Explainable AI for Energy Prediction and Anomaly Detection in Solar Energy Systems

The integration of solar energy systems into power grids is gaining momentum globally, promising sustainable and renewable energy solutions. However, the intermittent and dynamic nature of solar energy poses challenges for its efficient utilization and management. In response, this research investigates the application of Explainable Artificial Intelligence (XAI) techniques for enhancing the prediction accuracy and anomaly detection in solar energy systems. By leveraging advanced machine learning algorithms and interpretable models, this study aims to provide insights into the underlying factors influencing solar energy generation, thus enabling better decision-making and optimization strategies. In order to make machine learning models more understandable, approaches such as Explainable Artificial Intelligence (XAI) have been developed. The understanding and confidence that are ingrained in the output of machine learning algorithms is what makes XAI significant. In order to forecast Key Performance Indicators (KPIs), the current work constructs a thorough XAI model and evaluates the effectiveness of several learning algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R^2) coefficient of determination, the number of iterations, and execution time. The proposed framework holds significant potential to improve the reliability, efficiency, and performance of solar energy systems, contributing to the transition towards a more sustainable and resilient energy infrastructure.