A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting

The accuracy of the wind speed prediction is of crucial significance for the operation and dispatch of the power grid system reasonably. However, wind speed is so random and intermittent that the accuracy of wind speed prediction always remains unsatisfactory. Moreover, the coupling relationship between other meteorological variables and wind speed in the time and frequency domains has rarely been studied. Subsequently, a hybrid wind speed prediction model based on weather research and forecasting (WRF) simulation is proposed according to a multivariate data decomposition method and deep learning algorithm optimized by an attention mechanism and a grid search algorithm. Firstly, the WRF simulation is utilized to obtain the predicted wind speed and other meteorological variables are also extracted from WRF different domains. Furthermore, the pearson correlation coefficient (PCC) method is adopted to select principal meteorological variables as the input series. Additionally, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method decomposes input series and historical data into respective intrinsic mode functions (IMFs). Then, a new hybrid deep learning model, combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BLSTM) optimized via an attention mechanism (AM) and a grid search method (GS), is proposed to predict the error and correct the wind speed from WRF innermost domain. Finally, the validation case study is conducted to verify the effectiveness of the proposed model. The results indicate that the proposed model outperforms other comparative models in terms of single-step and multi-step wind speed prediction. Specifically, the values of the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) are 0.1042 m/s, 4.63% and 0.1309 m/s after correction, decreased by 94.13%, 91.75% and 93.93%, respectively, compared to those without correction.