Ultra-short term prediction of wind power based on multi-resolution closed-loop method

Abstract

In order to improve the accuracy and stability of wind power prediction, a multi-resolution closed-loop wind power ultra-short term prediction method is proposed. Using historical data with different resolutions, the differential data is generated by the pre-predictor with low, medium and high resolutions, and then the differential data is combined with the post-prediction data into the post-predictor with three resolutions for training. Since the differential data is obtained from the prediction data with different resolutions, the post-prediction data containing the differential data contains inconsistent information between different resolutions, so that the post-predictor receives the inconsistency between different resolutions and minimizes the inconsistency after several closed-loop iterations, and finally outputs wind power prediction with different resolutions. At the end of the paper, simulation experiments are carried out using the data of the 2022 KDD Cup competition, and comparison is made with some mainstream wind power prediction models. It is found that the proposed model has high accuracy and stability.