Optimization of Power System Balance and Short Term Generation Prediction Model Based on Machine Learning

To achieve balance in the power system, it is necessary to accurately predict the demand for electricity and the ability to generate electricity. In the construction of the entire short-term power generation prediction model, traditional methods are based on statistical models and rules, but these methods often cannot fully consider the complexity and uncertainty of the power system. Therefore, introducing machine learning has become an important way to improve the efficiency and reliability of the power system. This article conducted relevant research on the importance analysis of power system balance and short-term power generation prediction, and used BPNN (back propagation neural network) to optimize and improve the short-term power generation prediction model. Combining variable input module, data processing module, multi task learning module, BPNN module, and power generation prediction output module, a short-term power generation prediction optimization model was designed. Finally, based on the experiment, the following conclusion can be drawn: the power generation prediction optimization model based on BPNN has improved efficiency and accuracy by approximately 14.4% and 9.5% compared to traditional methods. Finally, the power balance system proposed in this article can ensure that the power supply meets the needs of users while maximizing the utilization of power generation resources, which has certain reference value.