A clustering model for mining consumption patterns from imprecise electric load time series data

Authors:
Qiudan Li;Stephen Shaoyi Liao;Dandan Li
Affiliations:
Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing;Department of Information System, City University of Hong Kong, School of Economics and Management, South West Jiao Tong University, China;Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

This paper presents a novel clustering model for mining patterns from imprecise electric load time series. The model consists of three components. First, it contains a process that deals with representation and preprocessing of imprecise load time series. Second, it adopts a similarity metric that uses interval semantic separation (Interval SS)-based measurement. Third, it applies the similarity metric together with the k-means clustering method to construct clusters. The model gives a unified way to solve imprecise time series clustering problem and it is applied in a real world application, to find similar consumption patterns in the electricity industry. Experimental results have demonstrated the applicability and correctness of the proposed model.