AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Energy Outlier Detection in Smart Environments
Chao Chen, Diane J. Cook

Last modified: 2011-08-24

Abstract


Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.

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