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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Among the most feasible methods for storing solar energy involves the utilization of specific organic and inorganic substances, which are referred to as phase change materials (PCMs), which enable the latent heat of fusion to be harnessed [4]. . The efficient utilization of solar energy technology is significantly enhanced by the application of energy storage, which plays an essential role. Nowadays, a wide variety of applications deal with energy storage. Due to the intermittent nature of solar radiation, phase change materials are. . This article designs a high-altitude border guard post that can fully utilize the heat absorbed by solar collectors to continuously store thermal energy during the day and stably release heat at night. This device is a spherical encapsulated paraffin phase change heat exchanger device (stainless. .
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