Short-term wind speed prediction using an extreme learning machine model with error correction

Abstract

Wind speed forecasting is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this challenge, a new hybrid model is proposed for short-term wind speed forecasting, where the short-term forecasting period is ten minutes. The model combines extreme learning machine with improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and autoregressive integrated moving average (ARIMA). The extreme learning machine model is employed to obtain short-term wind speed predictions, while the autoregressive model is used to determine the best input variables. An ensemble method is used to improve the robustness of the extreme learning machine. To improve the prediction accuracy, the ICEEMDAN-ARIMA method is developed to postprocess the errors; this method can also be used to preprocess original wind speed. Additionally, this paper reports the results of a comparative study on preprocessing and postprocessing time series data. Three experimental results show that: (1) the error correction is effective in decreasing the prediction error, and the proposed models with error correction are suitable for short-term wind speed forecasting; (2) the ICEEMDAN method is more powerful than other variants of empirical mode decomposition in performing non-stationary decomposition, and the ICEEMDAN-ARIMA method achieves satisfactory performance both for preprocessing and postprocessing; and (3) for prediction, the preprocessing of time series is more effective than its postprocessing.