This paper presents a variety of ML approaches combined with XAI to predict solar power generation, aiming to optimize energy management in smart grids. . Machine learning (ML) algorithms can provide highly accurate predictions, but their complexity often makes them difficult to interpret due to their black-box nature. Combining ML and Explainable Artificial Intelligence (XAI) makes these models more transparent and enables users to understand the. . This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power generation rates based on environmental conditions. .
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With the energy crisis and the constant blackout in the Mozambique Power Company grid, the option of applying solar photovoltaic (PV) systems has been one of the most used alternatives in the neighborhoods of the Maputo region. . We aim to make solar power a simple and reliable option for families, schools, and small businesses. Helping people thrive where electricity is limited. However, inefficient power delivery caused by unproper sizing and. .
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