Analysis and Prediction of Power Consumption Pattern Using Spatiotemporal Data Mining Techniques in GIS-AMR System


KIPS Transactions on Software and Data Engineering, Vol. 16, No. 3, pp. 307-316, Mar. 2009
10.3745/KIPSTD.2009.16.3.307, Full Text:

Abstract

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer’s power consumption. Second, the patterns of customer’s power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.


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Cite this article
[IEEE Style]
J. H. Park, H. G. Lee, J. H. Shin and K. H. Ryu, "Analysis and Prediction of Power Consumption Pattern Using Spatiotemporal Data Mining Techniques in GIS-AMR System," KIPS Journal D (2001 ~ 2012) , vol. 16, no. 3, pp. 307-316, 2009. DOI: 10.3745/KIPSTD.2009.16.3.307.

[ACM Style]
Jin Hyoung Park, Heon Gyu Lee, Jin Ho Shin, and Keun Ho Ryu. 2009. Analysis and Prediction of Power Consumption Pattern Using Spatiotemporal Data Mining Techniques in GIS-AMR System. KIPS Journal D (2001 ~ 2012) , 16, 3, (2009), 307-316. DOI: 10.3745/KIPSTD.2009.16.3.307.