Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artifical Intelligence-Driven Distributed Measurement System DOI Creative Commons
Virginia Negri, Roberto Tinarelli, Lorenzo Peretto

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2456 - 2456

Опубликована: Апрель 14, 2025

Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed addressing this issue, with research increasingly focused on sustainable practices. For instance, there a growing trend in designing buildings be net-zero emitters, while older structures are being retrofitted for energy efficiency achieve similar goals. To purpose, the study aims enhance management capabilities of an educational building by implementing smart infrastructure. Equipped photovoltaic panels and distributed measurement system, captures voltage current data calculates power. These electrical quantities then forecasted through AI-driven framework that manages data. paper details AI model used, including its experimental validation. results show provides reliable forecasts parameters. evaluation collected offers valuable insights, which support more informed actions optimizing performance. A key novelty lies exploration generalization nodes. This approach supported correlation analysis data, highlights potential accurate predictions case gaps. Moreover, ease deployment practical application were highlighted as factors scalability, allowing adaptation infrastructures.

Язык: Английский

EXP-Transformer time series prediction model for accident scenarios in high-reliability energy systems: Nuclear power plants case DOI
Xuan Zhang,

Meiqi Song,

Xiao Xiao

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135481 - 135481

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artifical Intelligence-Driven Distributed Measurement System DOI Creative Commons
Virginia Negri, Roberto Tinarelli, Lorenzo Peretto

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2456 - 2456

Опубликована: Апрель 14, 2025

Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed addressing this issue, with research increasingly focused on sustainable practices. For instance, there a growing trend in designing buildings be net-zero emitters, while older structures are being retrofitted for energy efficiency achieve similar goals. To purpose, the study aims enhance management capabilities of an educational building by implementing smart infrastructure. Equipped photovoltaic panels and distributed measurement system, captures voltage current data calculates power. These electrical quantities then forecasted through AI-driven framework that manages data. paper details AI model used, including its experimental validation. results show provides reliable forecasts parameters. evaluation collected offers valuable insights, which support more informed actions optimizing performance. A key novelty lies exploration generalization nodes. This approach supported correlation analysis data, highlights potential accurate predictions case gaps. Moreover, ease deployment practical application were highlighted as factors scalability, allowing adaptation infrastructures.

Язык: Английский

Процитировано

0