Intelligent Time Series Prediction Algorithm for Generating Power Based on Width Learning
The prediction of generation power is greatly influenced by meteorological data,which may lead to a certain deviation between the power prediction value and the actual value.In order to accurately predict the generation power,an intelligent time series prediction algorithm for generation power based on width learning is proposed.Corresponding data sets are formed ac-cording to different types and are used to train the prediction model.The fuzzy width learning is used to replace the original sparse automatic coding,the time series model is used to perform nonlinear transformation to form enhanced node layer,and establish a generating power prediction model by constructing an objective function.By combining meteorological data and width learning model,a more reliable digital twin power prediction result can be obtained.The experimental results show that the normalized average absolute error of the power prediction is 0.687％,the normalized root mean square error is 0.634％,and the correlation coefficient is 0.976.The overall fitting degree is good,and the generating power is close to the true value,which can accurately predict the photovoltaic power,and provide valuable reference and decision support.
万方数据
维普
