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 panels capture sunlight and convert it to electricity using photovoltaic (PV) cells like the one illustrated above. Such cells, which can power everything from calculators to cars (our example will be a house), have several components. First, and most obviously, are two layers. . When light shines on a photovoltaic (PV) cell – also called a solar cell – that light may be reflected, absorbed, or pass right through the cell. The PV cell is composed of semiconductor material; the “semi” means that it can conduct electricity better than an insulator but not as well as a good. . How do the photovoltaic cells that lie at the heart of them turn sunlight ("photo") into electricity ("voltaic")? Below, familiarize yourself with the parts of a basic photovoltaic cell, and find out how it goes about harnessing the free energy of the sun. — Stephanie Chasteen and Rima Chaddha 1.
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