Modelling of wind power forecasting errors based on kernel recursive least-squares method

doi:10.1007/s40565-016-0259-7

Home > Archive>Volume 5, Issue 5, 2017 >735-745. DOI:10.1007/s40565-016-0259-7

PDF HTML Export

Modelling of wind power forecasting errors based on kernel recursive least-squares method

DOI:

10.1007/s40565-016-0259-7

Author:

Man XU
Man XU
State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Zongxiang LU
Zongxiang LU
State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Ying QIAO
Ying QIAO
State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Yong MIN
Yong MIN
State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

Affiliation:

1 State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China

Fund Project:

This work was partly supported by National Natural Science Foundation of China (No. 51190101) and science and technology project of State Grid, Research on the combined planning method for renewable power base based on multi-dimensional characteristics of wind and solar energy. The authors would like to thank Tsingsoft Innovation Technology Co. Ltd for providing the wind power data. We also thank Professor Pierre Pinson for some innovative ideas.

Article

Figures

Metrics

Reference

Cited by

Materials

Abstract:

Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what speci?c forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the ?eld application adaptability problems. The kernel recursive least-squares (KRLS) model is introduced to meet the requirements of online error correction. An iterative error modi?cation approach is designed in this paper to yield the potential bene?ts of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on ?eld data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.

Key words:Forecasting error amending, Kernel recursive least-squares (KRLS), Spatial and temporal teleconnection, Wind power forecast

Get Citation

Copy

Article Metrics

Abstract:
PDF:
HTML:
Cited by:

History

Received:
Revised:
Adopted:
Online: September 15,2017
Published:

Home

Introduction

Editorial Board

For Author

Call For Papers

APC

Sponsor & Publisher

Get Citation

Share

Article Metrics

History