Leveraging machine learning for data-driven building energy rate prediction
This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER....
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025010072 |
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| author | Nasim Eslamirad Mehdi Golamnia Payam Sajadi Francesco Pilla |
| author_facet | Nasim Eslamirad Mehdi Golamnia Payam Sajadi Francesco Pilla |
| author_sort | Nasim Eslamirad |
| collection | DOAJ |
| description | This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER. Our approach leverages cutting-edge ML techniques, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), to develop highly accurate predictive models. The performance of these models was rigorously evaluated using comprehensive statistical metrics, such as Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), precision, recall, and overall accuracy (OA). The results demonstrate that the RF model outperformed other algorithms, achieving the highest classification accuracy, with AUC values ranging from 0.77 to 0.83. Notably, the model exhibited superior performance in classifying higher energy-consuming buildings (Class G), while the SVM showed limited discriminative power with AUC values between 0.37 and 0.49. These findings underscore the effectiveness of ML in Urban Building Energy Modelling (UBEM), particularly in forecasting energy consumption patterns and identifying high-energy-use buildings. This study makes significant contributions by advancing the application of ML to urban energy efficiency planning. By aligning the Building Energy Rating (BER) prediction framework with European energy performance standards (ISO/CEN), it ensures adaptability and relevance across diverse urban contexts. The approach addresses key limitations in UBEM while offering a robust tool for policymakers and urban planners to optimize energy consumption and reduce carbon emissions. Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research. |
| format | Article |
| id | doaj-art-7cf0f947f19b42dea030a7b4644142f7 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-7cf0f947f19b42dea030a7b4644142f72025-08-20T03:14:30ZengElsevierResults in Engineering2590-12302025-06-012610493110.1016/j.rineng.2025.104931Leveraging machine learning for data-driven building energy rate predictionNasim Eslamirad0Mehdi Golamnia1Payam Sajadi2Francesco Pilla3School of Architecture Planning and Environmental Policy, University College Dublin, Ireland; Department of Civil Engineering & Architecture, Tallinn University of Technology, Tallinn, Estonia; Correspond author.School of Architecture Planning and Environmental Policy, University College Dublin, IrelandSchool of Architecture Planning and Environmental Policy, University College Dublin, IrelandSchool of Architecture Planning and Environmental Policy, University College Dublin, IrelandThis paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER. Our approach leverages cutting-edge ML techniques, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), to develop highly accurate predictive models. The performance of these models was rigorously evaluated using comprehensive statistical metrics, such as Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), precision, recall, and overall accuracy (OA). The results demonstrate that the RF model outperformed other algorithms, achieving the highest classification accuracy, with AUC values ranging from 0.77 to 0.83. Notably, the model exhibited superior performance in classifying higher energy-consuming buildings (Class G), while the SVM showed limited discriminative power with AUC values between 0.37 and 0.49. These findings underscore the effectiveness of ML in Urban Building Energy Modelling (UBEM), particularly in forecasting energy consumption patterns and identifying high-energy-use buildings. This study makes significant contributions by advancing the application of ML to urban energy efficiency planning. By aligning the Building Energy Rating (BER) prediction framework with European energy performance standards (ISO/CEN), it ensures adaptability and relevance across diverse urban contexts. The approach addresses key limitations in UBEM while offering a robust tool for policymakers and urban planners to optimize energy consumption and reduce carbon emissions. Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research.http://www.sciencedirect.com/science/article/pii/S2590123025010072Urban building energy modelling (UBEM)Urban energy analysisData-driven approachMachine learning classification modelsBuilding energy rate (BER) |
| spellingShingle | Nasim Eslamirad Mehdi Golamnia Payam Sajadi Francesco Pilla Leveraging machine learning for data-driven building energy rate prediction Results in Engineering Urban building energy modelling (UBEM) Urban energy analysis Data-driven approach Machine learning classification models Building energy rate (BER) |
| title | Leveraging machine learning for data-driven building energy rate prediction |
| title_full | Leveraging machine learning for data-driven building energy rate prediction |
| title_fullStr | Leveraging machine learning for data-driven building energy rate prediction |
| title_full_unstemmed | Leveraging machine learning for data-driven building energy rate prediction |
| title_short | Leveraging machine learning for data-driven building energy rate prediction |
| title_sort | leveraging machine learning for data driven building energy rate prediction |
| topic | Urban building energy modelling (UBEM) Urban energy analysis Data-driven approach Machine learning classification models Building energy rate (BER) |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025010072 |
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