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Application of calendar-based temporal classification to forecast customer load patterns from load demand data

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6 Author(s)
Heon Gyu Lee ; Database/Bioinformatics Laboratory, Chungbuk National University, Korea ; Bum Ju Lee ; Jin-ho Shin ; Long Jin
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We present temporal classification technique in this paper how to predict power load patterns from load demand data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Therefore, we propose a temporal classification method for forecasting electrical customer load patterns. The main tasks include cluster analysis and temporal classification technique. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method uses the calendar-based temporal expression to discover load patterns in multiple time granularities such as short-, mid-, and long-term time interval. Lastly, in order to show the feasibility of temporal classification technique, the proposed methodology is applied on a set of high voltage customers of the Korea power system, and the results of our experiments are presented.

Published in:

Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on

Date of Conference:

8-11 July 2008