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Applying context in appliance load identification

Version 2 2025-01-15, 01:13
Version 1 2023-05-23, 18:39
conference contribution
posted on 2025-01-15, 01:13 authored by S Shahriar, A Rahman, D Smith
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.

History

Publication title

Proceedings, ICNC 2013

Volume

12

Editors

H Wang, SY Yuen, L Wang, L Shao, X Wang

Pagination

900-905

ISBN

978-1-4673-4714-3

Department/School

Information and Communication Technology

Publisher

Curran Associates Inc.

Publication status

  • Published

Place of publication

Red Hook, New York, United States

Event title

2013 Ninth International Conference on Natural Computation

Event Venue

Shenyang, China

Date of Event (Start Date)

2013-07-23

Date of Event (End Date)

2013-07-25

Socio-economic Objectives

280111 Expanding knowledge in the environmental sciences

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    University Of Tasmania

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