基于双通道生成对抗网络的城市用电负荷缺失数据补全方法  

Completion Method for Missing Urban Power Load Data Based on Double-channel Generative Adversarial Networks

作　　者：刘志坚[1] 陶韵旭 刘航 罗灵琳[1] 李明 LIU Zhijian;TAO Yunxu;LIU Hang;LUO Linglin;LI Ming(Faculty of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学电力工程学院,云南省昆明市650500

出　　处：《电力系统自动化》2024年第17期161-170,共10页Automation of Electric Power Systems

基　　金：云南省基础研究计划重点项目(202301AS070055);云南省基础研究计划青年项目(202201AU070086);云南省基础研究计划面上项目(202401AT070356)。

摘　　要：用电负荷数据的完整性与有效性在负荷预测等应用中具有重要意义。传统的缺失数据补全方法缺乏对用电负荷和多种外部时空关联信息的挖掘,难以获得高精度的补全结果。文中提出了一种双通道生成对抗网络,对缺失的负荷数据进行补全。首先,根据负荷的周期性变化特征和时空关联性构建三阶负荷张量,并将影响负荷变化的多种外部因素构建为三阶辅助信息张量。然后,为满足两种张量的双输入需求,在生成对抗网络的输入层引入双通道机制,通过卷积与反卷积运算提取张量的特征;为提升网络对张量数据的训练效果和补全精度,将张量分解损失引入原始损失函数,并采用改进的混沌映射粒子群优化算法联合优化超参数和网络。最后,在真实负荷数据集上开展数据补全实验。结果表明,所提方法能够对随机缺失率不超过50%、连续缺失不超过3天的负荷数据进行准确补全。The integrity and validity of load data are of great significance in load forecasting and other applications.The traditional completion methods for missing data lack the mining of the power load and the various external spatio-temporal correlation information,so it is difficult to obtain high-precision completion results.In this paper,a double-channel generative adversarial network is proposed to complete the missing load data.First,a third-order load tensor is constructed according to the periodic variation characteristics of load and the spatio-temporal correlation,and a variety of external factors affecting load changes are constructed as the third-order auxiliary information tensors.Then,in order to meet the double input requirements of the two tensors,a double-channel mechanism is introduced at the input layer of the generative adversarial network,and the features of the tensors are extracted by convolution and deconvolution operations.In order to improve the training effect and completion accuracy of network on tensor data,the tensor decomposition loss is introduced into the original loss function,and an improved chaotic particle swarm optimization algorithm is used to jointly optimize the hyperparameters and the network.Finally,the data completion experiment is carried out on the real load data set.The results show that the proposed method can accurately complete the load data with random loss rate of less than 50%and continuous loss of less than 3 days.

关 键 词：负荷数据缺失 负荷预测 三阶张量 生成对抗网络 分解损失 混沌映射粒子群优化算法 补全方法 

分 类 号：TM715[电气工程—电力系统及自动化]