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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By incorporating solar fields—typically using parabolic trough collectors with direct steam generation (DSG)—into gas turbine cycles, ISCC systems enhance overall thermal efficiency and reduce reliance on fossil fuels. . This study ofers a comprehensive techno-economic and environmental evaluation of a hybrid solar-natural gas combined cycle power plant designed for the Kirkuk region, taking advantage of its high solar irradiance. The proposed system incorporates advanced technologies to maximize eficiency and. . Rising international power requirements has enabled a sudden need of renewable energy-waste heat recovery solutions demand efficient thermodynamic models competent of binding solar and thermal energy successfully.
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