Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
This review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability cha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/8/1/5 |
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| _version_ | 1850081391262302208 |
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| author | Amjad Baset Muhyiddine Jradi |
| author_facet | Amjad Baset Muhyiddine Jradi |
| author_sort | Amjad Baset |
| collection | DOAJ |
| description | This review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability challenges, fostering the broader adoption of data-driven solutions among stakeholders. A critical contribution of this review is its in-depth analysis of innovative applications of AI techniques to handle incomplete data, optimize energy performance, and predict retrofit outcomes with enhanced accuracy. Furthermore, the review identifies previously underexplored areas, such as scaling data-driven methods to diverse building typologies and incorporating future climate scenarios in retrofit planning. Future research directions include improving data availability and quality, developing scalable urban simulation tools, advancing modeling techniques to include life-cycle impacts, and creating practical decision-support systems that integrate economic and environmental metrics, paving the way for efficient and sustainable retrofitting solutions. |
| format | Article |
| id | doaj-art-a48b13f4e9eb490a84dbbe2e8770011d |
| institution | DOAJ |
| issn | 2571-5577 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-a48b13f4e9eb490a84dbbe2e8770011d2025-08-20T02:44:43ZengMDPI AGApplied System Innovation2571-55772024-12-0181510.3390/asi8010005Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A ReviewAmjad Baset0Muhyiddine Jradi1Mærsk McKinley Møller Institute, University of Southern Denmark, Campusvej 55, 5230 Odense, DenmarkMærsk McKinley Møller Institute, University of Southern Denmark, Campusvej 55, 5230 Odense, DenmarkThis review explores the novel integration of data-driven approaches, including artificial intelligence (AI) and machine learning (ML), in advancing building energy retrofits. This study uniquely emphasizes the emerging role of explainable AI (XAI) in addressing transparency and interpretability challenges, fostering the broader adoption of data-driven solutions among stakeholders. A critical contribution of this review is its in-depth analysis of innovative applications of AI techniques to handle incomplete data, optimize energy performance, and predict retrofit outcomes with enhanced accuracy. Furthermore, the review identifies previously underexplored areas, such as scaling data-driven methods to diverse building typologies and incorporating future climate scenarios in retrofit planning. Future research directions include improving data availability and quality, developing scalable urban simulation tools, advancing modeling techniques to include life-cycle impacts, and creating practical decision-support systems that integrate economic and environmental metrics, paving the way for efficient and sustainable retrofitting solutions.https://www.mdpi.com/2571-5577/8/1/5energy retrofitsbuilding energy performanceenergy efficiencyartificial intelligencemachine learning |
| spellingShingle | Amjad Baset Muhyiddine Jradi Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review Applied System Innovation energy retrofits building energy performance energy efficiency artificial intelligence machine learning |
| title | Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review |
| title_full | Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review |
| title_fullStr | Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review |
| title_full_unstemmed | Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review |
| title_short | Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review |
| title_sort | data driven decision support for smart and efficient building energy retrofits a review |
| topic | energy retrofits building energy performance energy efficiency artificial intelligence machine learning |
| url | https://www.mdpi.com/2571-5577/8/1/5 |
| work_keys_str_mv | AT amjadbaset datadrivendecisionsupportforsmartandefficientbuildingenergyretrofitsareview AT muhyiddinejradi datadrivendecisionsupportforsmartandefficientbuildingenergyretrofitsareview |