Short-Term Wind Speed Forecasting Based on the EEMD-GS-GRU Model

Abstract

To improve the accuracy of short-term wind speed forecasting, we proposed a Gated Recurrent Unit network forecasting method, based on ensemble empirical mode decomposition and a Grid Search Cross Validation parameter optimization algorithm. In this study, first, in the process of decomposing, the set empirical mode of decomposition was introduced to divide the wind time series into high-frequency modal, low-frequency modal, and trend modal, using the Pearson correlation coefficient. Second, during parameter optimization, the grid parameter optimization algorithm was employed in the GRU model to search for the combination of optimal parameters. Third, the improved GRU model was driven with the decomposed components to predict the new components, which were used to obtain the predicted wind speed by modal reorganization. Compared with other models (i.e., the LSTM, GS-LSTM, EEMD-LSTM, and the EEMD-GS-LSTM), the proposed model was applied to the case study on wind speed of a wind farm, located in northwest China. The results showed that the presented forecasting model could reduce the forecasting error (RMSE) from 1.411 m/s to 0.685 m/s and can improve the accuracy of forecasts. This model provides a new approach for short-term wind speed forecasting.

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“…Moreover, the decomposition method has shown that through the decomposition process, each subsequence of the original signal can reveal the signal's distinct intrinsic features and obtain more features for the predicted signals. The empirical mode decomposition (EMD) technique has widely been used for decomposing original signals into their intrinsic multiscale features [21]. Tan [22] and Elias [23] each proposed a prediction model combining the EEMD method and the ML algorithms.…”