Research on load forecasting and scheduling optimization algorithm of power system based on artificial intelligence

The study aims to enhance the operational efficiency of power systems, reduce energy consumption and environmental pollution, and strengthen the reliability of power supply. Initially, the paper analyzes the importance of load forecasting in power systems and the challenges faced, followed by proposing a load forecasting model based on Temporal Convolutional Networks (TCN) from deep learning. This model integrates multiple information sources such as historical load data, weather conditions, and holiday factors to achieve precise predictions. Subsequently, the paper designs a load scheduling optimization algorithm for power systems based on a reinforcement learning framework. This algorithm takes into account various constraints and optimization objectives to realize accurate scheduling of power system loads. Through experimental analysis, this study verifies that the proposed TCN model outperforms traditional Long Short-Term Memory (LSTM) models in load forecasting tasks. Furthermore, in terms of scheduling optimization, the Deep Reinforcement Learning (DRL) algorithm demonstrates significant advantages over traditional rule-based scheduling methods in both economic performance and constraint satisfaction.