Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression

Abstract

Short-term wind speed forecasting plays a major role in wind energy plant operations and the integration of wind power into traditional grid systems. This paper proposes a hybrid model, which is composed of the EWT (empirical wavelet transform), PACE (partial auto-correlation function) and GPR (Gaussian process regression) method, for short-term wind speed prediction. In this proposed approach, the EWT is employed to extract meaningful information from the wind speed series by designing an appropriate wavelet filter bank, and the GPR simulates the internal uncertainties and dynamic features of the wind speed time-series using inputs identified by the PACF. The hybrid GPR model can offer point predictions and interval estimations of future wind speed. Additionally, this study adopts a moving window approach in the prediction process to deal adequately with the training data set, thereby adapting to the time-varying characteristic of the wind speed. The proposed hybrid model was validated with real mean half-hour wind speed data and hourly wind speed data. The computational results show that the suggested hybrid model favorably improves point wind speed forecasts in comparison with other models and provides satisfactory interval wind speed prediction.