A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model. (15th December 2015)

• Record Type: Journal Article
• Authors: Wang, Jianzhou; Hu, Jianming
• Is Part Of: Energy. Volume 93:Part 1(2015)
• Page Start: 41
• Page End: 56
• Publication Date: 2015-12-15
• Subjects: Gaussian Process Regression -- Wind speed forecasting -- Empirical Wavelet Transform -- Extreme Learning Machine -- Support Vector Machine; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.08.045
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. Highlights: The proposed approach can make probabilistic modeling for wind speed series. The proposed approach adapts to the time-varying characteristic of the wind speed. The hybrid approach can extract the meaningful components from the wind speed series. The proposed method can generate adaptive, reliable and more accurate forecasting results. The proposed model combines four independent forecasting engines in a nonlinear way.