WTCGRU: Short-term wind speed prediction based on window feature importance sampling

Abstract

In short-term wind speed prediction, different features in the time window have different degrees of influence on the prediction results. However, the importance of different features in the time window is mostly treated equally, which results in the deficiency of responding promptly to the time-varying characteristics of wind speed and treating differently the time window feature weights that affect the prediction accuracy. Hence, a novel model called WTCGRU is proposed to improve the prediction accuracy by fully considering the influence of different features in the time window during wind speed prediction. In WTCGRU, the weights of different features in the time window are sampled through an adaptive genetic approach, which is called importance sampling, and fed into a hybrid model composed of GRU and TCN, to predict more precisely the short-term wind speed; This step is executed repeatedly until WTCGRU's prediction accuracy is higher than a predefined threshold. Finally, theoretical experiments are carried out on the wind speed dataset from Szeged of Hungary, and it is shown that the WTCGRU has outperformed the mainstream short-term wind speed prediction model TCN, BILSTM, CNN-LSTM, and ensemble-CNN.