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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These ratings determine how much energy your solar panels can generate and store. Essentially, they help you gauge the efficiency and effectiveness of your solar power system. Solar power units are generally measured in two main ways: kilowatts (kW) and kilowatt-hours (kWh). These units might sound. . Factors affecting solar panel ratings include wattage, solar cell efficiency, and the number of panels in a system, all impacting power output and overall system performance. Interpreting solar panel ratings involves understanding power rating benchmarks and standard power output ratings and. .
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