Dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning

Jianxin Sui; Houhui Xiong; Miao Mao; Haixiao Wang; Chunlan Guo

doi:10.1117/12.3038122

7 August 2024 Dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning

Jianxin Sui, Houhui Xiong, Miao Mao, Haixiao Wang, Chunlan Guo

Author Affiliations +

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132291F (2024) https://doi.org/10.1117/12.3038122
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

In response to the low accuracy and high false alarm rate of anomaly identification in energy big data, this paper proposes a dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning. This method deeply mines the optimal sub segments in energy big data, uses deep learning techniques to automatically learn advanced feature representations in the data, and combines dynamic weighting strategies to accurately identify abnormal behavior. By selecting the optimal sub segment, this method can focus on the data features most relevant to abnormal behavior, improving recognition accuracy. The experimental results show that the proposed method can capture subtle changes in abnormal energy big data, further enhancing the accuracy of recognition and reducing the false alarm rate. In summary, the method proposed in this article has high accuracy and low false alarm rate in the field of energy big data anomaly recognition, providing strong technical support for decision-making, optimization, and management in the energy industry.

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

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Jianxin Sui, Houhui Xiong, Miao Mao, Haixiao Wang, and Chunlan Guo "Dynamic weighted identification method for energy big data anomalies based on optimal sub segment deep learning", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132291F (7 August 2024); https://doi.org/10.1117/12.3038122

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KEYWORDS

Deep learning

Data modeling

Education and training

Data communications

Statistical modeling

Industrial applications

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