Short Term Photovoltaic Output Interval Prediction Based on Integrated Machine Learning Model
To comprehensively explore the correlation information between different factors affecting photovoltaic output,the paper proposes a short term photovoltaic output interval prediction method based on integrated machine learning model to further improve the accuracy of the short-term photovoltaic output interval prediction with the machine learning model.Firstly,the fast correlation based filter(FCBF)is used to extract the optimal features from multidimensional historical photovoltaic data and meteorological data.Then on the basis of integrating multiple machine learning models,the prediction errors during the training process are collected,and the probability distribution of the prediction errors is obtained through maximum likelihood estimation,thereby obtaining the upper and lower limits of the prediction interval.Finally,the photovoltaic output curve is got by combining the integrated learning model prediction,and the final day-ahead photovoltaic output prediction interval is obtained.The reliability and superiority of the proposed model are verified through the examples.
short-term photovoltaic power predictionfeature selectionmachine learninginterval prediction
万方数据
维普
