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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How many billions of profits does the energy storage power station generate? The inquiry into the financial returns of energy storage power stations reveals that they can yield profits in the tens to hundreds of billions of dollars annually. . Energy storage power stations enhance grid reliability and support renewable integration, 2. Profitability hinges on long-term contracts and market participation strategies, 3. Initial capital investment is substantial, requiring careful financial planning, 4. This profitability stems from various factors, including. . Energy storage is the capture of energy produced at one time for use at a later time [1] to reduce imbalances between energy demand and energy production. A device that stores energy is generally called an accumulator or battery. Energy comes in multiple forms including radiation, chemical. .
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