A corrected hybrid approach for wind speed prediction in Hexi Corridor of China

Zhenhai Guo, Jing Zhao, Wenyu Zhang and Jianzhou Wang

Energy, 2011, vol. 36, issue 3, 1668-1679

Abstract: Wind energy has been well recognized as a renewable resource in electricity generation, which is environmentally friendly, socially beneficial and economically competitive. For proper and efficient evaluation of wind energy, a hybrid Seasonal Auto-Regression Integrated Moving Average and Least Square Support Vector Machine (SARIMA–LSSVM) model is significantly developed to predict the mean monthly wind speed in Hexi Corridor. The design concept of combining the Seasonal Auto-Regression Integrated Moving Average (SARIMA) method with the Least Square Support Vector Machine (LSSVM) algorithm shows more powerful forecasting capacity for monthly wind speed prediction at wind parks, when compared with the single Auto-Regression Integrated Moving Average (ARIMA), SARIMA, LSSVM models and the hybrid Auto-Regression Integrated Moving Average and Support Vector Machine (ARIMA–SVM) model. To verify the developed approach, the monthly data from January 2001 to December 2006 in Mazong Mountain and Jiuquan are used for model construction and model testing. The simulation and hypothesis test results show that the developed method is simple and quite efficient.

Keywords: Wind speed; Hybrid SARIMA–LSSVM model; Hypothesis test (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:36:y:2011:i:3:p:1668-1679

DOI: 10.1016/j.energy.2010.12.063

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