Automatic Appliance Identi cation Investigation of Automatic Appliance Identi cation scenarios and methodologies
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Issue Date
2020-06-01
Language
en
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Abstract
Automatic Appliance Identi cation refers to the task of identifying household devices given
measurements of its power consumption. Solving this problem is crucial for modern energy monitoring
applications but, so far, it has been shown to be non-trivial. In addition, there seems to
be confusion about the practical scenarios on which Appliance Identi cation can be deployed.
In this research project we attempt to untangle the de nition of Appliance Identi cation by
proposing a distinction of three di erent scenarios. Among these, we describe the Appliance
Load Identi cation scenario that, even though it had been implicitly mentioned in past works, it
was never explicitly de ned. With regards to experiments, we initially replicate results of noteable
past works using open datasets. Next, we propose a novel set of techniques for Appliance
Identi cation that use a mix of VI trajectory data, handpicked features and Multi-Modal Neural
Networks. Finally, we propose three classi ers for the newly-de ned Appliance Load Identi cation
scenario. Through tests we nd that most existing models are not robust to tests across
datasets. We also nd that combining VI trajectory representations with other features leads to
increased performance. Last, we provide the results of our Appliance Load Identi cation models
as baseline for future research.
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Faculteit der Sociale Wetenschappen