Non-parametric hybrid models for wind speed forecasting

It is essential to predict the wind speed accurately in order for protecting the security of wind power integration. The aim of this study is to develop non-parametric hybrid models for probabilistic wind speed forecasting. By adopting the non-parametric models, two hybrid models, namely the hybrid autoregressive moving averageinon-parametric and hybrid non-parametric/autoregressive moving average models, are proposed and their performance is compared. In the hybrid autoregressive moving average/nonparametric models, the residuals obtained after fitting with the autoregressive moving average model are studied by the non-parametric model to extract the nonlinear part of the data. In the hybrid nonparametric/autoregressive moving average models, the residuals obtained from the non-parametric model are fitted by the autoregressive moving average model. In order for comparisons, the artificial neural network with back propagation, support vector machine and random forest models are also introduced for hybrid modeling. Through conducting various tests on the real hourly wind speed time series, the prediction performance of both single and hybrid models is compared and evaluated in detail. The results of this study are to show that non-parametric based hybrid models generally outperform the other models and have more robust forecast performances. When the single autoregressive moving average model basically outperforms the single non-parametric models, the introduction of the autoregressive moving average model for the residuals from the non-parametric fitting is possible to obtain better prediction accuracy. (C) 2017 Elsevier Ltd. All rights reserved.