Optimizing Renewable Energy Systems With LSTM Networks, Support Vector Regression, and Reinforcement Learning

The rapid expansion of renewable energy systems has increased the need for intelligent forecasting and control techniques that can support stable and efficient grid integration. This study proposes a hybrid optimization framework that combines Long Short‐Term Memory networks, Support Vector Regression, and Reinforcement Learning for renewable energy prediction, storage control, and real‐time dispatch management. Renewable energy data covering solar, wind, hydropower, biofuel, and geothermal sources from 1965 to 2022 were used for model development and evaluation. The input data were cleaned and preprocessed before being used in the hybrid predictive model. The LSTM component was applied to capture temporal dependencies in renewable energy generation, while SVR was used to refine nonlinear forecasting patterns and improve prediction stability. The predicted energy output was then supplied to a reinforcement learning agent, which optimized energy flow, storage charging and discharging decisions, and supply–demand balancing under changing grid conditions. The proposed method was evaluated through a hybrid solar‐wind case study and compared with conventional methods, including Machine Learning Techniques, Deep Image Processing, and Dynamic Load Management. The results show that the LSTM‐SVR‐RL framework achieved 98.1% energy consumption accuracy, 96.4% cost‐effectiveness, 97.4% reliability, and 96.2% sustainability. The model also reduced grid instability by 48.6% and prediction errors by 22.8%. In the hybrid solar‐wind system, the supply–demand mismatch decreased from 17.3% under conventional machine learning techniques to 4.8% using the proposed method, while the grid stability coefficient improved to 0.91. Environmental performance also improved, with annual carbon dioxide emissions reduced from 1345 metric tons to 87 metric tons due to better renewable energy utilization and reduced dependence on backup power sources. These findings demonstrate that the proposed hybrid framework provides a reliable, cost‐effective, and sustainable solution for renewable energy forecasting and smart grid energy management.