A Hybrid Method for Short-Term Wind Speed Forecasting

Jinliang Zhang, YiMing Wei, Zhong-fu Tan, Ke Wang and Wei Tian
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Jinliang Zhang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
YiMing Wei: Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China
Zhong-fu Tan: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Wei Tian: School of Management and Economics, Illinois Institute of Technology, Chicago, IL 60616, USA
Authors registered in the RePEc Author Service: Yi-Ming Wei

Sustainability, 2017, vol. 9, issue 4, 1-10

Abstract: The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

Keywords: short-term wind speed forecasting; ensemble empirical mode decomposition (EEMD); adaptive neural network based fuzzy inference system (ANFIS); seasonal auto-regression integrated moving average (SARIMA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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