Non intrusive load identification method based on deep learning

Xuwei Xia; Shuang Zhang; Zhenhua Yan; Jia Liu; Jianhui Cai; Rui Ma

doi:10.1117/12.2685363

21 July 2023 Non intrusive load identification method based on deep learning

Xuwei Xia, Shuang Zhang, Zhenhua Yan, Jia Liu, Jianhui Cai, Rui Ma

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271716 (2023) https://doi.org/10.1117/12.2685363
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

The current non-invasive load identification methods have limited recognition range and are prone to increasing time consumption. Therefore, a non-invasive load identification method based on deep learning is proposed. Based on actual recognition requirements and standards, extract recognition features, adopt multi-level processing forms, break through the limitations of recognition range, and set them as multi-level adaptive recognition nodes. We constructed a deep learning non-invasive load identification model and utilized GAGOA multi-objective recognition optimization to achieve non-invasive processing. The test results show that the time consumption of the non-invasive load identification unit obtained by applying the proposed method is controlled below 0.5 seconds, which is shorter. This indicates that the practical application effect of this method is better and has certain practical application value.

Citation Download Citation

Xuwei Xia, Shuang Zhang, Zhenhua Yan, Jia Liu, Jianhui Cai, and Rui Ma "Non intrusive load identification method based on deep learning", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271716 (21 July 2023); https://doi.org/10.1117/12.2685363

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