关键词: 变分模态分解, 分形理论, 迭代函数系统, 电力负荷预测, 重标极差法, 分形插值函数

Abstract: In order to improve the accuracy of load prediction results, a short-term power load prediction model based on VMD(Variational Mode Decomposition) and fractal theory is designed. Firstly, the date similar to the measured meteorological data is selected as the reference date, and the modal of the main signal (trend term) is extracted from the load curve of the reference date through variational modal decomposition. After extracting the disturbance term ( IMF1 ) and noise term ( IMF2, IMF3, IMF4 ), the extreme points of the trend term and disturbance term are extracted after re-scaling range analysis, and the IFS ( Iterative Function System) is established. The load data corresponding to the trend term and disturbance term are determined by the iterative function curve, and then added to the load data corresponding to the noise term (IMF2,IMF3,IMF4) to obtain the final forecast data. Compared with the traditional fractal model and BP(Back Propagation) neural network model, the mean absolute percentage error (MAPE: MeanAbsolute Percentage Error) decreases by 5% , which proves that vmD-fractal prediction model has better prediction effect. 

Key words: variational mode decomposition, fractal theory, Iterative function system, power load forecasting, rescale range analysis, fractal interpolation system