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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Solar energy power stations harness the sun's energy to generate electricity efficiently. They use solar panels, equipped with solar cells, to capture solar radiation. These cells convert sunlight directly into electrical energy through the photovoltaic effect. . Solar power, also known as solar electricity, is the conversion of energy from sunlight into electricity, either directly using photovoltaics (PV) or indirectly using concentrated solar power. While effective, these sources contribute significantly to greenhouse gas. . At the heart of any solar power station lies its most iconic component: the solar panel. Each panel consists of many solar cells, typically made. . They use arrays of solar panels that tirelessly convert sunlight into electricity. Let's delve deeper into the workings of these marvels. contribute to environmental conservation, 4. support energy independence. In a typical setup, a solar array. .
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