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Volume 48 Issue 2
Feb.  2022
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Journal of Beijing University of Aeronautics and Astronautics  > 2022  >  48(2): 311-323.
LU Ruirui, YU Haiyang, YANG Zhen, et al. User electricity consumption behavior mode analysis based on energy decomposition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(2): 311-323. doi: 10.13700/j.bh.1001-5965.2020.0557(in Chinese)
Citation: LU Ruirui, YU Haiyang, YANG Zhen, et al. User electricity consumption behavior mode analysis based on energy decomposition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(2): 311-323. doi: 10.13700/j.bh.1001-5965.2020.0557(in Chinese)
shu

User electricity consumption behavior mode analysis based on energy decomposition

doi: 10.13700/j.bh.1001-5965.2020.0557

  • Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Funds:

National Natural Science Foundation of China 61671030

Beijing Great Wall Scholar CIT & TCD20190308

China Postdoctoral Science Foundation 2019M660377

More Information
  • Corresponding author: YANG Zhen, E-mail: yangzhen@bjut.edu.cn
  • Received Date: 27 Sep 2020
  • Accepted Date: 06 Nov 2020
  • Publish Date: 20 Feb 2022
    Abstract

  • With the popularization of smart grids and the development of big data technology, more and more attention has been paid to the analysis of users' electricity consumption behavior through electricity consumption data. The existing energy decomposition methods cannot meet the high requirements for resolution and decomposition accuracy in practical applications, and the cluster analysis method is too rough and does not fully show the electricity consumption characteristics of each type of electrical appliances. In view of this, this paper proposes an analysis method of users' electricity consumption behavior based on energy decomposition. Based on the discriminative sparse coding algorithm model, firstly, to solve the problem that the regular term of L0 is not easy to solve and the effect of the sparse constraint of L1 regular term is not ideal, we propose to use the sparse constraint of L1/2 regular term to perform energy decomposition, and add the homogeneity between users as a regular term to the basic model to modify the performance of the model. Secondly, based on the results of energy decomposition, we use the electricity consumption characteristics of a user's single-type electrical appliances instead of the total electricity consumption characteristics to refine the analysis of user's electricity consumption behavior, and improve the traditional K-Mean clustering algorithm for experimental verification. The experimental results show that the energy decomposition method based on the sparse constraint of L1/2 regular term and the constraint of homogeneity can effectively improve the accuracy of energy decomposition compared with the traditional discriminative sparse coding method. At the same time, the result of cluster analysis of users' electricity consumption behavior based on energy decomposition is also significantly improved.

     

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