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Applying context in appliance load identification


Shahriar, S and Rahman, A and Smith, D, Applying context in appliance load identification, Proceedings, ICNC 2013, 23-25 July 2013, Shenyang, China, pp. 900-905. ISBN 978-1-4673-4714-3 (2013) [Non Refereed Conference Paper]

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DOI: doi:10.1109/ICNC.2013.6818104


We investigate the impact of including context features with conventional machine learning models for energy disaggregation. Four types of context features that were broadly categorized as either temporal context or activity based context were individually examined across ten class of household appliance. We demonstrate that all machine learning models using context features in conjunction with traditional power features produced a significant improvement in classification accuracy of up to 38%. This could be attributed to the context features improving the class homogeneity of the feature space. It was also shown that classes were more linearly separable in the combined feature space of context and power features.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:appliance load identification, machine learning models, energy disaggregation, context features
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Technology
UTAS Author:Shahriar, S (Dr Sumon Shahriar)
ID Code:116727
Year Published:2013
Deposited By:Information and Communication Technology
Deposited On:2017-05-17
Last Modified:2017-05-17

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