Wind power interval prediction based on hybrid semi-cloud model and nonparametric kernel density estimation

In today's increasingly serious world energy crisis, Renewable energy such as wind energy has gradually penetrated into life. Aiming at the uncertainty of wind power and the need of a mass of sample data in nonparametric kernel density estimation, a wind power interval prediction method based on hybrid semi-cloud model and nonparametric kernel density estimation is proposed. Firstly, aiming at the asymmetrical distribution of prediction error, a qualitative description method of error concept using hybrid semi-cloud model is proposed. And the conceptual data produced by the hybrid semi-cloud model are filtered through the 3En rule. Secondly, according to the fitting characteristics of nonparametric fitting, the conceptual cloud droplets are combined with nonparametric kernel density estimation to get the shortest prediction interval under different confidence levels. Finally, in order to verify the effectiveness of the method, the simulation of a wind farm data in the Oklahoma State is carried out, and the relative entropy is introduced to evaluate the fitting effect of the nonparametric fitting method. The comprehensive performance of the prediction interval under different methods is compared by three evaluation indexes: interval coverage probability, prediction interval width and comprehensive evaluation index F value. The experimental results show that compared with the prediction results of the other two methods, the method used in this paper has the best fitting effect, and the relative entropy is reduced by 1.1542. The comprehensive evaluation index F value of the prediction interval under each confidence level has been significantly improved, among which the increase at 80% confidence level is the most, and the F value increases by 0.0063. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

In this paper, a new wind power interval prediction method based on a hybrid semi-cloud model and nonparametric kernel density estimation is proposed. The method combines the conceptual data produced by the hybrid semi-cloud model with nonparametric kernel density estimation to obtain the shortest prediction interval under different confidence levels. Experimental results show that the proposed method has the best fitting effect and significantly improves the comprehensive evaluation index of the prediction interval.