Estimating residential energy consumption in metropolitan areas: A microsimulation approach

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

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · 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

A tutorial on support vector regression

Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works

Ridge regression: Biased estimation for nonorthogonal problems

Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648 · see citing works

Stochastic gradient boosting

Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works

Ridge Regression: Biased Estimation for Nonorthogonal Problems

Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 6,948 · see citing works

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 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 · see citing works

Contrasting the capabilities of building energy performance simulation programs

Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2006) · Building and Environment · doi:10.1016/j.buildenv.2006.10.027 · Cited by 1,867 · see citing works

The greenness of cities: Carbon dioxide emissions and urban development

Glaeser, E. L., & Kahn, M. E. (2009) · Journal of Urban Economics · doi:10.1016/j.jue.2009.11.006 · Cited by 1,257 · 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

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

Logistic Regression, AdaBoost and Bregman Distances

Collins, M., Schapire, R. E., & Singer, Y. (2002) · Machine Learning · doi:10.1023/a:1013912006537 · Cited by 686 · see citing works

Comparing High and Low Residential Density: Life-Cycle Analysis of Energy Use and Greenhouse Gas Emissions

Norman, J., MacLean, H. L., & Kennedy, C. A. (2006) · Journal of Urban Planning and Development · doi:10.1061/(asce)0733-9488(2006)132:1(10 · Cited by 613 · see citing works

Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

Kavousian, A., Rajagopal, R., & Fischer, M. A. (2013) · Energy · doi:10.1016/j.energy.2013.03.086 · Cited by 602 · 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

Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces

Wang, S., Fang, C., Guan, X., Pang, B., & Ma, H. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.09.059 · Cited by 468 · see citing works

Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks

Köksal, M. A., Ugursal, V. I., & Fung, A. S. (2002) · Applied Energy · doi:10.1016/s0306-2619(01)00049-6 · Cited by 302 · see citing works

Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks

Köksal, M. A., Ugursal, V. I., & Fung, A. S. (2004) · Applied Energy · doi:10.1016/j.apenergy.2003.12.006 · Cited by 236 · see citing works

A statistical analysis of a residential energy consumption survey study in Hangzhou, China

Chen, J., Wang, X., & Steemers, K. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.07.045 · Cited by 193 · see citing works

Long-term equilibrium relationship between urbanization, energy consumption and economic activity: Empirical evidence from India

Ghosh, S., & Kanjilal, K. (2014) · Energy · doi:10.1016/j.energy.2013.12.052 · Cited by 164 · see citing works

Urban Form and Residential Energy Use

Ko, Y. (2013) · Journal of Planning Literature · doi:10.1177/0885412213491499 · Cited by 141 · see citing works

Statistical Matching

Rässler, S. (2002) · Lecture notes in statistics · doi:10.1007/978-1-4613-0053-3 · Cited by 130 · see citing works

A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption

Tso, K. F., & Guan, J. (2014) · Energy · doi:10.1016/j.energy.2014.01.056 · Cited by 117 · see citing works

The geography of metropolitan carbon footprints

Brown, M. A., Southworth, F., & Sarzynski, A. (2009) · Policy and Society · doi:10.1016/j.polsoc.2009.01.001 · Cited by 115 · see citing works

Analysis and future outlook of natural gas consumption in the Italian residential sector

Bianco, V., Scarpa, F., & Tagliafico, L. A. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.07.081 · Cited by 109 · see citing works

Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure

Nichols, B. G., & Kockelman, K. M. (2014) · Energy Policy · doi:10.1016/j.enpol.2013.12.062 · Cited by 76 · see citing works

Towards more effective behavioural energy policy: An integrative modelling approach to residential energy consumption in Europe

Lopes, M. A. R., Antunes, C. H., & Martins, N. A. D. (2015) · Energy Research & Social Science · doi:10.1016/j.erss.2015.03.004 · Cited by 51 · see citing works

Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

Hargreaves, A. J., Cheng, V., Deshmukh, S. S., Leach, M. A., & Steemers, K. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.02.095 · Cited by 45 · see citing works

Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions

Konduri, K. C., You, D., Garikapati, V. M., & Pendyala, R. M. (2016) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2563-08 · Cited by 45 · see citing works

Targeting Residential Energy Assistance

Raffio, G. S., Isambert, O., Mertz, G. A., Schreier, C., & Kissock, K. (2007) · ASME International Conference on Energy Sustainability (ES) · doi:10.1115/es2007-36080 · Cited by 36 · see citing works

Impact of Urban Form on Energy Use in Central City and Suburban Neighborhoods: Lessons from the Phoenix Metropolitan Region

Guhathakurta, S., & Williams, E. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.594 · Cited by 19 · see citing works

Econometric models for major residential energy end-uses

Fung, A. S., Köksal, M. A., & Ugursal, V. I. (1999) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 15 · see citing works

Application of an Enhanced Population Synthesis Model that Accommodates Controls at Multiple Geographic Resolutions

Konduri, K. C., You, D., Garikapati, V. M., & Pendyala, R. M. (2016) · Cited by 13 · see citing works

Statistical Matching and Imputation of Survey Data with StatMatch

D’Orazio, M. (2012) · Cited by 10 · see citing works

Trends in Automobile Energy use and GHG Emissions in Suburban and Inner City Neighborhoods: Lessons from Metropolitan Phoenix, USA

Zhang, W., Guhathakurta, S., & Ross, C. L. (2016) · Energy Procedia · doi:10.1016/j.egypro.2016.06.028 · Cited by 7 · see citing works

Progress in Energy and Carbon Management in Large U.S. Metropolitan Areas

Brown, M. A., & Cox, M. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.600 · Cited by 6 · see citing works

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

Garikapati, V. M., You, D., Zhang, W., Pendyala, R. M., Guhathakurta, S., Brown, M. A., & Dilkina, B. (2017) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2668-01 · Cited by 5 · see citing works

Machine learning in energy economics and finance: A review

Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.05.006 · Cited by 443 · see citing works

High energy burden and low-income energy affordability: conclusions from a literature review

Brown, M. A., Soni, A., Lapsa, M. V., Southworth, K., & Cox, M. (2020) · Progress in Energy · doi:10.1088/2516-1083/abb954 · Cited by 221 · 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

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

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

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

Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change

Pimenow, S., Pimenowa, O., & Prus, P. (2024) · Energies · doi:10.3390/en17235965 · Cited by 111 · see citing works

Energy Consumption Prediction Using Machine Learning; A ReviewPreprint

Mosavi, A. H., & Bahmani, A. (2019) · Preprints.org · doi:10.20944/preprints201903.0131.v1 · Cited by 99 · see citing works

Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data

Shi, X., Wang, K., Cheong, T. S., & Zhang, H. (2020) · Energy Economics · doi:10.1016/j.eneco.2020.104942 · Cited by 97 · see citing works

The impact of urban compactness on energy-related greenhouse gas emissions across EU member states: Population density vs physical compactness

Xu, C., Haase, D., Su, M., & Yang, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113671 · Cited by 86 · 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

Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods

Amiri, S. S., Mueller, M., & Hoque, S. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112965 · Cited by 75 · see citing works

Exploring household emission patterns and driving factors in Japan using machine learning methods

Chen, P., Wu, Y., Zhong, H., Long, Y., & Meng, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118251 · Cited by 61 · see citing works

Roles of Artificial Intelligence and Machine Learning in Enhancing Construction Processes and Sustainable Communities

Kazeem, K. O., Olawumi, T. O., & Osunsanmi, T. O. (2023) · Buildings · doi:10.3390/buildings13082061 · Cited by 59 · see citing works

Advancing energy and well-being research

Rao, N. D., & Wilson, C. (2021) · Nature Sustainability · doi:10.1038/s41893-021-00775-7 · Cited by 58 · see citing works

Low-Income Energy Affordability: Conclusions From A Literature Review

Brown, M. A., Soni, A., Lapsa, M. V., & Southworth, K. (2020) · doi:10.2172/1607178 · Cited by 52 · see citing works

A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets

Peplinski, M., Dilkina, B., Chen, M., Silva, S. J., Ban-Weiss, G., & Sanders, K. T. (2024) · Applied Energy · doi:10.1016/j.apenergy.2023.122413 · Cited by 44 · see citing works

GEIN: An interpretable benchmarking framework towards all building types based on machine learning

Jin, X., Xiao, F., Zhang, C., & Li, A. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111909 · Cited by 49 · see citing works

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Hu, T., Wang, T., Yan, Q., Chen, T., Jin, S., & Hu, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119473 · Cited by 47 · see citing works

Temporal dynamic assessment of household energy consumption and carbon emissions in China: From the perspective of occupants

Su, S., Ding, Y., Li, G., Li, X., Li, H., Skitmore, M., & Menadue, V. (2023) · Sustainable Production and Consumption · doi:10.1016/j.spc.2023.02.014 · Cited by 45 · see citing works

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

di Stefano, A. G., Ruta, M. F., & Masera, G. (2023) · Applied Sciences · doi:10.3390/app132412981 · Cited by 45 · see citing works

Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery

Wang, J., & Lu, F. (2021) · Energy · doi:10.1016/j.energy.2021.121305 · Cited by 45 · 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

MOIRAE – bottom-up MOdel to compute the energy consumption of the Italian REsidential sector: Model design, validation and evaluation of electrification pathways

Besagni, G., Borgarello, M., Vilà, L. P., Najafi, B., & Rinaldi, F. (2020) · Energy · doi:10.1016/j.energy.2020.118674 · Cited by 31 · see citing works

Semi-supervised learning based framework for urban level building electricity consumption prediction

Jin, X., Xiao, F., Zhang, C., & Chen, Z. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120210 · Cited by 29 · see citing works

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel, H., & Shah, M. (2021) · SN Computer Science · doi:10.1007/s42979-021-00698-2 · Cited by 24 · see citing works

Energy footprint controlled by urban demands: How much does supply chain complexity contribute?

Chen, S., Zhu, F., Long, H., & Yang, J. G. (2019) · Energy · doi:10.1016/j.energy.2019.06.167 · Cited by 19 · see citing works

Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities

Ali, M., Prakash, K., Macana, C. A., Bashir, A. K., Jolfaei, A., Bokhari, A., Klemeš, J. J., & Pota, H. R. (2022) · Energies · doi:10.3390/en15062163 · Cited by 17 · see citing works

Extending and understanding: an application of machine learning to the World Bank's logistics performance index

Shepherd, B., & Sriklay, T. (2023) · International Journal of Physical Distribution & Logistics Management · doi:10.1108/ijpdlm-06-2022-0180 · Cited by 17 · see citing works

Of actors, cities and energy systems: advancing the transformative potential of urban electrification

Romero‐Lankao, P., Wilson, A. M., Sperling, J. B., Miller, C. A., Zimny-Schmitt, D., Sovacool, B. K., Gearhart, C., Muratori, M., Bazilian, M., Zünd, D., et al. (2021) · Progress in Energy · doi:10.1088/2516-1083/abfa25 · Cited by 17 · see citing works

Urban Electrification: Knowledge Pathway Toward an Integrated Research and Development AgendaPreprint

Romero‐Lankao, P., Wilson, A. M., Sperling, J. B., Miller, C. A., Zimny-Schmitt, D., Bettencourt, L. M. A., Wood, E. W., Young, S. E., Muratori, M., Arent, D. J., et al. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3440283 · Cited by 13 · see citing works

Population estimation beyond counts—Inferring demographic characteristics

Szarka, N., & Biljecki, F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266484 · Cited by 12 · see citing works

Machine Learning in Energy Economics and Finance: A ReviewPreprint

Creamer, G. G., Ghoddusi, H., & Rafizadeh, N. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3270251 · Cited by 13 · see citing works

Distribution transformer load behavior, burden, and characteristics of residential consumers: A case study of Baghdad City

Al-Bahrani, L. T., Seyedmahmoudian, M., Horan, B., & Stojcevski, A. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109693 · Cited by 7 · see citing works

Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map

Soltanisarvestani, A., & Safavi, A. (2021) · Utilities Policy · doi:10.1016/j.jup.2021.101251 · Cited by 6 · see citing works

Creating a Large-Scale National Residential Building Energy Dataset Using a Two-Stage Machine Learning Approach

Vosoughkhosravi, S., & Jafari, A. (2024) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784485279.032 · Cited by 5 · see citing works

A microsimulation of spatial inequality in energy access: A bayesian multi-level modelling approach for urban India

Neto-Bradley, A. P., Choudhary, R., & Challenor, P. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083211073140 · Cited by 5 · see citing works

Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application

di Stefano, A. G., Ruta, M. F., Masera, G., & Hoque, S. (2024) · Buildings · doi:10.3390/buildings14123866 · Cited by 5 · see citing works

Examining spatial variations in the relationship between domestic energy consumption and its driving factors using multiscale geographically weighted regression: a case study in Nottingham, England

Feng, Y., Miao, Y., & Turner, E. (2025) · Energy Sustainability and Society · doi:10.1186/s13705-025-00523-1 · Cited by 4 · see citing works

A novel temporal domain adaptation framework for residential electricity consumption forecasting under incomplete information

Li, S., Xu, X., Xu, Y., & Wu, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115513 · Cited by 4 · see citing works

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 · see citing works

Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation

di Stefano, A. G., Ruta, M. F., Masera, G., & Hoque, S. (2024) · ASME Journal of Engineering for Sustainable Buildings and Cities · doi:10.1115/1.4066565 · Cited by 4 · see citing works

Boosting Renewable Energy Technology Uptake in Ireland: A Machine Learning ApproachPreprint

Mukherjee, S. C. (2020) · RePEc: Research Papers in Economics · Cited by 4 · see citing works

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 · see citing works

A data-driven approach to discover hidden complicated relationships of energy variables and estimate energy consumption in U.S. homes

Choi, D., & Kim, C. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112175 · Cited by 3 · see citing works

Classifying Residential Electricity Demand in Mexico using Random Forest and Multinomial Logistic Regression

Hernandez, M. H., & Patiño‐Echeverri, D. (2019) · FISE-IEEE/CIGRE Conference - Living the Energy Transition (FISE/CIGRE) · doi:10.1109/fisecigre48012.2019.8984953 · Cited by 3 · see citing works

Exploring influential parameters affecting residential building energy use: advancing energy efficiency through machine learning

Chaturvedi, P. K., Kumar, N., Toftum, J., Andersen, R. K., Lamba, R., & Mehta, K. (2025) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-025-03301-x · Cited by 2 · see citing works

Analyzing seasonal and nonlinear built environment-energy dynamics using street view imagery in New York City

Lin, Q., Zhou, Y., Quan, S. J., & Guan, C. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107325 · Cited by 2 · see citing works

Simulating household energy behavior diffusion using spatial microsimulation and econometric models

Jian-hua, Z., Bouman, L., Ballas, D., & Liu, X. (2025) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102256 · Cited by 2 · see citing works

An AI-based microsimulation for predicting health outcomes among people experiencing homelessness

Blasco-Calafat, A., Blanes-Selva, V., Doñate-Martínez, A., Fragner, T., Borrás, T. A., Gawronska, J., Μουδάτσου, Μ., Tabaki, I., Belogianni, K., Karnaki, P., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109112 · Cited by 1 · see citing works

Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network

Zhang, Z., & Wang, Z. (2023) · Mathematics · doi:10.3390/math11204350

CSSI: A novel climatic-seismic severity index to assess the integrated energy-structural performance of existing RC buildings

Menna, C., d’Aragona, M. G., Mauro, G. M., Bianco, N. D., & Prota, A. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112024

Optimizing Urban Energy Efficiency Through a Machine Learning-Driven Framework: A Case Study in Reykjavik

di Stefano, A. G., Aliprandi, A., Viganò, R., Ruta, M. F., Masera, G., & Hoque, S. (2025) · Digital innovations in architecture, engineering and construction · doi:10.1007/978-3-031-87224-2_2

Deconstructing Household Energy Use: A Machine Learning ApproachPreprint

Ramanathan, A., & Ballard, T. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5351306

Evaluating the Impacts of Building Retrofits on U.S. Low-Income Households: A Data-Driven ApproachPreprint

Vosoughkhosravi, S., & Jafari, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5374376

Evaluating the Impacts of Building Retrofits on U.S. Low-Income Households: A Data-Driven ApproachPreprint

Vosoughkhosravi, S., & Jafari, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5374374

A Novel Approach for Prediction of Multiple Horizon Time Series Data Based on Decomposition and Integrated Hybrid Model for Electricity Demand Forecast: Data Driven Study on Developed and Developing CountryPreprint

omar, Z., & Liyanage, L. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5382326

The dual burden of energy poverty and climate exposure

Zhang, Y. (2026) · Energy Economics · doi:10.1016/j.eneco.2026.109560

Geography, income, and the incidence of a Massachusetts carbon tax

Sayre, S. S. (2026) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2026.103296

A novel framework for modeling monthly building energy consumption at the mesoscale community level during the zoning phase

Lin, Q., & Guan, C. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117237

The causal relationship between green technologies, green innovation and GDP growth

Maria, M. Y., Vitenu‐Sackey, P. A., & Li, W. (2026) · Journal of Applied Economics · doi:10.1080/15140326.2026.2685973

A hybrid physics-machine learning framework for improving simulations of urban air-conditioning electricity consumption

Deng, X., Cohen, J. J., Rodriguez, J., Svoma, B. M., Gonzales, C., & Georgescu, M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117982

Determinants of Energy Consumption in Residential, Commercial, and Industrial Sectors: A Systematic Literature Review

Bhusal, T., & Choudhury, A. (2026) · Sustainability · doi:10.3390/su18147470

Integrating Household Expenditure and Residential Energy Surveys: A Statistical-Matching WorkflowPreprint

Gutiérrez-Lopez, J. B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7445835

Evaluating the impacts of building retrofits on end-use energy consumption and retrofitting cost burden in US low-income households: a data-driven national assessment

Vosoughkhosravi, S., & Jafari, A. (2026) · Advances in Building Energy Research · doi:10.1080/17512549.2026.2733350

Core driving determinants of India’s steel demand: evidence from ARDL-ECM and machine learning cross-validation

Zhang, X., Fan, F., & Li, L. (2026) · Frontiers in Earth Science · doi:10.3389/feart.2026.1883390

A Generalizable Method for Estimating Household Energy by Neighborhoods in US Urban Regions

Zhang, W., Guhathakurta, S., Pendyala, R. M., Garikapati, V. M., & Ross, C. L. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.12.774 · Cited by 6 · see citing works

A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE

Chingcuanco, F., & Miller, E. J. (2011) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2011.11.005 · Cited by 43 · see citing works

Spatial microsimulation for residential energy consumption, a case study of Scotland

Alvarez-Gallo, D., Neto-Bradley, A. P., Demšar, U., & Benitez‐Paez, F. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20181503

Open Data Model for Estimating Regional to Town Scale Residential Energy ConsumptionPreprint

García-López, J. M., Domínguez-Amarillo, S., & Salas, J. J. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4578449

An urban planning method for modelling energy use with application to selected Canadian cities

Regenstreif, A. (1987) · Mspace (University of Manitoba)