Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System
Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend i...
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2456 |
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| author | Virginia Negri Roberto Tinarelli Lorenzo Peretto Alessandro Mingotti |
| author_facet | Virginia Negri Roberto Tinarelli Lorenzo Peretto Alessandro Mingotti |
| author_sort | Virginia Negri |
| collection | DOAJ |
| description | Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend in designing new buildings to be net-zero emitters, while older structures are being retrofitted for energy efficiency to achieve similar goals. To this purpose, the study aims to enhance the energy management capabilities of an educational building by implementing a smart infrastructure. Equipped with photovoltaic panels and a distributed measurement system, the building captures voltage and current data and calculates power. These electrical quantities are then forecasted through an AI-driven framework that manages the data. The paper details the AI model used, including its experimental validation. The results show that the system provides reliable forecasts of electrical parameters. The evaluation of the distributed measurement system and the collected data offers valuable insights, which support more informed actions for optimizing energy management and system performance. A key novelty of this study lies in the exploration of model generalization across measurement nodes. This approach is supported by the correlation analysis of data, which highlights the potential for accurate predictions in case of data gaps. Moreover, the ease of deployment and the practical application of the system were highlighted as key factors for scalability, allowing for potential adaptation in similar infrastructures. |
| format | Article |
| id | doaj-art-2066fcdabc6c4fcd9086f72f88e05208 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2066fcdabc6c4fcd9086f72f88e052082025-08-20T02:25:04ZengMDPI AGSensors1424-82202025-04-01258245610.3390/s25082456Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement SystemVirginia Negri0Roberto Tinarelli1Lorenzo Peretto2Alessandro Mingotti3Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyRecent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend in designing new buildings to be net-zero emitters, while older structures are being retrofitted for energy efficiency to achieve similar goals. To this purpose, the study aims to enhance the energy management capabilities of an educational building by implementing a smart infrastructure. Equipped with photovoltaic panels and a distributed measurement system, the building captures voltage and current data and calculates power. These electrical quantities are then forecasted through an AI-driven framework that manages the data. The paper details the AI model used, including its experimental validation. The results show that the system provides reliable forecasts of electrical parameters. The evaluation of the distributed measurement system and the collected data offers valuable insights, which support more informed actions for optimizing energy management and system performance. A key novelty of this study lies in the exploration of model generalization across measurement nodes. This approach is supported by the correlation analysis of data, which highlights the potential for accurate predictions in case of data gaps. Moreover, the ease of deployment and the practical application of the system were highlighted as key factors for scalability, allowing for potential adaptation in similar infrastructures.https://www.mdpi.com/1424-8220/25/8/2456distributed measurement systemforecastingaccuracyartificial intelligencedigital twinenergy meters |
| spellingShingle | Virginia Negri Roberto Tinarelli Lorenzo Peretto Alessandro Mingotti Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System Sensors distributed measurement system forecasting accuracy artificial intelligence digital twin energy meters |
| title | Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System |
| title_full | Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System |
| title_fullStr | Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System |
| title_full_unstemmed | Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System |
| title_short | Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System |
| title_sort | comprehensive forecasting of electrical quantities in an educational building via artificial intelligence driven distributed measurement system |
| topic | distributed measurement system forecasting accuracy artificial intelligence digital twin energy meters |
| url | https://www.mdpi.com/1424-8220/25/8/2456 |
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