Communication Dans Un Congrès Année : 2025

Implementation of an AI-driven dynamic control system to optimize excess photovoltaic energy management in grid-connected sustainable BIPV

Résumé

Abstract This study proposes an integrated approach for optimizing grid-connected photovoltaic (PV) systems through AI-based forecasting and a Dynamic Automatic Control System (DACS). Using Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN-LSTM model, we predict PV production and building energy demand. The CNN-LSTM model achieved the best performance for PV forecasting (MAE = 0.0423), while LSTM excelled in energy demand prediction (MAE = 0.0130). A disaggregated analysis of key building surfaces (South Facade, South Roof, East Facade, and North Roof) revealed significant variations in energy contributions. MATLAB/Simulink simulations based on one day of demand indicate that the DACS attains a 70% PV utilization rate, with the South Roof and South Facade reaching 69% and 42% utilization, respectively. These results demonstrate the potential of AI-driven energy management systems to optimize renewable energy use and support decarbonization by reducing grid dependency. This study integrates, for the first time, ultra-short-term (5-minute) forecasting and real-time dynamic control, offering continuous PV–battery–grid operation and establishing a new standard in energy efficiency.

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hal-05389163 , version 1 (29-11-2025)

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Mame Cheikh Sow, Youssef Jouane, Mourad Zghal. Implementation of an AI-driven dynamic control system to optimize excess photovoltaic energy management in grid-connected sustainable BIPV. CISBAT, Sep 2025, Lausanne, Switzerland. pp.032017, ⟨10.1088/1742-6596/3140/3/032017⟩. ⟨hal-05389163⟩
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