Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression

He, Yaoyao1,2; Li, Haiyan1,2; Wang, Shuo3; Yao, Xin3,4

2021-03-21

10.1016/j.neucom.2020.10.093

NEUROCOMPUTING   影响因子和分区

0925-2312

1872-8286

Accurate forecasting of wind power plays an important role in an effective and reliable power system. However, the fact of non-schedulability and fluctuation of wind power significantly increases the uncertainty of power systems. The output power of a wind farm is usually mixed with uncertainties, which reduce the effectiveness and accuracy of wind power forecasting. In order to handle the uncertainty of wind power, this paper first proposes to conduct outlier detection and reconstruct data before the prediction. Then, a wind power probability density forecasting method is proposed, based on cubic spline interpolation and support vector quantile regression (CSI-SVQR), which can better estimate the whole wind power probability density curve. However, the probability density prediction method can not acquire the optimal point prediction and interval prediction results at the same time. In order to analyze the uncertainty of wind power, the present study considers the prediction results from the perspective of probabilistic point prediction and interval prediction respectively. Three sets of real-world wind power data from Canada and China are used to validate the CSI-SVQR method. The results show that the proposed method not only efficiently eliminates the outliers of wind power but also provides the probability density function, offering a complete description of wind power generation fluctuation. Furthermore, more accurate point prediction and prediction interval (PI) can be obtained compared to existing methods. Wilcoxon signed rank test is used to verify that CSI can improve the performance of forecasting methods. (c) 2020 Elsevier B.V. All rights reserved.

Wind power forecasting Support vector quantile regression (SVQR) Cubic spline interpolation (CSI) function Probability density forecasting

EI ; SCI

英语

其他

National Natural Science Foundation[71771073,61329302] ; Fundamental Research Funds for the Central Universities[PA2020GDKC0006] ; Science and Technology Innovation Commitee Foundation of Shenzhen[ZDSYS201703031748284] ; EPSRC[EP/K001523/1]

Computer Science

Computer Science, Artificial Intelligence

WOS:000617365300012

ELSEVIER

20205109656104

Interpolation ; Weather forecasting ; Statistics ; Uncertainty analysis ; Regression analysis ; Electric power generation ; Probability density function

Meteorology:443 ; Wind Power (Before 1993, use code 611 ):615.8 ; Numerical Methods:921.6 ; Probability Theory:922.1 ; Mathematical Statistics:922.2

COMPUTER SCIENCE

Web of Science

1.Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China
2.Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei 230009, Peoples R China
3.Univ Birmingham, Sch Comp Sci, CERCIA, Birmingham B15 2TT, W Midlands, England
4.Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Sch Comp Sci & Engn, Shenzhen 518055, Peoples R China

He, Yaoyao,Li, Haiyan,Wang, Shuo,et al. Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression[J]. NEUROCOMPUTING,2021,430:121-137.

He, Yaoyao,Li, Haiyan,Wang, Shuo,&Yao, Xin.(2021).Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression.NEUROCOMPUTING,430,121-137.

He, Yaoyao,et al."Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression".NEUROCOMPUTING 430(2021):121-137.