A Design and Comparative Analysis of a Home Energy Disaggregation System Based on a Multi-Target Learning Framework
Auteur(s): |
Bundit Buddhahai
Suratsavadee Koonlaboon Korkua Pattana Rakkwamsuk Stephen Makonin |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 24 mars 2023, n. 4, v. 13 |
Page(s): | 911 |
DOI: | 10.3390/buildings13040911 |
Abstrait: |
Insightful information on energy use encourages home residents to conduct home energy conservation. This paper proposes an experimental design for an energy disaggregation system based on the low-computational-cost approaches of multi-target classification and multi-target regression, which are under the multi-target learning framework. The experiments are set up to determine the optimal learning algorithm and model parameters. In addition, the designated system can provide inference of the appliance power state and the estimated power consumption from both approaches. The kernel density estimation technique is utilized to formulate the appliance power state as a finite-state machine for the multi-target classification approach. Multi-target regression can directly provide the estimation of appliance power demand from the aggregate data, and this work unifies the system’s design together with multi-target classification. The predictive performances obtained through the F-score (micro-averaged) and power estimation accuracy index for the power state inference and the estimated power demand, respectively, are shown to outperform a deep-learning-based denoising autoencoder network under the same data settings from both approaches. The results lead to a recommendation to apply the approach in home energy monitoring, which is mainly based on the characteristics of appliance power and the information that the residents wish to perceive. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10728230 - Publié(e) le:
30.05.2023 - Modifié(e) le:
01.06.2023