Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
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.
|Authors||Azad, M. I., Rajabi, R. and Estebsari, A.|
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.
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Digital Object Identifier (DOI)||https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194819.|
|Accepted author manuscript|
File Access Level
|03 Aug 2023|
|Publication process dates|
|Deposited||25 Aug 2023|
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