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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TEHRAN, Jan. 04 (MNA) – Iran's Deputy Defense Minister for Industrial Research Affairs announced that the ministry will cooperate with the Energy Ministry of Energy to build power plants to produce 2. 8 MW of solar and wind energy across the country. . TEHRAN – Iran's largest solar power plant located in central Tehran is nearing completion and will soon come online as part of a sweeping national push to expand renewable energy, a senior official said. Farhad Shabihi, managing director of Tehran Regional Electricity Company, told IRNA that the. . Iran, with its vast solar potential and pressing energy demands, is poised to transform its energy landscape through renewable energy, particularly solar photovoltaic (PV) and energy storage. Blessed with an average annual solar irradiation of 4. 5 kWh/m² and up to 2,200 kilowatt-hours of solar. . TEHRAN, Jan. According to Energy Press, Iran's energy. .
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