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8 May 2022 Non-intrusive load monitoring based on self-attention mechanism
Zikai Lin, Mingzhi Mao, Rong Pan
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Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 1224916 (2022) https://doi.org/10.1117/12.2636603
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China
Abstract
Demand-side energy management relying on load monitoring technology is an important guarantee for promoting smart grid construction. Non-intrusive load decomposition (NILD) technology has received a lot of attention worldwide due to its low cost, easy maintenance and high security. In this work, we propose a neural network combining an on/off state classification subnetwork with a power regression subnetwork. We incorporate a tailored self-attention module into the power regression subtask to improve the generalization of the model. The experimental results show the proposed deep neural network outperforms other SGN models.
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Zikai Lin, Mingzhi Mao, and Rong Pan "Non-intrusive load monitoring based on self-attention mechanism", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 1224916 (8 May 2022); https://doi.org/10.1117/12.2636603
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KEYWORDS
Neural networks
Data modeling
Algorithm development
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