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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Solar panel manufacturing is a complex technological process that converts solar energy into usable electricity. . The U. solar industry is experiencing an unprecedented surge in manufacturing capacity, adding a remarkable 8. 6 gigawatts (GW) of new solar module production in the first quarter of 2025. This growth marks one of the strongest quarters in U. This transformation occurs through the photovoltaic effect, discovered in 1839 by Alexandre Edmond Becquerel, which enables solar cells to generate electrical current when exposed to. .
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