loading page

Time-of-Use Period Partition Based on Improved Fuzzy C-Means and Abnormal Period Correction
  • +1
  • Peng Wang,
  • Yiwei Ma,
  • Zhiqi Ling,
  • Gen-hong Luo
Peng Wang
Chongqing University of Posts and Telecommunications
Author Profile
Yiwei Ma
Chongqing University of Posts and Telecommunications

Corresponding Author:mayw@cqupt.edu.cn

Author Profile
Zhiqi Ling
Chongqing University of Posts and Telecommunications
Author Profile
Gen-hong Luo
Chongqing University of Posts and Telecommunications
Author Profile

Abstract

In time-of-use tariff period partition, clustering algorithms are commonly used. However, as load demands become more diverse in this big data era, large amount of non-linear data makes conventional clustering algorithms methods no longer be applicable in this field alone. Facing high-time-resolution daily load data with strong non-linearity, we propose a new method to partition periods. It consists of an improved fuzzy c-means clustering algorithm and a correction method for abnormal periods. Firstly, we propose modified fuzzy membership functions to improve the initialization of clustering for operation efficiency. Secondly, the method for calculating the fuzzy parameters based on the loss function is given. Thirdly, the initial period partition is obtained by the improved clustering. Next, the recognition model and fuzzy subsethood-based correction model for abnormal periods are structed, then the corrected period partition is confirmed. Finally, the effectiveness of the proposed methods is verified by two daily load data with a time resolution of 5 minutes.
21 Jun 2023Submitted to IET Generation, Transmission & Distribution
23 Jun 2023Submission Checks Completed
23 Jun 2023Assigned to Editor
27 Jun 2023Reviewer(s) Assigned
29 Aug 2023Review(s) Completed, Editorial Evaluation Pending
09 Sep 2023Editorial Decision: Revise Major
30 Sep 20231st Revision Received
03 Oct 2023Submission Checks Completed
03 Oct 2023Assigned to Editor
03 Oct 2023Review(s) Completed, Editorial Evaluation Pending
03 Oct 2023Reviewer(s) Assigned
05 Nov 2023Editorial Decision: Accept