Ultra-short-term Wind power prediction algorithm based on bidirectional neural controlled differential equations

Abstract

Against the backdrop of the continuous growth of the new energy electricity trading market, improving the accuracy of ultra-short-term electricity forecasting and reducing lag are crucial for new energy enterprises. This article proposes a ultra-short-term wind power prediction model (Bi-NDCE-UPF) based on bidirectional neural control differential equations to explore ways to improve prediction accuracy and lag. It has two innovative points: 1. A bidirectional neural controllable ordinary differential model for ultra-short-term power forecasting has been proposed. Compared with Bi-GRU and Bi-LSTM, this model has significantly improved the accuracy and delay of the third point in ultra-short-term forecasting. 2. A prediction delay mitigation structure has been designed to effectively alleviate the lag and distortion of prediction data. This algorithm has been validated in four wind farms in central China and has unique advantages. We use four metrics to evaluate all models: MSE, MAE, Dynamic Time Warping (DTW), and Time Distortion Index (TDI). Compared with Bi-GRU, Bi-LSTM, and CNN-LSTM, the text model has significantly improved in terms of MSE and DTW. Compared with DLlinear and PatchTST models, the DTW and TDI models in this paper have better advantages.