Accurate and efficient medium- and long-term forecasts of wind power can provide technical support for the efficient development and utilization of wind resources. Considering the regional characteristics of wind resources, the regional-similarity factor was introduced into the study of wind-power forecasting, and, to assess the long-term dependence of wind power, the long-short-term-memory method was selected for medium- and long-term forecasting of wind-power trends in a case study carried out in Northwest China. The results showed that the forecasting error of the presented method was reduced by an average of 20.80%, compared with the forecasting of individual stations, which verified the effectiveness of considering the regional characteristics in wind-resource prediction. Different area-division methods resulted in different effects on prediction accuracy. This study provides a new approach and a reference for medium- and long-term wind-resource prediction.

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.