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A Symbolic Time-series Data Mining Framework for Analyzing Load Profiles of Electricity Consumption

Volume / Date

 VOL. 15 (NO. 2) / DEC.2017

Page

 pp. 21-44

DOI

 10.6182/jlis.2017.15(2).021

Language

 English

I-Chin Wu*, Tzu-Li Chen, Guan-Qun Hong, Yen-Ming Chen, Tzu-Chi Liu

* Corresponding Author

I-Chin Wu* Graduate Institute of Library and Information Studies, National Taiwan Normal University, Taipei, Taiwan
icwu@ntnu.edu.tw
Tzu-Li Chen Department of Information Management, Fu-Jen Catholic University, New Taipei, Taiwan
Guan-Qun Hong Department of Information Management, Fu-Jen Catholic University, New Taipei, Taiwan
Yen-Ming Chen Industrial Technology Research Institute, Hsinchu, Taiwan
Tzu-Chi Liu Industrial Technology Research Institute, Hsinchu, Taiwan

Abstract

Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In this research, we analyzed electricity consumption from the perspective of load profiling, which charts variations in electrical load during a specified period in order to track energy consumption of an annealing furnace in a co-operating steel forging plant. We made a preliminary proposal to use a symbolic time-series data mining framework for electricity consumption analysis. First, we adopted a piecewise aggregate approximation (PAA) approach to perform dimension reduction. Then, we refined the distance measure of the symbolic aggregate approximation (SAX) algorithm. SAX is a symbolic representation of time-series for dimensionality reduction and indexing with a lower-bounding distance measure. Our experimental results showed that the dimension reduction method known as PAA can better detect the state of the annealing furnace compared to the fixed feature point (FFP) method. In addition, the refined lower-bounding distance measure proved to be better than the traditional measure for calculating the similarity between energy load profiles. The results can help the plant conduct further normal and abnormal electricity pattern detection.

Keywords

Electricity Load Profiling; Piecewise Aggregate Approximation; Symbolic Aggregate Approximation; Time-series Data Mining