A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting

Min Ding, Hao Zhou, Hua Xie, Min Wu, Kang Zhi Liu, Yosuke Nakanishi, Ryuichi Yokoyama

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a time series model based on hybrid-kernel least-squares support vector machine (HKLSSVM) with three processes of decomposition, classification, and reconstruction is proposed to predict short-term wind power. Firstly, on the basis of the maximal wavelet decomposition (MWD) and fuzzy C-means algorithm, a decomposition method decomposes wind power time series and classifies the decomposition time series components into three classes according to amplitude–frequency characteristics. Then, time series models on the basis of least-squares support vector machine (LSSVM) with three different kernels are established for these three classes. Non-dominated sorting genetic algorithm II optimizes the parameters of each forecasting model. Finally, outputs of forecasting models are reconstructed to obtain the forecasting power. The proposed model is compared with the empirical-mode-decomposition least-squares support vector machine (EMD-LSSVM) model and wavelet-decomposition least-squares support vector machine (WDLSSVM) model. The results of the comparison show that proposed model performs better than these benchmark models.

Original languageEnglish
JournalISA Transactions
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Least-squares support vector machines
  • Short-term wind power forecasting
  • Time series forecasting model
  • Wavelet decomposition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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