User Behavior Analysis Based on Stacked Autoencoder and Clustering in Complex Power Grid Environment

被引:23
作者
Deng, Song [1 ]
Cai, Qingyuan [2 ]
Zhang, Zi [3 ]
Wu, Xindong [4 ,5 ]
机构
[1] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210003, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210003, Peoples R China
[3] Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin 541004, Peoples R China
[4] Hefei Univ Technol, Key Lab Knowledge Engn Big Data, Minist Educ, Hefei 230009, Peoples R China
[5] Mininglamp Technol, Mininglamp Acad Sci, Shanghai 200232, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature extraction; Clustering algorithms; Analytical models; Power demand; Correlation; Power grids; Load modeling; Feature selection; stacked autoencoder; unsupervised learning; power consumption behavior analysis; electric vehicle charging behavior analysis; LOAD; NETWORKS;
D O I
10.1109/TITS.2021.3076607
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.
引用
收藏
页码:25521 / 25535
页数:15
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