University of Tasmania
Browse

File(s) under permanent embargo

Applying context in appliance load identification

conference contribution
posted on 2023-05-23, 18:39 authored by Shahriar, S, Rahman, A, Smith, D
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

Editors

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

Pagination

900-905

ISBN

978-1-4673-4714-3

Department/School

School of Information and Communication Technology

Publisher

Curran Associates Inc.

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

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the environmental sciences

Usage metrics

    University Of Tasmania

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC