Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series

Ning Guo; Pengju Wang

doi:10.1117/12.3006094

10 October 2023 Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series

Ning Guo, Pengju Wang

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127990K (2023) https://doi.org/10.1117/12.3006094
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

At present, the conventional detection methods for abnormal data of power purchase materials mainly use correlation vector machines to extract and reduce the dimensions of data features. Due to less dimensions of data feature extraction, the detection effect is poor. In this regard, a method based on BIRCH clustering algorithm and time series for anomaly detection of power purchase material data is proposed. The data is preprocessed by removing outliers and supplementing missing values, and a time series autoregression model is constructed according to data dimensions to extract the flow characteristics of material data. The determination of abnormal data is realized by using local density threshold. In the experiment, the detection performance of the designed detection method is tested. The final results can prove that the proposed method has a low false detection rate and an ideal detection effect when it is used for abnormal detection of power purchase material data.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ning Guo and Pengju Wang "Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127990K (10 October 2023); https://doi.org/10.1117/12.3006094

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