Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

Conference paper

Azad, M. I., Rajabi, R. and Estebsari, A. (2023). Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring. IEEE 23rd International Conference on Environment and Electrical Engineering (EEEIC). Madrid 06 - 09 Jun 2023 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194819.
AuthorsAzad, M. I., Rajabi, R. and Estebsari, A.
TypeConference paper
Abstract

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.

Year2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194819.
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Print03 Aug 2023
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AcceptedMay 2023
Deposited25 Aug 2023
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https://openresearch.lsbu.ac.uk/item/94qqz

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