Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy
This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. In a concise multi-stage process, long-term climatic data and on-site technical documentati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/7/1041 |
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| author | Lorenzo Villani Martina Casciola Davide Astiaso Garcia |
| author_facet | Lorenzo Villani Martina Casciola Davide Astiaso Garcia |
| author_sort | Lorenzo Villani |
| collection | DOAJ |
| description | This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. In a concise multi-stage process, long-term climatic data and on-site technical documentation were analyzed to create a detailed BIM model. This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. The study also incorporated photovoltaic panels and solar collectors, reducing reliance on non-renewable energy sources. Results demonstrate the effectiveness of smart energy management, showcasing significant potential for scalability in similar building typologies. Future improvements include integrating a temporal evolution model, refining feature selection using advanced optimization techniques, and expanding validation across multiple case studies. This research highlights the transformative role of ML, IoT, and BIM in achieving sustainable, smart, and efficient building energy systems, offering a replicable framework for sustainable renovations in the hospitality sector. |
| format | Article |
| id | doaj-art-9e5d3ca8ae1f465aa926b84b0e55d8d8 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-9e5d3ca8ae1f465aa926b84b0e55d8d82025-08-20T03:06:24ZengMDPI AGBuildings2075-53092025-03-01157104110.3390/buildings15071041Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in ItalyLorenzo Villani0Martina Casciola1Davide Astiaso Garcia2Department of Astronautical, Electrical and Energy Engineering DIAEE, Sapienza University of Rome, 00184 Rome, ItalyDepartment of History, Representation and Restoration of Architecture DSDRA, Sapienza University of Rome, 00186 Rome, ItalyDepartment of Astronautical, Electrical and Energy Engineering DIAEE, Sapienza University of Rome, 00184 Rome, ItalyThis study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. In a concise multi-stage process, long-term climatic data and on-site technical documentation were analyzed to create a detailed BIM model. This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. The study also incorporated photovoltaic panels and solar collectors, reducing reliance on non-renewable energy sources. Results demonstrate the effectiveness of smart energy management, showcasing significant potential for scalability in similar building typologies. Future improvements include integrating a temporal evolution model, refining feature selection using advanced optimization techniques, and expanding validation across multiple case studies. This research highlights the transformative role of ML, IoT, and BIM in achieving sustainable, smart, and efficient building energy systems, offering a replicable framework for sustainable renovations in the hospitality sector.https://www.mdpi.com/2075-5309/15/7/1041smart building energy systemsMachine Learning (ML)Building Information Modeling (BIM)smart renovation strategiesHVAC systems adaptation |
| spellingShingle | Lorenzo Villani Martina Casciola Davide Astiaso Garcia Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy Buildings smart building energy systems Machine Learning (ML) Building Information Modeling (BIM) smart renovation strategies HVAC systems adaptation |
| title | Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy |
| title_full | Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy |
| title_fullStr | Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy |
| title_full_unstemmed | Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy |
| title_short | Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy |
| title_sort | integrated technologies for smart building energy systems refurbishment a case study in italy |
| topic | smart building energy systems Machine Learning (ML) Building Information Modeling (BIM) smart renovation strategies HVAC systems adaptation |
| url | https://www.mdpi.com/2075-5309/15/7/1041 |
| work_keys_str_mv | AT lorenzovillani integratedtechnologiesforsmartbuildingenergysystemsrefurbishmentacasestudyinitaly AT martinacasciola integratedtechnologiesforsmartbuildingenergysystemsrefurbishmentacasestudyinitaly AT davideastiasogarcia integratedtechnologiesforsmartbuildingenergysystemsrefurbishmentacasestudyinitaly |