Artificial Intelligence for Optimizing Renewable Energy Systems in Sustainable Power Generation - Ageng Setiani Rafika; Dendy Jonas; Muchlisina Madani; Oliver Sauntos (International Transactions on Artificial Intelligence, 2026)
The rapid expansion of renewable energy adoption has increased the need for intelligent energy management, as conventional rule based dispatch systems of ten struggle with the dynamic, nonlinear, and uncertain operating conditions of high-penetration renewable grids. Traditional controllers show limited energy utilization efficiency and frequent frequency-standard violations under variable wind and solar conditions. This study proposes and evaluates an integrated Artificial Intelligence (AI) framework combining a Long Short-Term Memory (LSTM) neural network for 24-hour energy demand and generation forecasting with Particle Swarm Optimization (PSO) for real-time dispatch optimization. The framework is tested against a conventional rule-based baseline using three benchmark datasets from the UCI Machine Learning Repository, the National Renewable Energy Laboratory (NREL), and Open Power System Data, covering 36 months of hourly solar and wind observations. The objective is to design and experimentally validate an AI-based optimization framework that improves energy efficiency, reduces operational losses, and enhances grid stability in renewable energy systems. The proposed LSTM-PSO framework reduces Mean Absolute Error (MAE) by 50.7% and Root Mean Square Error (RMSE) by 44.3%. Energy efficiency increases from 76.2% to 91.4%, while energy losses decrease from 20.7% to 9.6%, equivalent to approximately 5,800 tonnes of CO2 equivalent avoided annually at a 100 MW grid scale. The integrated LSTM PSO architecture provides a reliable and scalable basis for AI-driven renewable energy optimization, supporting SDG 7, SDG 9, SDG 11, and SDG 13.