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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Adam: A Method for Stochastic Optimization

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

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Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741

Large-Scale Machine Learning with Stochastic Gradient Descent

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United Nations Environment Programme

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Traffic Flow Prediction With Big Data: A Deep Learning Approach

Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

Zhang, J., Zheng, Y., & Qi, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10735 · Cited by 2,452

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

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017) · Sensors · doi:10.3390/s17040818 · Cited by 1,417

Advances in smart cities with system integration and energy digitalization technologies: A state-of-the-art review

Kong, J., Dong, Y., Zhang, Z. F., Yap, P., & Zhou, Y. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.104012 · Cited by 25

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

Quan, S. J., Xue, Y., & Li, C. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125344 · Cited by 21

A combined workflow to generate citywide building energy demand profiles from low-level datasets

Guo, T., Bachmann, M., Kersten, M., & Kriegel, M. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104694 · Cited by 21

Adopting explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates

Eshraghi, P., Talami, R., Dehnavi, A. N., Mirdamadi, M. S., & Zomorodian, Z. S. (2025) · Advances in Building Energy Research · doi:10.1080/17512549.2025.2513280 · Cited by 20

Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis

Saad, M. M., Menon, R. P., & Eicker, U. (2023) · Energies · doi:10.3390/en16166030 · Cited by 20

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

Shi, Q., Liu, C., & Xiao, C. (2022) · Frontiers of Engineering Management · doi:10.1007/s42524-021-0181-1 · Cited by 19

A bottom-up approach for community load prediction based on multi-agent model

Yu, Z., Song, C., Liu, Y., Wang, D., & Li, B. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104774 · Cited by 19

Enhancing hourly heat demand prediction through artificial neural networks: A national level case study

Zhang, M., Millar, M.-A., Chen, S., Ren, Y., Yu, Z., & Yu, J. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100315 · Cited by 19

Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning

Zhou, S., Lin, M., Huang, S., & Xiao, K. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123603 · Cited by 19

Building simulation in adaptive training of machine learning models

Amini, H., Alanne, K., & Kosonen, R. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105564 · Cited by 19

Methodology developed for the construction of an urban-energy diagnosis aimed to assess alternative scenarios: An intra-urban approach to foster cities’ sustainability

Chévez, P. J., Martini, I., & Díscoli, C. A. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.037 · Cited by 19

DeepRadiation: An intelligent augmented reality platform for predicting urban energy performance just through 360 panoramic streetscape images utilizing various deep learning models

Nakhaee, A., & Paydar, A. (2022) · Building Simulation · doi:10.1007/s12273-022-0953-5 · Cited by 18

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Bass, B. C., New, J., & Copeland, W. (2020) · Energies · doi:10.3390/en14010132 · Cited by 18

Investigating the reliability of building energy models: Comparative analysis of the impact of data pipelines and model complexities

Saad, M. M., & Eicker, U. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106511 · Cited by 17

Climate-neutral municipal building stock - life cycle assessment of large residential building stocks based on semantic 3D city models

Harter, H., Willenborg, B., Lang, W., & Kolbe, T. H. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113141 · Cited by 17

Community stochastic domestic electricity forecasting

Amin, A., & Mourshed, M. M. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122342 · Cited by 19

Building energy performance metamodels for district energy management optimisation platforms

Sánchez, V., & Gómez-Acebo, T. (2023) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2023.100512 · Cited by 20

Methods for calculating building-embodied carbon emissions for the whole design process

Lu, M. F., Luo, Z., Cang, Y., Zhang, N., & Yang, L. (2024) · Fundamental Research · doi:10.1016/j.fmre.2022.07.015 · Cited by 17

Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California

Flores, R. J., Houssainy, S., Wang, W., Cu, K. N., Nie, X., Woolfolk, N., Polly, B., Faramarzi, R., Maclay, J., Lee, J., et al. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100169 · Cited by 16

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Kumar, D., Bassill, N. P., & Ghosh, S. (2024) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2024-0014 · Cited by 16

Ten questions concerning the design of urban energy systems

Koirala, B. P., Bendiek-Laranjo, A., Biéron, M., Chen, Y. C., Humbert, G., Hunhevicz, J., Mutschler, R., Obrist, M., Romano, E., Vulic, N., et al. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113348 · Cited by 15

A procedure set to construct the optimal energy saving retrofit strategy for old residential buildings in China

Wang, P., Ji, C., Yu, P., & Huang, L. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0134055 · Cited by 15

A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing

Ye, C., Ding, Y., Song, Y., Lin, Z., & Wang, L. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.202 · Cited by 14

Data-driven prosumer-centric energy scheduling using convolutional neural networks

Hua, W., Jiang, J. S., Sun, H., Tonello, A. M., Qadrdan, M., & Wu, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118361 · Cited by 13

SHAPE: A temporal optimization model for residential buildings retrofit to discuss policy objectives

Rit, M., Thomas, A., Villot, J., Thorel, M., Garreau, E., & Robin, G. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122936 · Cited by 13

E-Audit: A “no-touch” energy audit that integrates machine learning and simulation

Excell, L. E., Andrews, A., & Jain, R. K. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114360 · Cited by 13

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

Instant infrared: Estimating urban surface temperatures from street view imagery

Klimenka, M., Zhao, K., Hilland, R., Zhang, F., Voogt, J., & Ratti, C. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112122 · Cited by 12

A machine learning approach assisting soot radiation-based thermometry to recover complete flame temperature field in a laminar flame

Wang, Q., Li, Z., Li, C., Liu, H., & Ren, T. (2021) · Applied Physics B · doi:10.1007/s00340-021-07591-z · Cited by 12

Review of temperature profile inversion of satellite-borne infrared hyperspectral sensors

Cao, X., Xiaoying, L. I., Qi, L. U. O., Liu, S., Li, P., & Xin, L. (2021) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210009 · Cited by 13

Physics-Guided Memory Network for building energy modeling

Danish, M. U., Ali, K., Siddiqui, K., & Grolinger, K. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100538 · Cited by 11

A novel entropy-based method for quantifying urban energy demand aggregation: Implications for urban planning and policy

Wang, R., Liu, X., Zhao, X., Cheng, X., & Qiu, H. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105284 · Cited by 11

Synergistic optimization framework for office building energy efficiency and natural daylighting based on multi-objective optimization algorithms and intelligent Decision Support

Tang, Z., Zhang, L., Yan, H., Jiang, L., Wang, S., & Qi, Z. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.113517 · Cited by 11

Configurations of renewable power generation in cities using open source approaches: With Philadelphia case study

Ranalli, J. A., & Alhamwi, A. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115027 · Cited by 11

Predicting Electricity Consumption in Microgrid-Based Educational Building Using Google Trends, Google Mobility, and COVID-19 Data in the Context of COVID-19 Pandemic

Wasesa, M., Andariesta, D. T., Afrianto, M. A., Haq, I. N., Pradipta, J., Siallagan, M., Leksono, E., Iskandar, B. P., & Putro, U. S. (2022) · IEEE Access · doi:10.1109/access.2022.3161654 · Cited by 13

A Novel Graph Neural Network for Zone-Level Urban-Scale Building Energy Use Estimation

Halaçlı, E. G., Canlı, İ., İşeri, O. K., Yavuz, F., Akgül, Ç. M., Kalkan, S., & Dino, İ. G. (2023) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3600100.3623747 · Cited by 10

Implementation of BIM Data in CityGML—Research and Perspectives for Creating a QGIS Plugin for Spatial Analysis: Experience from Poland

Janisio-Pawłowska, D., & Pawłowski, W. (2024) · Sustainability · doi:10.3390/su16020642 · Cited by 10

ResNet diagnosis of rotor faults in oil transfer pumps

Chen, L., Dong, L., Wu, Z., Fan, C., Shi, W.-H., Li, H., Hua, R., & Dai, C. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36170 · Cited by 10

A data-driven framework for urban morphology optimization: integrating energy efficiency and environmental quality

Eshraghi, P., Zomorodian, Z. S., & Asl, B. M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116241 · Cited by 10

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

Geodesign in the era of artificial intelligence

Ye, X., Huang, T., Song, Y. H., Li, X. S., Newman, G., Lin, Z., & Wu, D. J. (2025) · Frontiers of Urban and Rural Planning · doi:10.1007/s44243-025-00054-5 · Cited by 10

AI-driven short-term load forecasting enhanced by clustering in multi-type university buildings: Insights across building types and pandemic phases

Hu, Y.-S., Lo, K.-Y., & Hsieh, I.-Y. L. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112417 · Cited by 9

Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials

Guo, Y., Shi, J., Guo, T., Guo, F., Lu, F., & Su, L. (2024) · Energies · doi:10.3390/en17215463 · Cited by 8

Synthetic data generation and energy consumption prediction in district building energy modeling

Bolluk, S., Aydoğan, R., Sefer, E., Kalaycıoğlu, E., & Seyıs, S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116716 · Cited by 8

Exploring the Integration of Simulation and Deep Learning Models for Urban Building Energy Modeling and Retrofit Analysis

Nutkiewicz, A., & Jain, R. K. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210264 · Cited by 8

Energy consumption prediction by modified fish migration optimization algorithm: City single-family homes

Xiong, S., Li, Y., Li, Q., Ye, Z., & Pouramini, S. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122065 · Cited by 8

Operational day-ahead load forecasting using graph-based data-driven urban building energy modeling

Kim, J., Yeom, S., Ahn, S., Seo, S., Koo, C., & Hong, T. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114384 · Cited by 7

Urban revitalization pathways toward zero carbon emissions through systems architecting of urban digital twins

Ramnarine, I. D., Sherif, T., Alorabi, A. H., Helmy, H., Yoshida, T., Murayama, A., & Yang, P. P. (2025) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083251318142 · Cited by 10

Integration of SLAM with GIS to model sustainable urban transportation system: A smart city perspective

Ghosh, K., & Musti, K. S. S. (2020) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn49253.2020.9242571 · Cited by 7

Quality Control Methods for Advanced Metering Infrastructure Data

Garrison, E., & New, J. (2021) · Smart Cities · doi:10.3390/smartcities4010012 · 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

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

A review on the applicability of machine learning techniques to the metamodeling of energy systems

Starke, A. R., & da Silva, A. K. (2023) · Numerical Heat Transfer Part B Fundamentals · doi:10.1080/10407790.2023.2280208 · Cited by 6

Gaussian-based plug load profile prediction in non-residential buildings archetype

Dabirian, S., Miller, C., Adli, A., & Eicker, U. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123970 · Cited by 5

Estimating the influence of building and urban form on the thermal loads of urban dwellings in the Mediterranean climate using machine learning

Vartholomaios, A., Chatzidimitriou, A., & Ioannidis, K. (2021) · Energy Sources Part B Economics Planning and Policy · doi:10.1080/15567249.2021.1916796 · Cited by 6

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

Yakut, M. Z., & Esen, S. (2023) · International Journal of Energy Studies · doi:10.58559/ijes.1228599 · Cited by 6

Data-driven framework with theoretical modelling to evaluate fuel savings through air lubrication system

Kim, D., & Rhee, S. H. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119920 · Cited by 5

Development of representative city region models for south China’s Pearl River Delta: Data statistics and model definition

Lou, S., Huang, C., Zou, Y., & Huang, Y. J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115653 · Cited by 5

Urban Building Modeling: A Comprehensive Review of Inputs, Outputs, and Tools for Sustainable Development

Eshraghi, P., Korsavi, S. S., Ghahramani, A., & Zomorodian, Z. S. (2025) · Environmental Modeling & Assessment · doi:10.1007/s10666-025-10057-7 · Cited by 5

Building geometry data from online maps for accurate thermal simulations of districts

Rodríguez, L. R., Ramos, J. S., & Domínguez, S. Á. (2023) · Building Simulation · doi:10.1007/s12273-023-1046-9 · Cited by 5

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

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional NetworkPreprint

Hu, Y., Cheng, X., Wang, S., Chen, J., Zhao, T., & Dai, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13399 · Cited by 5

Machine Learning in Urban Building Energy Modeling

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

Active and passive cooling approaches for a Southern California residential community

Flores, R. J., Cu, K. N., Houssainy, S., Wang, W., Nie, X., Woolfolk, N., Polly, B., Faramarzi, R., Maclay, J., Lee, J., et al. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100234 · Cited by 4

DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems

Yang, Z., Gupta, K., & Jain, R. K. (2019) · Energy Procedia · doi:10.1016/j.egypro.2019.01.114 · Cited by 4

Household, sociodemographic, building and land cover factors affecting residential summer electricity consumption: A systematic statistical study in Phoenix, AZ

Ramírez-Aguilar, E. A., Sailor, D. J., & Wentz, E. A. (2024) · Energy · doi:10.1016/j.energy.2024.133819 · Cited by 4

SCHMEAR

Dougherty, T. R., Huang, T., Chen, Y., Jain, R. K., & Rajagopal, R. (2021) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3486611.3486666 · Cited by 4

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

Urban building energy modeling : A systematic evaluation of modeling and simulation approaches

Johari, F. (2021) · Uppsala University Publications (Uppsala University) · Cited by 4

Urban-scale building energy modeling under future climate scenarios: a scalable workflow and insights from Nassau County, New York

Jalilian, R., & Kamel, E. (2025) · Frontiers in Energy Research · doi:10.3389/fenrg.2025.1683787 · Cited by 4

Deep temporal convolutional residual neural network (ResNet)–based surrogate models for time-series load prediction and fast demand response evaluation across buildings and climates

Qin, Q., & Evins, R. (2026) · Energy Conversion and Management · doi:10.1016/j.enconman.2026.121278 · Cited by 3

Evaluating strategic retrofit measures for energy-efficient residential buildings with artificial intelligence

Werner, T., Konhäuser, K., & Schwarz, N. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117205 · Cited by 3

Machine learning for optimized buildings morphosis

Raboudi, K., & Saci, A. B. (2020) · International Conference on Digital Tools & Uses Congress (DTUC) · doi:10.1145/3423603.3424057 · Cited by 3

Exploring the Potential of Digital Twins at the District Scale: a Framework for Investigation

Odeh, K., & de Wilde, P. (2023) · Building Simulation Conference proceedings · doi:10.26868/25222708.2023.1201 · 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

Time-series building energy use data inventory framework with an automated approach

Jeon, S., & Kim, S. (2025) · Solar Energy · doi:10.1016/j.solener.2025.114142 · 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

Methodology for integrated building energy modeling and impact assessment of urban energy system scenarios

Nakkorn, N., Phdungsilp, A., & Vorarat, S. (2022) · International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE) · doi:10.1109/icue55325.2022.10113539 · Cited by 2

AI-Integrated Urban Building Energy Simulation: A framework to forecast the morphological impact on daylight availability

Aman, J., Kim, J. B., & Verniz, D. (2023) · eCAADe proceedings · doi:10.52842/conf.ecaade.2023.2.369 · Cited by 2

Data-Driven Research on Energy-Efficient Retrofit and Multi-Objective Optimization of Urban Building Clusters

Luo, L., Wu, J., Lin, Z., Wei, H., & Wang, W. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202456205007 · Cited by 2

A generalizability analysis of a data-driven method for the Urban Heat Island phenomenon assessment

Acosta, M. P., Vahdatikhaki, F., dos Santos, J. O., Hammad, A., & Dorée, A. G. (2021) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2021/0012 · Cited by 2

Evaluating Energy Performance Certificate Data with Data Science

Anastasiadou, M., Santos, V., & Dias, M. S. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698806 · Cited by 2

Review of Uncertainties in Building Characterization for Urban-Scale Energy Modeling

Bolluk, S., & Seyıs, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-16895-6_11 · Cited by 1

Integration of building archetypes and typological urban forms to assess energy performance and thermal comfort: Proposal of suitable parameters for Latin America

Guillén-Mena, V., Martín-Escudero, K., & Irulegi, O. (2024) · Journal of Building Physics · doi:10.1177/17442591241300709 · Cited by 1

Enhancing building energy performance prediction: A fusion of deep learning and first-principles simulation methods

Shirzadi, N., Gilani, S., & Stylianou, M. (2025) · Journal of Building Physics · doi:10.1177/17442591251333433 · Cited by 1

Machine learning and artificial intelligence for energy optimization and sustainability

Zafar, M. B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29293-4.00011-5 · 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

Limitations of machine learning for building energy predictionPreprint

Miller, C. C., Picchetti, B., Fu, C., & Pantelic, J. (2021) · arXiv (Cornell University) · Cited by 1

Emerging Trends of Urban Energy Systems and Management

Kumar, D. (2023) · doi:10.1002/9781119847595.ch1 · Cited by 1

Instant Infrared: Estimating Urban Surface Temperatures from Street View ImageryPreprint

Klimenko, N., Zhao, K., Hilland, R., Zhang, F., Voogt, J., & Ratti, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4647634 · Cited by 1

ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles

Quintana, M., Stoeckmann, T., Park, J. Y., Turowski, M., Hagenmeyer, V., & Miller, C. C. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112096 · Cited by 11

Urban-scale Building Energy Consumption Platform: A Case Study and An Interface for Wuhan

Ding, C., Feng, W., & Qin, T. L. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.211330

Case Machine Learning Experience into Modelling and Simulation to Improve the Efficiency of Construction Engineering Process Management

Alanya-Beltrán, J., Kanwer, B., Buddhi, D., Jeelani, S. H., Valderrama-Zapata, C., & Jaiswal, S. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823866

HULCGIS: a new web-based tool for the accurate thermal simulation of buildings and districts

Rodríguez, L. R., Ramos, J. S., & Domínguez, S. Á. (2022) · International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED) · doi:10.1109/synergymed55767.2022.9941386

Building's Energy Consumption Prediction with Limited Historical Data via AR-RNN

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