Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning

Abstract

More than 20 years ago, China's hydropower undertakings rapid development, hydropower installed capacity has been a breakthrough. With the continuous improvement of hydropower installed capacity, the optimal dispatching of hydropower system is facing great challenges. In this paper, the average daily processing prediction of artificial hydropower station based on machine learning is studied. In this paper, the prediction model based on BPNN neural network is discussed to predict the daily runoff of hydropower station, and the overall system architecture is designed from the aspects of logical structure, physical structure and technical structure. Through THE simulation analysis of the water level of the hydropower station in different time periods, it is verified that the model has good simulation accuracy in the process of water level deduction, which lays a foundation for the optimization of hydropower station operation in the future.