A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine

Min Ding, Min Wu, Ryuichi Yokoyama, Yosuke Nakanishi, Yicheng Zhou

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Wind power forecasting improves the wind power trade and the wind power dispatch level. Wind speed is closely related to the accuracy of wind energy forecasting. This chapter introduces the process of wind power generation, describes an amplitude-frequency characteristic extraction method for the wind speed, and presents a hybrid-kernel least-squares support vector machine based wind power forecasting method.

Original languageEnglish
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Pages395-411
Number of pages17
DOIs
Publication statusPublished - 2021

Publication series

NameStudies in Systems, Decision and Control
Volume329
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Keywords

  • Amplitude-frequency characteristic
  • Least-squares support vector machines
  • Short-term wind power forecasting
  • Time series forecasting model

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)

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