posted on 2023-05-23, 18:39authored byShahriar, 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