摘要
为有效解决用电信息采集系统中电量数据丢失问题,提出基于正则自编码器的缺失数据填补方法。首先,根据正则自编码器学习到的特征重构电量数据,实现缺失数据的修复。然后,通过对损失函数增加L21范数及正交约束实现正则化,提升模型的泛化能力,并采用Optuna实现超参数的自动寻优。最后,实际数据集的测试结果表明:与其他自编码器相比,正则自编码器能够较为准确地补齐缺失数据。
In order to effectively solve the problem of consumption loss in the electric energy information acquisition system,a method of filling missing data based on regular self-encoders is proposed.Firstly,the energy data according to the characteristics learned by the regular autoencoder is reconstructed,and the repair of the missing data is realized.Then,regularization by adding the L21-norm is realized and orthogonal constraints to the loss function,the generalization ability of the model and uses Optuna to realize the automatic optimization of hyperparameters is improved.Finally,the test results of the actual data set show that compared with other autoencoders,the regular autoencoder can accurately fill in the missing data.
作者
陈慧
陈适
郭银婷
连淑婷
王康
韦先灿
CHEN Hui;CHEN Shi;GUO Yinting;LIAN Shuting;WANG Kang;WEI Xiancan(Marketing Service Center,State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350001,China;College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)
出处
《电力需求侧管理》
2023年第5期53-58,共6页
Power Demand Side Management
基金
国网福建省电力有限公司科技项目(52130X21001A)。
