Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration

Abstract

Wind power prediction decreases the uncertainty of the entire energy system, which is essential for balancing energy supply and demand. In order to improve the prediction accuracy, a short-term wind power prediction method based on data cleaning and feature reconfiguration is proposed. A large number of historical samples consisting of wind direction, wind speed, and wind power are mapped into a multidimensional sample space, and the distribution of wind data in different dimensions are analyzed in depth. By calculating the local density of each sample, outliers are effectively detected. The features of wind are reconfigured into a global information map combined with the time series information, which reflects the variation of the wind process in the short term. The features of the original data are greatly enriched, providing a high-quality training set for the prediction model. A redesigned convolutional neural network was used to predict short-term wind power, and the proposed methods were trained and tested based on a dataset of a real wind farm in China. Data cleaning and feature reconfiguration reduce the average single-point error by 1.38% and 2.56%, respectively, while the combined method reduced it by 6.24%. Plenty of experimental results show that the proposed methods achieve good performance and effectively improve the accuracy of short-term wind power prediction.