Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid 


Vol. 39,  No. 1, pp. 72-81, Jan.  2014


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  Abstract

Recently, because of irregular power demand, we have suffered from an electric power shortage. The necessity of the adoption of smart grid which makes effective supply of power by using the two-way communication across the grid between the customers and electric energy providers is growing more and more. If smart grid set up in our country, the third-parties which provide services to customer using the information acquired from smart grid, might be revved up. In this paper, we suggest a methodology how classify the types of family by analysing an power consumption pattern using data mining technique. To make a classifier for categorizing the household types, we need power consumption data and their family type. However, it is hard to get both of them. Therefore we develop the simulator that generates power consumption patterns of the household and classify the types of family. Also, we present a potential for application services such as customized services for a specific family or goods marketing.

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  Cite this article

[IEEE Style]

J. Kim, Y. Lee, H. Kim, "Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 1, pp. 72-81, 2014. DOI: .

[ACM Style]

Ji-hyun Kim, Yun-jin Lee, and Ho-won Kim. 2014. Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid. The Journal of Korean Institute of Communications and Information Sciences, 39, 1, (2014), 72-81. DOI: .

[KICS Style]

Ji-hyun Kim, Yun-jin Lee, Ho-won Kim, "Development of the Power Consumption Simulator and Classification of the Types of Household by Using Data Mining Over Smart Grid," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 1, pp. 72-81, 1. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880