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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This year, massive solar farms, offshore wind turbines, and grid-scale energy storage systems will join the power grid. Dozens of large-scale solar, wind, and storage projects will come online worldwide in 2025, representing several gigawatts of new capacity. . Solar and wind not only kept pace with global electricity demand growth, they surpassed it across a sustained period for the first time, signalling that clean power is now steering the direction of the global energy system. Solar gained momentum in regions once seen as peripheral, from Central. . While energy is essential to modern society, most primary sources are non-renewable. The current fuel mix causes multiple environmental impacts, including climate change, acid rain, freshwater depletion, hazardous air pollution, and radioactive waste. The Oasis de Atacama in Chile will be. .
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