Notice of Violation of IEEE Publication Principles: User Power Interaction Behavior Clustering Analysis That is Based on the Self-Organizing-Center K-Means Algorithm

Notice of Violation of IEEE Publication Principles

“User Power Interaction Behavior Clustering Analysis That is Based on the Self- Organizing-Center K-Means Algorithm”
by Shiyu Zhang and Shaoyun Ge
in IEEE Access, Volume 7, December 2019

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper translated and copied content from the original content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

“Clustering Analysis of User Power Interaction Behavior Based on Self-organizing Center K-means Algorithm”
by Bingyu Zhou, Bo Liu，Dan Wang，Yu Lan，Xiran Ma，Dongdong Sun, Qiuyi Huo
in Electric Power Construction, Vol 40, No 1, January 2019


The participation of power users in grid-side interactive power and auxiliary services has attracted increasing attention. The analysis of users' interaction power behaviors is a core task. By combining the self-organizing map (SOM) neural network and the K-means clustering algorithm, a self-organizing-center K-means algorithm is developed in this paper for conducting a cluster analysis of users' interaction electricity consumption behaviors. This approach can realize more accurate recognition and fast clustering. First, the principle of K-means algorithm in the self-organizing center is analyzed, and its advantages in the clustering analysis of electricity consumption over the traditional clustering algorithm are demonstrated. Then, in the context of peak-to-valley time-of-use electricity pricing, the adjustment potential index, which is based on user psychology, is developed, and a cluster analysis of users' electricity consumption behaviors that is based on load data and the adjustment potential index is conducted. Finally, the daily load data of users within the jurisdiction of a power company are studied, and the two-stage clustering results that are obtained via the self-organizing-center K-means algorithm are compared with the clustering results that are obtained via the K-means algorithm, which demonstrates the advantages of the self-organizing-center K-means algorithm that is based on the adjustment potential index in the accurate recognition and accurate clustering of users.