building stock using a hybrid approach

Li, X., & Yao, R. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110740 · Cited by 154 · see citing works

Component-based machine learning for performance prediction in building design

Geyer, P., & Singaravel, S. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.07.011 · Cited by 135 · see citing works

Early predicting cooling loads for energy-efficient design in office buildings by machine learning

Ngo, N.-T. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.10.004 · Cited by 133 · see citing works

Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China

Batunacun, Wieland, R., Lakes, T., & Nendel, C. (2021) · Geoscientific model development · doi:10.5194/gmd-14-1493-2021 · Cited by 132 · see citing works

Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles

Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., & Srinivasan, R. S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110929 · Cited by 129 · see citing works

Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data

Bonczak, B. J., & Kontokosta, C. E. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.09.004 · Cited by 123 · see citing works

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

Bayomi, N., & Fernández, J. (2023) · Drones · doi:10.3390/drones7100637 · Cited by 120 · see citing works

Predicting city-scale daily electricity consumption using data-driven models

Wang, Z., Hong, T., Li, H., & Piette, M. A. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2021.100025 · Cited by 118 · see citing works

Developing a common approach for classifying building stock energy models

Langevin, J., Reyna, J., Ebrahimigharehbaghi, S., Sandberg, N. H., Fennell, P. J., Nägeli, C., Laverge, J., Delghust, M., Mata, É., Hove, M. Y. C. V., et al. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110276 · Cited by 116 · see citing works

Integrated energy systems planning with electricity, heat and gas using particle swarm optimization

Qin, C., Yan, Q., & He, G. (2019) · Energy · doi:10.1016/j.energy.2019.116044 · Cited by 113 · see citing works

On the accuracy of Urban Building Energy Modelling

Oraiopoulos, A., & Howard, B. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111976 · Cited by 113 · see citing works

SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods

Roth, J., Martin, A., Miller, C. C., & Jain, R. K. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115981 · Cited by 111 · see citing works

Planning for sustainable cities by estimating building occupancy with mobile phones

Barbour, E. R., Davila, C. C., Gupta, S., Reinhart, C. F., Kaur, J., & González, M. C. (2019) · Nature Communications · doi:10.1038/s41467-019-11685-w · Cited by 109 · see citing works

Window opening model using deep learning methods

Markovic, R., Grintal, E., Wölki, D., Frisch, J., & van Treeck, C. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.09.024 · Cited by 108 · see citing works

Investigating the performance of machine learning models combined with different feature selection methods to estimate the energy consumption of buildings

Liu, X., Tang, H., Ding, Y., & Yan, D. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112408 · Cited by 108 · see citing works

Energy Cost Burdens for Low-Income and Minority Households

Kontokosta, C. E., Reina, V. J., & Bonczak, B. J. (2019) · Journal of the American Planning Association · doi:10.1080/01944363.2019.1647446 · Cited by 107 · see citing works

From simulation to data-driven approach: A framework of integrating urban morphology to low-energy urban design

Wang, W., Liu, K., Zhang, M., Shen, Y., Jing, R., & Xu, X. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.08.024 · Cited by 103 · see citing works

On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy

Zinzi, M., Carnielo, E., & Mattoni, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.192 · Cited by 101 · see citing works

Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days

Kamel, E., Sheikh, S., & Huang, X. (2020) · Energy · doi:10.1016/j.energy.2020.118045 · Cited by 100 · see citing works

The future role of energy geostructures in fifth generation district heating and cooling networks

Meibodi, S. S., & Loveridge, F. A. (2021) · Energy · doi:10.1016/j.energy.2021.122481 · Cited by 100 · see citing works

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim, F. D., Dong, B., Ouf, M. M., Wang, Q., Pigliautile, I., Kang, X., Hong, T., Wu, W., Liu, Y., Rumi, S. K., et al. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106964 · Cited by 99 · see citing works

An integrated data-driven framework for urban energy use modeling (UEUM)

Abbasabadi, N., Ashayeri, M., Azari, R. S., Stephens, B., & Heidarinejad, M. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113550 · Cited by 96 · 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

The impact of mandatory energy audits on building energy use

Kontokosta, C. E., Spiegel-Feld, D., & Papadopoulos, S. (2020) · Nature Energy · doi:10.1038/s41560-020-0589-6 · Cited by 84 · see citing works

Data-Driven Tools for Building Energy Consumption Prediction: A Review

Olu-Ajayi, R., Alaka, H., Owolabi, H. A., Akanbi, L., & Ganiyu, S. A. (2023) · Energies · doi:10.3390/en16062574 · Cited by 83 · see citing works

Development of an energy atlas for renovation of the multifamily building stock in Sweden

Johansson, T. D., Olofsson, T., & Mangold, M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.027 · Cited by 83 · see citing works

Modelling building energy use at urban scale: A review on their account for the urban environment

Wong, C. H. H., Cai, M., Ren, C., Huang, Y., Liao, C., & Yin, S. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108235 · Cited by 85 · see citing works

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

Wang, W., Hong, T., Xu, X., Chen, J., Liu, Z., & Xu, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.04.085 · Cited by 80 · see citing works

Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits

Shen, M., Lu, Y., Wei, K. H., & Cui, Q. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109839 · Cited by 80 · see citing works

Comparative study of data driven methods in building electricity use prediction

Zeng, A., Liu, S., & Yu, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.04.029 · Cited by 79 · see citing works

Adaptive learning based data-driven models for predicting hourly building energy use

Wang, L., Kubichek, R. F., & Zhou, X. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.10.054 · Cited by 78 · see citing works

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi, E., Basak, J. K., Sihalath, T., Park, J., Kim, H. T., & Moon, B. E. (2021) · Animals · doi:10.3390/ani11010222 · Cited by 77 · see citing works

Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach

Nutkiewicz, A., Choi, B., & Jain, R. K. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2021.100038 · Cited by 77 · see citing works

Pattern recognition in building energy performance over time using energy benchmarking data

Papadopoulos, S., Bonczak, B., & Kontokosta, C. E. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.079 · Cited by 76 · see citing works

Incorporating machine learning with building network analysis to predict multi-building energy use

Xu, X., Wang, W., Hong, T., & Chen, J. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.01.002 · Cited by 76 · see citing works

A review and reflection on open datasets of city-level building energy use and their applications

Jin, X., Zhang, C., Xiao, F., Li, A., & Miller, C. C. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112911 · Cited by 75 · see citing works

An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings

Canale, L., Fazio, A. R. D., Russo, M., Frattolillo, A., & Dell’Isola, M. (2021) · Energies · doi:10.3390/en14041078 · Cited by 73 · see citing works

A review of operational energy consumption calculation method for urban buildings

Li, Z., Lin, B., Zheng, S., Liu, Y., Wang, Z., & Dai, J. (2020) · Building Simulation · doi:10.1007/s12273-020-0619-0 · Cited by 73 · see citing works

Designing climate resilient energy systems in complex urban areas considering urban morphology: A technical review

Javanroodi, K., Perera, A. T. D., Hong, T., & Nik, V. M. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100155 · Cited by 70 · see citing works

Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach

Nageler, P. J., Koch, A., Mauthner, F., Leusbrock, I., Mach, T., Hochenauer, C., & Heimrath, R. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.09.034 · Cited by 70 · see citing works

A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

Wu, W., Dong, B., Wang, Q., Kong, M., Yan, D., An, J., & Liu, Y. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115656 · Cited by 69 · see citing works

Predicting long-term monthly electricity demand under future climatic and socioeconomic changes using data-driven methods: A case study of Hong Kong

Liu, S., Zeng, A., Lau, K. K., Ren, C., Chan, P. W., & Ng, E. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102936 · Cited by 69 · see citing works

Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale

Kazmi, H., Fu, C., & Miller, C. C. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110407 · Cited by 68 · see citing works

Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective

Roth, J., Lim, B., Jain, R. K., & Grueneich, D. M. (2020) · Energy Policy · doi:10.1016/j.enpol.2020.111327 · Cited by 64 · see citing works

Generation, analysis, and applications of high resolution electricity load profiles in Qatar

Bayram, I. S., Saffouri, F., & Koç‬, M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.02.084 · Cited by 63 · see citing works

Measured Performance of a Mixed-Use Commercial-Building Ground Source Heat Pump System in Sweden

Spitler, J. D., & Gehlin, S. E. A. (2019) · Energies · doi:10.3390/en12102020 · Cited by 59 · see citing works

A method to evaluate building energy consumption based on energy use index of different functional sectors

Wu, J. S., Lian, Z., Zheng, Z., & Zhang, H. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101893 · Cited by 57 · see citing works

A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework

Ali, A., Jayaraman, R., Azar, E., & Maalouf, M. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111268 · Cited by 57 · see citing works

Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model

Jiang, F., Ma, J., Li, Z., & Ding, Y. (2022) · Energy · doi:10.1016/j.energy.2022.123631 · Cited by 56 · see citing works

A sustainable data-driven energy consumption assessment model for building infrastructures in resource constraint environment

Mohapatra, S. K., Mishra, S., Tripathy, H. K., & Alkhayyat, A. H. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102697 · Cited by 56 · see citing works

A systematic review of current AI techniques used in the context of the SDGs

Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-024-00668-5 · Cited by 55 · see citing works

Data-driven approach to predicting the energy performance of residential buildings using minimal input data

Seo, J.-H., Kim, S.-H., Lee, S., Jeong, H., Kim, T., & Kim, J. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.108911 · Cited by 54 · see citing works

A deep spatial-temporal data-driven approach considering microclimates for power system security assessment

Huang, T., Guo, Q., Sun, H., Tan, C., & Hu, T. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.013 · Cited by 51 · see citing works

Application of machine learning to estimate building energy use intensities

Veiga, R. K., Veloso, A. C. D. O., Melo, A. P., & Lamberts, R. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111219 · Cited by 51 · see citing works

Energy burden and air conditioning adoption in New York City under a warming climate

Ortiz, L., Gamarro, H., González, J. E., & McPhearson, T. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103465 · Cited by 51 · see citing works

A data-driven approach for building energy benchmarking using the Lorenz curve

Chen, Y., Tan, H., & Berardi, U. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.03.066 · Cited by 50 · see citing works

Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics

Manivannan, M., Najafi, B., & Rinaldi, F. (2017) · Energies · doi:10.3390/en10111905 · Cited by 47 · see citing works

Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach

Ye, Z., Cheng, K.-L., Hsu, S., Wei, H.-H., & Cheung, C. M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117453 · Cited by 46 · see citing works

Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling

Zhang, D., Chen, Y., Pan, X., Peng, Z., & Yang, J. (2021) · Energies · doi:10.3390/en14041049 · Cited by 45 · see citing works

Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning

Massidda, L., & Marrocu, M. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121783 · Cited by 44 · see citing works

Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data

Gianniou, P., Reinhart, C. F., Hsu, D., Heller, A., & Rode, C. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.05.016 · Cited by 43 · see citing works

Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network

Tian, C., Ye, Y., Lou, Y., Zuo, W., Zhang, G., & Li, C. (2022) · Building Simulation · doi:10.1007/s12273-022-0887-y · Cited by 43 · see citing works

A systematic review of explainable artificial intelligence in urban building energy modeling: methods, applications, and future directions

Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42 · see citing works

Effects of Co-substitution on the reactivity of double perovskite oxides LaSrFe2-xCoxO6 for the chemical-looping steam methane reforming

Zhao, K., Chen, J., Li, H., Zheng, A., & He, F. (2018) · Journal of the Energy Institute · doi:10.1016/j.joei.2018.03.013 · Cited by 42 · see citing works

Transfer Learning Approach for Occupancy Prediction in Smart Buildings

Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. L. (2021) · International Renewable Energy Congress · doi:10.1109/irec51415.2021.9427869 · Cited by 42 · see citing works

A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches

Mohapatra, S. K., Mishra, S., Tripathy, H. K., Bhoi, A. K., & Barsocchi, P. (2021) · Energies · doi:10.3390/en14133900 · Cited by 41 · see citing works

Explaining Spatial Variations in Residential Energy Usage Intensity in Chicago: The Role of Urban Form and Geomorphometry

Li, C., Song, Y., Kaza, N., & Burghardt, R. (2019) · Journal of Planning Education and Research · doi:10.1177/0739456x19873382 · Cited by 41 · see citing works

Feature assessment frameworks to evaluate reduced-order grey-box building energy models

Shamsi, M. H., Ali, U., Mangina, E., & O’Donnell, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117174 · Cited by 40 · see citing works

Modelling occupant behaviour for urban scale simulation: Review of available approaches and tools

Doma, A., & Ouf, M. M. (2022) · Building Simulation · doi:10.1007/s12273-022-0939-3 · Cited by 39 · see citing works

New methods for clustering district heating users based on consumption patterns

Wang, C., Du, Y., Li, H., Wallin, F., & Min, G. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113373 · Cited by 39 · see citing works

An artificial intelligence framework for predicting operational energy consumption in office buildings

Golafshani, E. M., Chiniforush, A. A., Zandifaez, P., & Ngo, T. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114409 · Cited by 39 · see citing works

Comparing hyperparameter tuning methods in machine learning based urban building energy modeling: A study in Chicago

Quan, S. J. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114353 · Cited by 37 · see citing works

Predicting residential electricity consumption using aerial and street view images

Rosenfelder, M., Wussow, M., Gust, G., Cremades, R., & Neumann, D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117407 · Cited by 36 · see citing works

Building energy performance analysis at urban scale: A supporting tool for energy strategies and urban building energy rating identification

de Rubeis, T., Giacchetti, L., Paoletti, D., & Ambrosini, D. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103220 · Cited by 36 · see citing works

Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates

Oldenbroek, V., Smink, G., Salet, T., & van Wijk, A. (2019) · Applied Sciences · doi:10.3390/app10010143 · Cited by 35 · see citing works

Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period

(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.0831.20 · Cited by 34 · see citing works

Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules

Bianchi, C., Zhang, L., Goldwasser, D., Parker, A., & Horsey, R. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115470 · Cited by 34 · see citing works

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 · see citing works

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 · see citing works