Paper Info
Title
Self-similarity Modeling Research on Information Gathering Service for Power Utilization of Smart Grid.
Abstract
Given the self-similarity characteristic of the communication traffic-flow in the current information gathering service traffic in power utilization network, a self-similarity modeling method is proposed to quantify the feature analysis of the communication flow for the power information gathering service. Firstly, the flowu0027s characteristic and related course of the information gathering service traffic is analyzed, and a self-similarity model is built by means of combining the multiple ON/OFF flows. Secondly, the Hurst coefficient of the service simulation traffic is estimated using Variance-Time method to quantify the flowu0027s self-similarity with the estimation results of Hurst coefficient. Finally, it discusses the influence of Hurst coefficient is discussed according to the two factors including the acquisition accuracy and the gathering periodic. Experimental results illustrate that the proposed method is effective to model the self-similarity of the information gathering service for the power utilization in Smart Grid.
Year
DOI
Venue
2018
10.1109/FSKD.2018.8687280
ICNC-FSKD
Field
DocType
Citations 
Data mining,Smart grid,Computer science,Flow (psychology),Hurst exponent,Artificial intelligence,Self-similarity,Periodic graph (geometry),Machine learning,Pattern recognition (psychology)
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Shidong Chen100.68
Xiangqun Chen235834.11
Junhua Hu31228.94
jun lu495.04
Zesheng Hu500.34