A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH. Issue 174 (March 2018)

• Record Type: Journal Article
• Authors: Jiang, Yan; Huang, Guoqing; Peng, Xinyan; Li, Yongle; Yang, Qingshan
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 174(2018)
• Page Start: 28
• Page End: 38
• Publication Date: 2018-03
• Subjects: One time decomposition -- Real time decomposition -- Correlation analysis -- Illusive component -- End effect -- Discrete wavelet transform -- Least squares support vector machine -- Generalized autoregressive conditionally heteroscedastic -- Wind speed prediction; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2017.12.019
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: This paper addresses the difference between one time and real time decompositions in the wind speed prediction and results show the real time decomposition-based method may be ineffective in practice. Then the comprehensive analysis about challenges on applying the real time decomposition-based method is conducted, which is less addressed in literature. Such challenges mainly include: (i) the subseries decomposed from the training part are constantly changing with newly obtained data; (ii) the illusive components introduced by the decomposition reduce decomposition effectiveness; (iii) the end effect increases subseries volatility. Furthermore, to reduce these difficulties in prediction, a new hybrid method of correlation-aided discrete wavelet transform (DWT), least squares support vector machine (LSSVM) and generalized autoregressive conditionally heteroscedastic (GARCH) model is proposed. In this method: (i) if the correlation coefficient between each subseries and original data is smaller than the selected threshold, the corresponding subseries will be eliminated as illusive component; (ii) GARCH model is used to characterize the error for the remaining subseries and better capture the volatility in these subseries; (iii) model parameters are adjusted in real time to better reflect the wind speed change. Finally, case studies show that the proposed method has satisfactory performance in both accuracy and stability. Highlights: Difference between one time and real time decompositions is revisited. Challenges on applying real time decomposition are discussed. A hybrid method is proposed based on the correlation analysis and error modeling. Proposed method noticeably enhances forecasting performance in accuracy and stability.