Browse > Article
http://dx.doi.org/10.7840/kics.2014.39C.1.72

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

Kim, Ji-Hyun (부산대학교 전자전기컴퓨터공학과 정보보호 및 IoT 연구실)
Lee, Yun-Jin (부산대학교 전자전기컴퓨터공학과 정보보호 및 IoT 연구실)
Kim, Ho-Won (부산대학교 전자전기컴퓨터공학과 정보보호 및 IoT 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.39C, no.1, 2014 , pp. 72-81 More about this Journal
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.
Keywords
smartgrid; powersimulator; familyclassifier; datamining; powerconsumption;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 F. Hassan, "The path of the smart grid," Power and Energy Mag., IEEE, vol. 8, no. 1, pp. 18-28, 2010.
2 S. Mun, "The concept of smart grid," Information & Communications Mag., vol. 27, no. 4, pp. 3-9, Mar. 2010.
3 N.-K. Won, B.-N. Ha, S.-W. Lee, I.-Y. Seo, and M.-J. Jang. "A large scale smart-grid field-test in Jeju," J. KIEE, vol. 61, no. 1, pp. 1-6, 2012.   과학기술학회마을   DOI
4 NIST, Nist framework and roadmap for smart grid interoperability standards, release 2.0, Jan. 2012.
5 S. C. Lee and Y. Kim. "A consumer level simulation model for demand response analysis on smart grid," 2012.
6 S. Yoshiyuki et al. "Development of residential energy end-use simulation model at city scale," in Proc. Eighth Int'l IBPSA Conf., pp. 1201-1208, Eindhoven, Netherlands, Aug. 2003.
7 A. Adrian, et al. "Drivers of variability in energy consumption," Submitted for review to European Conf. on Machine Learning, 2013.
8 C. Ding and X. He, "K-means clustering via principal component analysis", in Proc. Int'l Conf. Machine Learning(ICML), pp. 225-232, 2004.
9 D. Asma, A. Bondu, and A. Cornuejols, "Early classification of individual electricity consumptions," RealStream, 2013.
10 B. Marc, "MODL: A bayes optimal discretization method for continuous attributes," Machine learning, vol. 65, no. 1, pp. 131-165, 2006.   DOI
11 F. Hongliang, et al., "Heat pump detection from coarse grained smart meter data with positive and unlabeled learning," in Proc. 19th ACM SIGKDD Int'l conf. on Knowledge discovery and data mining, ACM, 2013.
12 B. R. Barmish and C. M. Lagoa, "The uniform distribution: A rigorous justification for its use in robustness analysis," Mathematics of Control, Signals and Systems, vol. 10, no. 3, pp. 203-222, 1997.   DOI
13 M. Thomas, Non-life insurance mathematics: an introduction with the Poisson process, Springer, 2009.
14 K. Ron, "A study of cross-validation and bootstrap for accuracy estimation and model selection," IJCAI, vol. 14, no. 2, 1995.
15 N. S. Altman, "An introduction to kernel and nearest-neighbor nonparametric regression," The American Statistician, vol. 46, no. 3, pp. 175-185, 1992.