A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM)

Yonqian Liu, Ying Sun, David Infield, Yu Zhao, Shuang Han, Jie Yan

Research output: Contribution to journalArticle

  • 9 Citations

Abstract

In an electric power system with a high penetration of wind power, incoming power ramps pose a serious threat to the power system. To adopt suitable response strategies for wind power ramps, it is important to predict them accurately and in a timely manner. Since power ramps are caused by various factors, their occurrence have irregular characteristics and vary by location, bringing great difficulty in forecasting. To solve this problem, a hybrid forecasting model OT-SVM (Orthogonal Test and Support Vector Machine) was developed in this paper, which combines an orthogonal test with a support vector machine. A novel factor analysis method was established based on the theory of the orthogonal test (OT), and applied to determine the optimal
inputs of a support vector machine (SVM). The effectiveness of OT-SVM was tested with three wind farms in China, while comparing the results with other related methods. The results show that the proposed OT-SVM has the highest accuracy covering different input numbers and time resolutions. In addition, a novel evaluation index MAI (Mean Accuracy Index) was proposed, considering both the missed ramps and false ramps, which can be used as a supplementary index for CSI.
LanguageEnglish
Pages1-7
Number of pages7
JournalIEEE Transactions on Sustainable Energy
VolumePP
Issue number99
DOIs
StatePublished - 31 Aug 2016

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Wind power
Support vector machines
Factor analysis
Electric power systems
Farms

Keywords

  • wind power ramp forecasting Orthogonal test
  • multi-factor analysis
  • large-scale integration of wind power
  • meteorological factors
  • statistical analysis
  • support vector machine

Cite this

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title = "A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM)",
abstract = "In an electric power system with a high penetration of wind power, incoming power ramps pose a serious threat to the power system. To adopt suitable response strategies for wind power ramps, it is important to predict them accurately and in a timely manner. Since power ramps are caused by various factors, their occurrence have irregular characteristics and vary by location, bringing great difficulty in forecasting. To solve this problem, a hybrid forecasting model OT-SVM (Orthogonal Test and Support Vector Machine) was developed in this paper, which combines an orthogonal test with a support vector machine. A novel factor analysis method was established based on the theory of the orthogonal test (OT), and applied to determine the optimalinputs of a support vector machine (SVM). The effectiveness of OT-SVM was tested with three wind farms in China, while comparing the results with other related methods. The results show that the proposed OT-SVM has the highest accuracy covering different input numbers and time resolutions. In addition, a novel evaluation index MAI (Mean Accuracy Index) was proposed, considering both the missed ramps and false ramps, which can be used as a supplementary index for CSI.",
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A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM). / Liu, Yonqian; Sun, Ying; Infield, David; Zhao, Yu; Han, Shuang; Yan, Jie.

In: IEEE Transactions on Sustainable Energy, Vol. PP, No. 99, 31.08.2016, p. 1-7.

Research output: Contribution to journalArticle

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AU - Sun,Ying

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AU - Zhao,Yu

AU - Han,Shuang

AU - Yan,Jie

N1 - (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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