The Application of Cluster Analysis Algorithm in the Indicators Comparison of Grid Enterprise

Xue, Wan Lei; Lu, Kuan; Zhao, Xin; Liu, Xiao Li; Xu, Nan; Wei, Yang

doi:10.4028/www.scientific.net/amr.986-987.581

Cited by 2 publications

(2 citation statements)

References 1 publication

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) In terms of power grid development, scholars are concerned about the balance of power and electricity [6] , the establishment of distributed energy systems [7] , the grid connection of power terminals [8] , the operating conditions of power enterprises [9][10][11] , and customer satisfaction [12,13] and The demand and pricing of power projects [14] are evaluated and discussed, the complex indicators are streamlined, and the method of analytic hierarchy and entropy weight combination is used to determine the indicator weight, which makes up for the shortcomings in the single subjective or objective weight determination process. Some scholars have also conducted research on the current development level of power grids [15,16] , used the method of cluster analysis to classify provincial-level power supply companies according to power supply quality indicators, and identify the gap between standard companies and industry benchmarks, so as to continuously improve management levels and benefits. Xie, P et al [17] started from China's power productivity and obtained the regional differences and characteristics of the development of the power industry in different regions of China.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation Model of Physical Asset Management Maturity of China's Inter-provincial Grid Enterprises

Zhang¹,

Xia²,

Xu³

et al. 2023

Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022

View full text Add to dashboard Cite

A comprehensive evaluation of the physical assets of power grid enterprises is conducive to promoting the management of assets of enterprises and avoiding the loss and waste of state-owned assets. This paper collects the objective statistics of the enterprise and the subjective scoring data of the experts in the index system for the past 5 years, and determines the comprehensive weight of each index through the combination of subjective and objective weights. Grey correlation and topsis method is used to establish a dynamic evaluation model for objective data to capture the trend of the comprehensive value of physical assets, and subjective data is used to establish a maturity model to calculate the maturity level of physical assets. In this way, the results of dynamic assessment and maturity assessment are combined to obtain the results of grid enterprise asset evaluation under multi-dimensional dynamic perspective. Finally, an empirical analysis using a grid data shows that the company should learn from the scientific management experience of 2016, maintain a high level of asset structure and utilization efficiency, and focus on improving the optimal management of asset decommissioning and health.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation Model of Physical Asset Management Maturity of China's Inter-provincial Grid Enterprises

Zhang¹,

Xia²,

Xu³

et al. 2023

Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022

View full text Add to dashboard Cite

show abstract

“…Ref. [6] made K-means clustering algorithm apply to the indicators comparison of power enterprises. And through the example, it is showed that the proposed algorithm is effective.…”

Section: Introductionmentioning

confidence: 99%

K-means and Support Vector Machine in Electric Power Company Benchmarking Management

Zhang¹

2016

Proceedings of the 3d International Conference on Applied Social Science Research

View full text Add to dashboard Cite

In the electric power company benchmarking management, implementing classification of the enterprise, the clustering algorithm can set up the model enterprise. It's very important for the benchmarking management in the electric power company. K-means, as unsupervised learning algorithm, is suitable for processing great sample data, while support vector machine(SVM), as supervised learning algorithm, needs a small number of training samples and is able to obtain the higher classification accuracy. Therefore, the paper presented a classification method based on the combination of SVM and Kmeans. Using K-means clustered index data first, and then chose some samples which were close to each cluster center as study samples to training SVM classifier and classified all the index data with SVM classifier. Consequently, illustration showed that K-means combined with SVM had higher accuracy than K-means, which testified the validity of it.

show abstract

The Application of Cluster Analysis Algorithm in the Indicators Comparison of Grid Enterprise

Cited by 2 publications

References 1 publication

Research on the Evaluation Model of Physical Asset Management Maturity of China's Inter-provincial Grid Enterprises

Research on the Evaluation Model of Physical Asset Management Maturity of China's Inter-provincial Grid Enterprises

K-means and Support Vector Machine in Electric Power Company Benchmarking Management

Contact Info

Product

Resources

About