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• W3032836820 abstract "In this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models." @default.
• W3032836820 created "2020-06-05" @default.
• W3032836820 creator A5005212618 @default.
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• W3032836820 date "2015-01-01" @default.
• W3032836820 modified "2023-10-03" @default.
• W3032836820 title "Wind speed forecasting for wind farms: A method based on support vector regression" @default.
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• W3032836820 hasPublicationYear "2015" @default.
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