TY - JOUR T1 - Wind speed forecasting for wind farms: A method based on support vector regression JF - Renewable Energy Y1 - 2016 A1 - Santamaría-Bonfil, G. A1 - Reyes-Ballesteros, A. A1 - Gershenson, C. KW - Genetic algorithms KW - Non-linear analysis KW - Phase space reconstruction KW - Support vector regression KW - Wind speed forecasting AB - 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, México, 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 (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models. VL - 85 SN - 0960-1481 UR - http://www.sciencedirect.com/science/article/pii/S0960148115301014 ER -