Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
Machine learning (ML) has emerged as a transformative force in smart building management due to its ability to significantly enhance energy efficiency and promote sustainability within the built environment. This review examines the pivotal role of ML in optimizing building operations through the ap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7682 |
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| author | Fatema El Husseini Hassan N. Noura Ola Salman Khaled Chahine |
| author_facet | Fatema El Husseini Hassan N. Noura Ola Salman Khaled Chahine |
| author_sort | Fatema El Husseini |
| collection | DOAJ |
| description | Machine learning (ML) has emerged as a transformative force in smart building management due to its ability to significantly enhance energy efficiency and promote sustainability within the built environment. This review examines the pivotal role of ML in optimizing building operations through the application of predictive analytics and sophisticated automated control systems. It explores the diverse applications of ML techniques in critical areas such as energy forecasting, non-intrusive load monitoring (NILM), and predictive maintenance. A thorough analysis then identifies key challenges that impede widespread adoption, including issues related to data quality, privacy concerns, system integration complexities, and scalability limitations. Conversely, the review highlights promising emerging opportunities in advanced analytics, the seamless integration of renewable energy sources, and the convergence with the Internet of Things (IoT). Illustrative case studies underscore the tangible benefits of ML implementation, demonstrating substantial energy savings ranging from 15% to 40%. Future trends indicate a clear trajectory towards the development of highly autonomous building management systems and the widespread adoption of occupant-centric designs. |
| format | Article |
| id | doaj-art-1eec0f488d2f4e2fb08c35568c730192 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1eec0f488d2f4e2fb08c35568c7301922025-08-20T03:13:44ZengMDPI AGApplied Sciences2076-34172025-07-011514768210.3390/app15147682Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future TrendsFatema El Husseini0Hassan N. Noura1Ola Salman2Khaled Chahine3LISTIC, Université Savoie Mont Blanc, 74944 Annecy, Cedex, FranceIUT-NFC, National Council for Scientific Research (CNRS), Institut FEMTO-ST, Université Marie et Louis Pasteur, 90000 Belfort, FranceDeepVu, Berkeley, CA 94704, USACollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitMachine learning (ML) has emerged as a transformative force in smart building management due to its ability to significantly enhance energy efficiency and promote sustainability within the built environment. This review examines the pivotal role of ML in optimizing building operations through the application of predictive analytics and sophisticated automated control systems. It explores the diverse applications of ML techniques in critical areas such as energy forecasting, non-intrusive load monitoring (NILM), and predictive maintenance. A thorough analysis then identifies key challenges that impede widespread adoption, including issues related to data quality, privacy concerns, system integration complexities, and scalability limitations. Conversely, the review highlights promising emerging opportunities in advanced analytics, the seamless integration of renewable energy sources, and the convergence with the Internet of Things (IoT). Illustrative case studies underscore the tangible benefits of ML implementation, demonstrating substantial energy savings ranging from 15% to 40%. Future trends indicate a clear trajectory towards the development of highly autonomous building management systems and the widespread adoption of occupant-centric designs.https://www.mdpi.com/2076-3417/15/14/7682smart building managementenergy efficiencypredictive analyticsnon-intrusive load monitoringenergy forecasting |
| spellingShingle | Fatema El Husseini Hassan N. Noura Ola Salman Khaled Chahine Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends Applied Sciences smart building management energy efficiency predictive analytics non-intrusive load monitoring energy forecasting |
| title | Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends |
| title_full | Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends |
| title_fullStr | Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends |
| title_full_unstemmed | Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends |
| title_short | Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends |
| title_sort | machine learning in smart buildings a review of methods challenges and future trends |
| topic | smart building management energy efficiency predictive analytics non-intrusive load monitoring energy forecasting |
| url | https://www.mdpi.com/2076-3417/15/14/7682 |
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