Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1464389

An Approach to Model and Method for Current Pattern Mining for Electricity Consumption

Provisionally accepted
Zhenya Zhang Zhenya Zhang 1Hongmei Cheng Hongmei Cheng 1*Ping Wang Ping Wang 1*Shugang Zhang Shugang Zhang 2*
  • 1 Anhui Jianzhu University, Hefei, China
  • 2 University of Science and Technology of China, Hefei, Anhui Province, China

The final, formatted version of the article will be published soon.

    In the realm of intelligent electricity, it is a prevalent requirement for particular research and applications to ascertain the running state of appliances/devices based on the electricity situation of users. In this paper, we present the CPARSIM (Current pattern-based appliance running state identification model) to identify the running states of appliances based on current online data. Based on frequent online power situation data, current online data sequences can be segmented into some sequence pieces with different lengths. Within the framework of CPARSIM, variable-length pieces of current state sequences are represented by their univariate regression features, with each sequence piece being treated as one point within the two-dimensional feature space. Considering the relation between the appliance running state and current state patterns, the problem of current state pattern set mining is modeled as a cluster analysis problem within CPARSIM, and the presented approach employs a DBSCAN algorithm-based technique to mine the current state pattern set. Experimental results show that the DBSCAN algorithm-based approach for the current state pattern set mining is more effective than the k-means algorithm and SOM neural network.

    Keywords: current sequence piece, current state pattern, Univariate regression model, PSO algorithm, clustering, IDENTIFICATION

    Received: 17 Jul 2024; Accepted: 21 Nov 2024.

    Copyright: © 2024 Zhang, Cheng, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hongmei Cheng, Anhui Jianzhu University, Hefei, China
    Ping Wang, Anhui Jianzhu University, Hefei, China
    Shugang Zhang, University of Science and Technology of China, Hefei, 230026, Anhui Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.