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

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)58-68
ページ数11
ジャーナルISA Transactions
108
DOI
出版ステータスPublished - 2021 2

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 器械工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

フィンガープリント

「A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル