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 solar energy is generated by photovoltaic arrays mounted on buildings (usually roofing), rather than dedicated solar power stations. . Energy production from renewables consisted of 27,71 % hydropower production (8,91 % imported and 18,80 % domestic), as well as 4,76 % produced domestically from solar energy. What percentage of Liechtenstein's electricity. . In recent decades, renewable energy efforts in Liechtenstein have also branched out into solar energy production. Energy in Liechtenstein describes production. .
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