Towards the DT of an Educational Building: An AI-Based Distributed Measurement System for the Power Forecasting
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. In line with these efforts, this study centres on the electrical system of an educational building outfitted with renewable energy sources and an AI-powered distributed measurement system. Voltage and current measurements across the network enable calculating power usage and energy consumption, facilitating building and appliance power forecasting. The study outlines the innovative architecture of the AI-driven solution and presents preliminary results from the forecasting activities. Additionally, it briefly outlines future steps to create a digital twin of the electrical system.
Authors
- Alessandro Mingotti (ORCID: https://orcid.org/0000-0002-1739-1277)
- Lorenzo Peretto (ORCID: https://orcid.org/0000-0003-3407-2056)
- Andrea Acquaviva
- Virginia Negri (ORCID: https://orcid.org/0000-0001-8521-0776)
- Roberto Tinarelli (ORCID: https://orcid.org/0000-0002-5377-2421)
- Francesco Barchi (ORCID: https://orcid.org/0000-0003-1839-344X)
- Samuel Zanella
Institutions
- Laboratori Guglielmo Marconi (Italy) (IT)
- University of Bologna (IT)
Publication Details
- Published
- 2024-09-18
- DOI
- https://doi.org/10.1109/amps62611.2024.10706690
- Citations
- 3
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.63