A novel wind power prediction model based on <scp>PatchTST</scp> and temporal convolutional network

Abstract Due to the unpredictable nature of wind, wind power forecasting still faces certain challenges. The accuracy of wind power prediction plays a crucial role in the stability of the whole system. To improve the accuracy of wind power prediction, this research proposed an innovative hybrid prediction model that utilizes a multi‐layer perceptron, combined with a temporal convolutional network and PatchTST. Firstly, a multi‐layer perceptron is introduced to capture higher‐order features, and a temporal convolutional network is used to extract time‐domain features from the dataset to capture the dynamic changes of wind speed; then, PatchTST is used to accurately forecast wind power. The results show that the proposed model performs well in terms of prediction accuracy and prediction speed. The minimal MAPE is 14.4%, the prediction accuracy is improved by 9.22%, and the power generation efficiency is increased by 0.31%. In addition, this research used Bootstrapping to estimate the probability interval of wind power to provide a more comprehensive wind power forecast. This study provides a new and effective tool in the field of wind power forecasting, helping to improve the stability of power systems.