基于VMD-分形理论的短期电力负荷预测  

Short-Term Power Load Forecasting Based on VMD-Fractal Theory

作　　者：徐建军[1] 王硕昌 XU Jianjun;WANG Shuochang(School of Electrical Information and Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2022年第5期744-751,共8页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(51774088)。

摘　　要：为提高负荷预测结果的精度,设计了一种基于变分模态分解(VMD:Variational Mode Decomposition)和分形理论的短期电力负荷预测模型。首先选取和实测日气象数据相似的日期作为基准日,对基准日的负荷曲线进行变分模态分解提取主要信号的模态(趋势项),再提取扰动项(IMF1)和噪声项(IMF2、IMF3、IMF4),对趋势项和扰动项进行重标极差法分析后提取趋势项和扰动项的极值点建立迭代函数系统(IFS:Iterative Function System)。通过趋势项和扰动项的迭代函数曲线确定相对应的负荷数据,再同噪声项(IMF2、IMF3、IMF4)相对应的负荷数据相加,得到最终的预测数据。通过和传统的分形模型和BP(Back Propagation)神经网络模型进行对比,结果表明平均绝对百分比误差(MAPE:Mean Absolute PercentageError)下降了近5%,证明了VMD-分形预测模型的预测效果更好。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.

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 

分 类 号：TP305[自动化与计算机技术—计算机系统结构] TM715[自动化与计算机技术—计算机科学与技术]