A sequence labeling solution for identifying multiple variable-length events in non-intrusive load monitoring

Yi Wang; Sijin Cheng; Xiao Zhang; Xinyi Li; Shenzheng Wang; Yang Gu

doi:10.1117/12.3031456

19 July 2024 A sequence labeling solution for identifying multiple variable-length events in non-intrusive load monitoring

Yi Wang, Sijin Cheng, Xiao Zhang, Xinyi Li, Shenzheng Wang, Yang Gu

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810V (2024) https://doi.org/10.1117/12.3031456
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the sharp change of global climate, NILM (non-intrusive load monitoring) has been a promising approach to reduce energy consumption and carbon emissions. This paper presents a new load identification method using sequence labeling to identify multiple variable-length events within the sliding window of aggregate power signal simultaneously. Furthermore, the proposed method integrates local features of events themselves with temporal features of the multiple events through CNN (convolutional neural network) and attention network. The experimental results on two public datasets REDD and UKDALE indicate that the proposed method achieves better performance than the existing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Wang, Sijin Cheng, Xiao Zhang, Xinyi Li, Shenzheng Wang, and Yang Gu "A sequence labeling solution for identifying multiple variable-length events in non-intrusive load monitoring", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810V (19 July 2024); https://doi.org/10.1117/12.3031456

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