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

被引:159
作者
Guo, Zhenhai [2 ]
Zhao, Jing [1 ]
Zhang, Wenyu [3 ]
Wang, Jianzhou [1 ]
机构
[1] Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Gansu, Peoples R China
[2] Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China
[3] Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Gansu, Peoples R China
关键词
Wind speed; Hybrid SARIMA-LSSVM model; Hypothesis test; SUPPORT VECTOR MACHINES; SHORT-TERM PREDICTION; TIME-SERIES; NEURAL-NETWORK; COMBINATION; ARIMA;
D O I
10.1016/j.energy.2010.12.063
中图分类号
O414.1 [热力学];
学科分类号
070201 [理论物理];
摘要
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. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1668 / 1679
页数:12
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