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Applying context in appliance load identification
conference contribution
posted on 2023-05-23, 18:39 authored by Shahriar, S, Rahman, A, Smith, DWe 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 2013Editors
H Wang, SY Yuen, L Wang, L Shao, X WangPagination
900-905ISBN
978-1-4673-4714-3Department/School
School of Information and Communication TechnologyPublisher
Curran Associates Inc.Place of publication
Red Hook, New York, United StatesEvent title
2013 Ninth International Conference on Natural ComputationEvent Venue
Shenyang, ChinaDate of Event (Start Date)
2013-07-23Date of Event (End Date)
2013-07-25Repository Status
- Restricted