While grid-connected solar power is the least-cost renewable energy option for South Tarawa and there is significant resource potential of 554 MW, deployment has been limited. How much power does South Tarawa need?The photovoltaic systems account for 22% of installed capacity but supply only. . Solar container communication wind power constructi gy transition towards renewables is central to net-zero emissions. However,building a global power sys em dominated by solar and wind energy presents immense challenges.
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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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