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

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A Forecasting Approach for IoT-Based Energy and Power Quality Monitoring in Buildings

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Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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Statistical and machine learning approaches for energy efficient buildings

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MEBA: AI-powered precise building monthly energy benchmarking approach

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Data-driven optimization for mitigating energy consumption and GHG emissions in buildings

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Anomalous load profile detection in power systems using wavelet transform and robust regression

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Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

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A data-driven framework for abnormally high building energy demand detection with weather and block morphology at community scale

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The Impact of Urban Form and Density on Residential Energy Use: A Systematic Review

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Development and testing of an IoT platform with smart algorithms for building energy management systems

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Machine learning-based energy use prediction for the smart building energy management system

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Multi-objective optimal energy-efficient retrofit determination using hybrid urban building energy model: Considering uncertainties between models

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Short-term power load forecasting for industrial buildings based on decomposition reconstruction and TCN-Informer-BiGRU

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Enabling Demand Side Management: Heat Demand Forecasting at City Level

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Developing machine-learning meta-models for high-rise residential district cooling in hot and humid climate

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Are European cities achieving emission reduction commitments? A comparative analysis under the Covenant of Mayors initiative

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Research on Large-Scale Building Energy Efficiency Retrofit Based on Energy Consumption Investigation and Energy-Saving Potential Analysis

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Nonlinearity in the relationships between urban form and residential energy use intensity

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Urban electricity–temperature relationships in the Tokyo Metropolitan Area

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Development and calibration of apartment building energy model based on architectural and energy consumption characteristics

Lee, R., Kim, D., Yoon, J.-H., Kang, E., Cho, H., & Kim, J. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114874 · Cited by 20

Interrelationships between electricity, gas, and water consumption in large‐scale buildings

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Statistical and Artificial Intelligence-Based Tools for Building Energy Prediction: A Systematic Literature Review

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Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms

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Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods

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Machine learning in building energy management: A critical review and future directions

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Identification of undocumented buildings in cadastral data using remote sensing: Construction period, morphology, and landscape

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Machine learning based prediction of long-term energy consumption and overheating under climate change impacts using urban building energy modeling

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Infrastructure‐scale sustainable energy planning in the cityscape: Transforming urban energy metabolism in East Asia

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A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

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Spatially heterogeneous effect of temperature on electricity consumption in Shenzhen, China

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Building energy performance metamodels for district energy management optimisation platforms

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Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning

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Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale BuildingsPreprint

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E-Waste Management Using Machine Learning

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Developing a Building Stock Model to Enable Clustered Renovation—The City of Leuven as Case Study

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Urban building energy modelling-based framework to analyze the effectiveness of the community-wide implementation of national energy conservation codes

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A Machine Learning Pipeline for Demand Response Capacity Scheduling

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Towards an in-situ evaluation methodology of thermal resistance of basement walls in buildings

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Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data

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Distribution patterns of energy consumed in classified public buildings through the data mining process

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Use of publicly available data to create a dataset for data-driven urban commercial building energy intensity classification: Model accuracy, interpretation, and implications of an open data framework in Hong Kong

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From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data

Abbasabadi, N., & Ashayeri, M. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114440 · Cited by 13

Technological advancement, sectoral growth, and electricity consumption in Ghana

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A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

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FMM-VMD-Transformer: A hybrid deep learning model for predicting natural gas consumption

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Structural change and energy use in Ghana’s manufacturing and agriculture sectors

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Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis

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AI-driven multi-algorithm optimization for enhanced building energy benchmarking

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Spatial and temporal characteristics of carbon emission and sequestration of terrestrial ecosystems and their driving factors in mainland China—a case study of 352 prefectural administrative districts

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Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings

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Personalized Federated Transfer Learning for Building Energy Forecasting via Model Ensemble with Multi-Level Masking in Heterogeneous Sensing Environment

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Developing and tuning a community scale energy model for a disadvantaged community

Flores, R. J., Houssainy, S., Wang, W., Robertson, J., Cu, K. N., Polly, B., Faramarzi, R., Maclay, J., & Brouwer, J. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112861 · Cited by 10

Extracting principal building variables from automatically collected urban scale façade images for energy conservation through deep transfer learning

Yu, X., Zou, Z., & Ergan, S. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121228 · Cited by 10

Linking personality traits to behavior-based intervention: Empirical evidence from Hangzhou, China

Shen, M., Li, X., Song, X., & Lu, Y. (2022) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2022.106796 · Cited by 10

Predicting Smart Building Occupancy Using Machine Learning

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City-scale residential energy consumption prediction with a multimodal approach

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Prediction and strategies of buildings’ energy consumption: A review of modeling approaches and energy-saving technologies

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Learning from other cities: Transfer learning based multimodal residential energy prediction for cities with limited existing data

Sheng, Y., Arbabi, H., Ward, W. O. C., & Mayfield, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115723 · Cited by 9

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Chang, C., Chen, C., & Lin, T. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105949 · Cited by 8

Assessing urban morphology effects on residential building electricity consumption via explainable machine learning: Evidence from China’s hot summer and warm winter zone

Liu, G., Zheng, Y., Wu, X., Che, Y., Zhang, H., Gao, J., & Liu, X. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116063 · Cited by 7

A method to develop residential archetypes by associating thermophysical building attributes with utility meter data

Nojedehi, P., Gunay, B., O’Brien, W., & Papineau, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116351 · Cited by 7

Determinants of Electricity Consumption of Energy-Vulnerable Group Using Ensemble Gradient-Boosting Algorithm

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Leveraging online housing data for large-scale building energy modeling

Yang, Y.-Z., Adhikari, R., Sui, J.-Y., Lou, Y., Ye, Y.-Y., O’Donnell, J., Hewitt, N., & Zuo, W.-D. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112929 · Cited by 6

Understanding Technology, Fuel, Market and Policy Drivers for New York State’s Power Sector Transformation

Işık, M., & Kaplan, P. O. (2020) · Sustainability · doi:10.3390/su13010265 · Cited by 7

Campus Energy Use Prediction (CEUP) Using Artificial Intelligence (AI) to Study Climate Change Impacts

Fathi, S., Srinivasan, R. S., & Ries, R. J. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210874 · Cited by 7

Heating Energy Balance and Biomass Consumption for the Residential Sector in the Po Valley

Marigo, M., Zulli, F., Pillon, S., Susanetti, L., & Carli, M. D. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102814 · Cited by 7

Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data

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Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction

Ali, U., Shamsi, M. H., Hoare, C., Alshehri, F., Mangina, E., & O’Donnell, J. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210232 · Cited by 7

A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond

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Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles

Al-Madani, M. H. M., Fernando, Y., & Tseng, M. (2022) · Sustainability · doi:10.3390/su142215405 · Cited by 7

Data-Driven Energy and Population Estimation for Real-Time City-Wide Energy Footprinting

Wei, P., & Jiang, X. (2019) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3360322.3360847 · Cited by 7

Comparing MLR and ANN models for school building electrical energy prediction in Osijek-Baranja County in Croatia

Begić, H., & Krstić, H. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.09.039 · Cited by 6

EnergyNet: A modality-aware attention fusion network for building energy efficiency classification

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Ensuring real-time data integrity in smart building applications: A systematic end-to-end comprehensive pipeline evaluated in numerous real-life cases

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Bayesian Network for Predicting Energy Consumption in Schools in Florianópolis – Brazil

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A comprehensive survey of the urban building energy modeling (UBEM) process and approaches

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Visual special issue: Low carbon development and transformation of cities

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