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 power.However,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 accuracy.Therefore,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 function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The 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 examples.Finally,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.
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 power. However, 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 accuracy. Therefore, 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 function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II) to obtain the optimal solution set. The 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 examples. Finally, 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.
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).
作者简介
Gang ZHANG received his Ph.D.degree from Xi’an University of Technology,China in 2013.He is currently working in the Institute of Water Resources and Hydro-electric Engineering,Xi’an University of Technology.His research interests focus on the renewable energy prediction and power system dispatching,zhanggang3463003@163.com;Zhixuan LI is currently studying in the Institute of Water Resources and Hydro-electric Engineering,Xi’an University of Technology,China.His research interests focus on the renewable energy prediction and power system dispatching,15594818128@163.com;Kaoshe ZHANG received his Ph.D.degree from Xi’an Jiaotong University,China.He is currently working in the Institute of Water Resources and Hydro-electric Engineering,Xi’an University of Technology.His research interests focus on the renewable energy prediction and power system dispatching.zhangks@263.net;Lei ZHANG received her master degree from Xi’an University of Technology,China in 2018.She is currently working in the Shaanxi Baoji electric power company.Her research interests focus on the renewable energy prediction and power system dispatching.zhanglzlei@163.com;Xia HUA received his Ph.D.degree in semiconductor physics from the Department of Physics and Astronomy,Shanghai Jiao Tong University,China in 2014.He is currently working in the Gansu Electric Power Research Institute,Lanzhou,China.His research interest focuses on the renewable energy forecasting model.kevinxhua@163.com;Yongqing WANG is a professorate senior engineer,and currently working in Shaanxi Electric Power Research Institute,State Grid Shaanxi Electric Power Company,China.His research interest focuses on the renewable energy dispatching.3087984821@qq.com
