Wind speed forecasting for wind farms: A method based on support vector regression. (January 2016)

• Record Type: Journal Article
• Authors: Santamaría-Bonfil, G.; Reyes-Ballesteros, A.; Gershenson, C.
• Is Part Of: Renewable energy. Volume 85(2016)
• Page Start: 790
• Page End: 809
• Publication Date: 2016-01
• Subjects: Wind speed forecasting -- Phase space reconstruction -- Support vector regression -- Genetic algorithms -- Non-linear analysis; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.07.004
• Languages: English
• ISSNs: 0960-1481

Abstract

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, 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. Highlights: Short-term wind speed forecasting was performed using non-linear and machine learning methods. Univariate wind data was mapped to a higher dimensional space by the phase space reconstruction procedure. SVR and a genetic algorithm estimates a representative function of site's wind speed using the mapped data. Lyapunov exponents and complexity measures revealed that data presents features of a chaotic processes.