Intelligent power load forecasting method based on reinforcement learning

This paper proposes an intelligent power load forecasting method based on reinforcement learning, aiming to address the limitations of traditional forecasting techniques in coping with the integration of high-proportion renewable energy and complex electricity demand. The study first identifies key influencing factors such as temperature and humidity through Spearman correlation analysis and then constructs an innovative model architecture that integrates the Long Short-Term Memory (LSTM) network and Deep Deterministic Policy Gradient (DDPG). The model utilizes the LSTM network to extract load time-series features, achieves dynamic optimization through the DDPG’s reinforcement learning mechanism, and introduces an adaptive reward function and curriculum learning strategy to enhance model performance. Experiments on the power dataset of Tempe Campus at Arizona State University show that the proposed method significantly outperforms traditional LSTM and DDPG models in terms of indicators such as root mean square error (32.4 kW), mean absolute percentage error (2.7%), and R2 (0.963), especially demonstrating stronger adaptability in complex scenarios such as load mutations and holidays. The research findings provide a new technical solution for high-precision load forecasting in smart grid environments.