Adaptabilities of three mainstream short-term wind power forecasting methods

Yan, Jie; Gao, Xiaoli; Liu, Yongqian; Han, Shuang; Li, Li; Ma, Xiaomei; GU, Chenghong; Bhakar, Rohit; Li, Furong

doi:10.1063/1.4929957

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Adaptabilities of three mainstream short-term wind power forecasting methods

Author(s): Jie Yan ¹ , Xiaoli Gao ² , Yongqian Liu ¹ , Shuang Han ¹ , Li Li ¹ , Xiaomei Ma ¹ , Chenghong Gu ³ , Rohit Bhakar ³ , Furong Li ³

Publication date Created: September 2015

Publication date (Print): September 2015

Journal: Journal of Renewable and Sustainable Energy

Publisher: AIP Publishing

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Day-ahead wind speed forecasting using f-ARIMA models

Krithika Seetharaman, Rajesh Kavasseri (2009)

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On comparing three artificial neural networks for wind speed forecasting

Gong Li, Jing Shi (2010)

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Support vector machines for wind speed prediction

T.O. Halawani, Ahmed A. Hussain, S Rehman … (2004)

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Author and article information

Journal

Title: Journal of Renewable and Sustainable Energy

Abbreviated Title: Journal of Renewable and Sustainable Energy

Publisher: AIP Publishing

ISSN (Electronic): 1941-7012

Publication date Created: September 2015

Publication date (Print): September 2015

Volume: 7

Issue: 5

Page: 053101

Affiliations

[1 ]State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China

[2 ]Northwest Electric Power Design Institute Co., Ltd. of China Power Engineering Constitute Group, Xi'an 710075, China

[3 ]Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

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DOI: 10.1063/1.4929957

SO-VID: 59971fac-0a82-410a-aa94-16f326d0eb5c

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Adaptabilities of three mainstream short-term wind power forecasting methods

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Most cited references 36

Day-ahead wind speed forecasting using f-ARIMA models

On comparing three artificial neural networks for wind speed forecasting

Support vector machines for wind speed prediction

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