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.

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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
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article

Towards the DT of an Educational Building: An AI-Based Distributed Measurement System for the Power Forecasting

Alessandro Mingotti, Lorenzo Peretto, Andrea Acquaviva, Virginia Negri et al.
3 citations
Energy Load and Power Forecasting
0.63
article

Towards the DT of an Educational Building: An AI-Based Distributed Measurement System for the Power Forecasting

Alessandro Mingotti, Lorenzo Peretto, Andrea Acquaviva, Virginia Negri, Roberto Tinarelli, Francesco Barchi, Samuel Zanella
article en
3 citations

Abstract

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.

Laboratori Guglielmo Marconi (Italy) (IT), University of Bologna (IT)
Openalex Percentile: Top 31%
Energy Load and Power Forecasting
0.63
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Towards the DT of an Educational Building: An AI-Based Distributed Measurement System for the Power Forecasting — Alessandro Mingotti, Lorenzo Peretto, et al. · (2024) | TGRS Research Map | TGRS