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

doi:10.1007/978-3-030-62147-6_15

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

^*Corresponding author for this work

Graduate School of Environment and Energy Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Studies in Systems, Decision and Control
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	395-411
Number of pages	17
DOIs	https://doi.org/10.1007/978-3-030-62147-6_15
Publication status	Published - 2021

Publication series

Name	Studies in Systems, Decision and Control
Volume	329
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)

Access to Document

10.1007/978-3-030-62147-6_15

Cite this

Ding, M., Wu, M., Yokoyama, R., Nakanishi, Y., & Zhou, Y. (2021). A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine. In Studies in Systems, Decision and Control (pp. 395-411). (Studies in Systems, Decision and Control; Vol. 329). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62147-6_15

A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine. / Ding, Min; Wu, Min; Yokoyama, Ryuichi et al.
Studies in Systems, Decision and Control. Springer Science and Business Media Deutschland GmbH, 2021. p. 395-411 (Studies in Systems, Decision and Control; Vol. 329).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Ding, M, Wu, M, Yokoyama, R, Nakanishi, Y & Zhou, Y 2021, A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine. in Studies in Systems, Decision and Control. Studies in Systems, Decision and Control, vol. 329, Springer Science and Business Media Deutschland GmbH, pp. 395-411. https://doi.org/10.1007/978-3-030-62147-6_15

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AU - Zhou, Yicheng

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

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

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