期刊
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
卷 174, 期 -, 页码 28-38出版社
ELSEVIER
DOI: 10.1016/j.jweia.2017.12.019
关键词
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
资金
- National Natural Science Foundation of China [U1334201, 51578471]
- Youth Fund Program of Sichuan Province [2016JQ0005]
- Fundamental Research Funds for the Central University [106112017CDJQJ208849]
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.
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