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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Current commercially available solar panels convert about 20-22% of sunlight into electrical power. However, new research published in Nature has shown that future solar panels could reach efficiencies as high as 34% by exploiting a new technology called tandem solar cells. This surge in solar is fuelled by two key developments. First, scientists, engineers and those in industry are learning how to make solar panels by the billions. Every fabrication step is meticulously. . Solar panel technology is experiencing a remarkable transformation in 2024, revolutionizing home energy with groundbreaking innovations that promise to reshape residential power generation. Perovskite solar cells, the most exciting advancement in decades, are achieving efficiency rates above 30%. .
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