A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

Abstract

The share of wind power in global electricity generation is increasing year by year, and the prediction of wind power is a practical and necessary scientific research. In this paper, the TimesNet model is used for multistep prediction of wind power. It is observed that the prediction error of the TimesNet model could be improved by replacing the original convolutional structure with the dilated convolution and weight normalization (WN). And in addition, the population-based training (PBT) algorithm is introduced to guide the model tuning. Missing values in the raw data are imputed to enhance data integrity. And the maximal information coefficient (MIC) method is chosen to select features for the different influences on wind power, which reduces the computational effort of the model. In order to demonstrate the effectiveness of the proposed model MIC-PBT-DWTimesNet, nine models are arranged in this paper as a control group and compared with their predicted values with each other. It can be observed that the RMSE of the proposed model MIC-PBT-DWTimesNet is decreased by 17–33% compared to the base TimesNet method.