Exploration of Artificial Intelligence Based Power Supply and Demand Mutual Aid Prediction and Collaborative Dispatch Strategy

The safe and stable operation of the power system is the foundation for ensuring social and economic development, but with the continuous growth of electricity demand, the contradiction between electricity supply and demand is becoming increasingly prominent. The artificial intelligence based power supply and demand mutual benefit prediction and collaborative scheduling strategy provides a new solution for this. This study utilizes convolutional neural networks to accurately predict the supply and demand situation of the power system, and designs an optimized scheduling strategy based on the prediction results. The experiment first collects data related to the operation of the power system, designs a supply and demand forecasting model, and compares it with traditional models. Based on the predicted results, this article designs a coordinated power supply and demand scheduling strategy, including power generation planning, energy storage scheduling, demand response, etc. The experimental results show that the accuracy of the supply and demand forecasting model based on CNN is on average 98.51%, which can significantly improve the forecasting accuracy.