A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH

作者：Jiang, Yan; Huang, Guoqing^*; Peng, Xinyan; Li, Yongle; Yang, Qingshan

来源：Journal of Wind Engineering and Industrial Aerodynamics, 2018, 174: 28-38.

DOI：10.1016/j.jweia.2017.12.019

• 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

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.

• 出版日期2018-3
• 单位重庆大学; 西南交通大学