AI-enabled metaheuristic optimization for predictive management of renewable energy production in smart grids

Abstract

The integration of renewable energy sources into smart grids offers a promising solution for building sustainable and reliable energy systems. However, optimizing hybrid renewable energy systems remains a crucial area of research. The study presents a comprehensive approach combining artificial intelligence algorithm techniques with metaheuristic optimization algorithms for anticipating and managing renewable energy sources in smart grid environments. With precision, recall, and accuracy scores of 0.92, 0.93, and 0.92, respectively, the proposed Hybrid LSTM-RL model beats current algorithms in correctly forecasting energy demand patterns. With an accuracy of 0.91 for various load balancing measures, the RL-SA algorithm efficiently measures load balancing. With mean squared error (MSE), mean absolute error (MAE), R-squared score, root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 345.12, 15.07, 0.78, 18.57, and 7.83, respectively, the CNN-PSO algorithm also turns out to be the most successful at forecasting the generation of renewable energy. These discoveries help hybrid renewable energy systems in smart grid settings advance, enabling effective, dependable, and economical energy production and distribution. The suggested solution also has the potential to be used in rural and off-grid settings. Overall, this research offers a useful method for maximizing the production of renewable energy and acts as a spark for additional studies into energy management systems.