Currently, the most widely deployed large-scale mechanical energy storage technology is pumped hydro-storage (PHS). . Mechanical energy storage can be added to many types of systems that use heat, water or air with compressors, turbines, and other machinery, providing an alternative to battery storage, and enabling clean power to be stored for days. The energy industry as well as the U. Department of Energy are investing in mechanical energy storage. . Hence, mechanical energy storage systems can be deployed as a solution to this problem by ensuring that electrical energy is stored during times of high generation and supplied in time of high demand. While the physics of mechanical systems are often quite simple (e. These devices include technologies like pumped hydroelectric. .
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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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