ORCID Profile
0000-0002-4615-6487
Current Organisation
Macquarie University
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Publisher: Association for Computing Machinery (ACM)
Date: 21-06-2021
DOI: 10.1145/3448415
Abstract: Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of in idual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The s les fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 05-2020
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 08-2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2021
No related grants have been discovered for Queen Nguyen.