AI Based Optimization of a Solar Wind Battery Hybrid Renewable Energy System: A Case Study of Yavatmal, India

Abstract

Hybrid renewable energy systems integrating solar, wind, and battery storage are widely considered a sustainable solution for decentralized power generation. However, variability in renewable resources leads to operational inefficiencies and challenges in energy management. This study proposes an Artificial Intelligence (AI) based optimization framework that integrates Artificial Neural Networks (ANN) with a Genetic Algorithm (GA) to enhance the performance of a solar wind battery hybrid renewable energy system. The proposed method is evaluated using real meteorological data collected from Yavatmal district, Maharashtra, India. The ANN model is developed for accurate short-term power prediction, while GA is applied to optimize operational parameters including learning rate, weight coefficients, and battery charge-discharge limits. The proposed ANN-GA framework is compared with a conventional rule based energy management strategy to establish a baseline performance reference. Simulation results indicate that the optimized system improves prediction accuracy from 89.4% to 96.8%, increases annual energy yield by 13.6%, and reduces operational losses by approximately 10%. Furthermore, the optimized system demonstrates improved battery utilization and reduced cost of energy. The results confirm that the proposed AI based optimization framework provides significant improvements in reliability, efficiency, and economic performance of hybrid renewable energy systems in semi urban regions.