DOI:10.1007/s40565-018-0495-0 |
| |
| |
Multi-objective interval prediction of wind power based on conditional copula function |
| |
|
| |
Page view: 0
Net amount: 626 |
| |
Author:
Gang ZHANG1, Zhixuan LI1, Kaoshe ZHANG1, Lei ZHANG2, Xia HUA3, Yongqing WANG4
|
Author Affiliation:
1. Institute of Water Resources and Hydro-Electric Engineering,
Xi’an University of Technology, Xi’an 710048, China;
2. State Grid Shaanxi Baoji Electric Power Company, Baoji
721000, China;
3. State Grid Gansu Electric Power Company, Gansu Electric
Power Research Institute, Lanzhou 730050, China;
4. State Grid Shaanxi Electric Power Company, Shaanxi
Electric Power Research Institute, Xi’an 710054, China
|
Foundation: |
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), and the Shaanxi provincial education office fund (No. 17JK0547). |
|
|
Abstract: |
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 decreased prediction accuracy. Therefore, in this paper, 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 non-dominated 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 Chinese cities as examples for validation 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. |
Keywords: |
Wind power prediction, Interval prediction, Conditional copula function, Empirical distribution function, Multi-objective optimization algorithm |
| |
Online Time:2019/07/31 |
| |
|
|
View Full Text
Download reader
|
|
|