Power Load Prediction Method Based on VMD and Dynamic Adjustment BP

Abstract

Aiming at the shortcomings of low prediction accuracy due to the randomness and complexity of power load data, this paper bring up a power load prediction method on the strength of VMD and dynamic adjustment BP. Firstly, for the redundant information and trend components contained in the original data of the power load, the VMD decomposed component reconstruction is used to remove the trend component and the redundant information. Secondly, after the VMD detrended, there is a disadvantage that the fixed points in traditional BP neural network prediction may cause low accuracy, the dynamic adjustment of nodes is designed to achieve the optimal prediction. Finally, based on the electric load data provided by Chongqing Tongnan Electric Power Co., Ltd., the prediction model put forward in this paper is used to estimate the electric load. The comparison of the example simulation results shows that the predicted values of the VMD and the dynamically adjusted BP cooperative electric load forecasting method are closer to the real one. The load value and the prediction error are lower, which is a better short-term power load forecasting method.