IBT-PPO: A Dual-Stage Intelligent Forecasting and Reinforcement Learning Framework for Optimal Scheduling in Hybrid Renewable Energy Systems

Abstract

In this work, Intelligent Bidirectional Long Short-Term Memory with Temporal Fusion Transformer-based prediction and Proximal Policy Optimization (IBT-PPO) is proposed in response to the challenges of uncertain renewable generation, fluctuating demand, and inefficient energy scheduling in hybrid renewable energy systems. The algorithm is based on a dual-stage framework that integrates machine learning forecasting with reinforcement learning-based planning. Initially, a hybrid Bi-LSTM-TFT model is employed to generate accurate short-term forecasts of wind power, solar power, and demand, which employs temporal dependencies and multi-horizon patterns. After that, the PPO strategy is designed to optimize scheduling decisions, adaptively balancing battery usage, grid reliance, and renewable dispatch. To enhance robustness, adaptive feature weighting and temporal gating strategies are incorporated, ensuring stable convergence and reduced planning redundancy. Subsequently, the energy allocation is refined through iterative learning to minimize operational cost and maximize renewable penetration. The proposed framework is evaluated as an offline/post hoc forecasting and scheduling approach, with the Bi-LSTM–TFT module exploiting historical temporal representations and the PPO agent optimizing energy-management decisions based on the resulting forecasts. The experimental evaluation is carried out using the Open Power System Data (OPSD) dataset, which provides realistic time-series data for wind, solar, demand, and electricity prices. Thus, the IBT-PPO system integrates multi-horizon probabilistic forecasting and adaptive feature weighting for better prediction and planning accuracy and achieves a 24.1% cost reduction and 95.5% renewable utilization, thereby advancing efficient and intelligent energy prediction and planning.