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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Advances in solid-state, sodium-ion, and flow batteries promise higher energy densities, faster charging, and longer lifespans, enabling electric vehicles to travel farther, microgrids to operate efficiently, and renewable energy to integrate seamlessly into the grid. . The Breaking It Down series aims to inform and inspire people by putting advanced manufacturing technologies and processes into simpler terms. With electric vehicles (EVs) that get us places, cell phones that connect us to others, and utility-scale electric grid storage that powers our homes. . Energy storage beyond lithium ion is rapidly transforming how we store and deliver power in the modern world. Battery storage is the fastest responding dispatchable. .
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