Multi-objective interval prediction of wind power based on conditional copula function

作     者：Gang ZHANG Zhixuan LI Kaoshe ZHANG Lei ZHANG Xia HUA Yongqing WANG 

作者机构：Institute of Water Resources and Hydro-Electric EngineeringXi’an University of TechnologyXi’an 710048China State Grid Shaanxi Baoji Electric Power CompanyBaoji 721000China State Grid Gansu Electric Power CompanyGansu Electric Power Research InstituteLanzhou 730050China State Grid Shaanxi Electric Power CompanyShaanxi Electric Power Research InstituteXi’an 710054China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第4期

页      面：802-812页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by the National Natural Science Foundation of China(No.51507141) Key research and development plan of Shaanxi Province(No.2018ZDCXL-GY-10-04) the National Key Research and Development Program of China(No.2016YFC0401409) the Shaanxi provincial education office fund(No.17JK0547) 

主　　题：Wind power prediction Interval prediction Conditional copula function Empirical distribution function Multi-objective optimization algorithm 

摘      要：Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind ***,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction ***,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution *** use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution *** particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical ***,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.