In order to solve the problems of intermittence and randomness of new energy power generation,the study conducts the prediction research on the new energy power system and operational reserve quantization probability based on the chaotic characteristics of the atmosphere.The extreme learning machine quantile regression prediction is used to reduce the prediction error and ensure the operation safety of the system.At the same time,an integrated scheme of probabilistic prediction and decision making is proposed.Driven by artificial intelligence,the extreme learning machine quantile regression is adopted to introduce meteorological features mining non-parametric interval prediction,optimize the existing cost interval prediction and constraint conditions,and design a machine learning model integrating probabilistic prediction and decision making.The results show that this method can effectively overcome the problems of large error and insufficient decision-making performance,reduce the complexity of the model,improve the coordination of management,and ensure the safe and efficient operation of the power system.
关键词
电力系统/备用量化/概率预测-决策
Key words
Power system/Reserve quantification/Probabilistic prediction-decision making
维普
万方数据