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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Most of the BESS systems are composed of securely sealed, which are electronically monitored and replaced once their performance falls below a given threshold. Batteries suffer from cycle ageing, or deterioration caused by charge–discharge cycles. This deterioration is generally higher at and higher . This aging causes a loss of performance (capacity or voltage decrease), overheating, and may eventually l.
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