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      Adaptabilities of three mainstream short-term wind power forecasting methods

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          Most cited references 36

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          Support vector machines for wind speed prediction

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            Day-ahead wind speed forecasting using f-ARIMA models

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              Wind power forecasting using advanced neural networks models

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                Author and article information

                Journal
                Journal of Renewable and Sustainable Energy
                Journal of Renewable and Sustainable Energy
                AIP Publishing
                1941-7012
                September 2015
                September 2015
                : 7
                : 5
                : 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
                10.1063/1.4929957
                © 2015
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