 A hybrid forecasting approach applied to wind speed time seriesJianming Hu, Jianzhou Wang and Guowei Zeng Renewable Energy, 2013, vol. 60, issue C, 185-194 Abstract: In this paper, a hybrid forecasting approach, which combines the Ensemble Empirical Mode Decomposition (EEMD) and the Support Vector Machine (SVM), is proposed to improve the quality of wind speed forecasting. The essence of the methodology incorporates three phases. First, the original data of wind speed are decomposed into a number of independent Intrinsic Mode Functions (IMFs) and one residual series by EEMD using the principle of decomposition. In order to forecast these IMFs, excepting the highest frequency acquired by EEMD, the respective estimates are yielded using the SVM algorithm. Finally, these respective estimates are combined into the final wind speed forecasts using the principle of ensemble. The proposed hybrid method is examined by forecasting the mean monthly wind speed of three wind farms located in northwest China. The obtained results confirm an observable improvement for the forecasting validity of the proposed hybrid approach. This tool shows great promise for the forecasting of intricate time series which are intrinsically highly volatile and irregular. Keywords: Wind farm; Wind speed forecasting; Ensemble Empirical Mode Decomposition (EEMD); Support Vector Machine (SVM) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:60:y:2013:i:c:p:185-194 DOI: 10.1016/j.renene.2013.05.012 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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