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  • Performance of different hybrid algorithms for prediction of wind speed behavior

    Author(s)
    Mostafaeipour, Ali
    Goli, Alireza
    Rezaei, Mostafa
    Qolipour, Mojtaba
    Arabnia, Hamid-Reza
    Goudarzi, Hossein
    Behnam, Elham
    Griffith University Author(s)
    Rezaei, Mostafa
    Year published
    2019
    Metadata
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    Abstract
    This study seeks to provide a new method by proposing three hybrid algorithms. The proposed algorithms include genetic neural network hybrid algorithm, simulated annealing neural network hybrid algorithm, and shuffled frog-leaping neural network hybrid algorithm. The efficiency and reliability of the presented hybrid algorithms in prediction of wind speed behavior were evaluated using meteorological data of the city of Abadeh for a 10-year period from 2005 to 2015. The forecasting horizon is monthly for this study. The study parameters consisted of TMAX, TMIN, VP, RHMIN, RHMAX, WIND SPEED, PRECIPITATION, and SUNSHINE HOURS. ...
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    This study seeks to provide a new method by proposing three hybrid algorithms. The proposed algorithms include genetic neural network hybrid algorithm, simulated annealing neural network hybrid algorithm, and shuffled frog-leaping neural network hybrid algorithm. The efficiency and reliability of the presented hybrid algorithms in prediction of wind speed behavior were evaluated using meteorological data of the city of Abadeh for a 10-year period from 2005 to 2015. The forecasting horizon is monthly for this study. The study parameters consisted of TMAX, TMIN, VP, RHMIN, RHMAX, WIND SPEED, PRECIPITATION, and SUNSHINE HOURS. These eight parameters are used as the inputs, and one parameter (ET) is used as the output. Research findings show that the shuffled frog-leaping neural network hybrid algorithm providing a root mean square error value of 0.0761 and reliability of 0.91 is more suitable than other hybrid algorithms for prediction of wind speed behavior in the study area.
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    Journal Title
    Wind Engineering
    Volume
    45
    Issue
    2
    DOI
    https://doi.org/10.1177/0309524X19882431
    Subject
    Environmental engineering
    Maritime engineering
    Science & Technology
    Energy & Fuels
    Wind speed prediction
    neural network
    Publication URI
    http://hdl.handle.net/10072/408535
    Collection
    • Journal articles

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