Anomaly detection and identification of power consumption data based on LOF and isolation forest

Lu Han; Yuan Gao; Xuejian Zheng

doi:10.1117/12.3025688

28 February 2024 Anomaly detection and identification of power consumption data based on LOF and isolation forest

Lu Han, Yuan Gao, Xuejian Zheng

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307134 (2024) https://doi.org/10.1117/12.3025688
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

With the rapid development of the energy Internet, the power industry has ushered in the era of power big data. Through dynamic monitoring and analysis of all kinds of data in power grid operation, potential risks can be found effectively and timely, and the safety of power grid operation can be improved. As an important part of power big data, user-side power consumption data provides strong support for managers to visually display users' abnormal power consumption behaviors. It provides a theoretical basis for better and faster solution to the behavior of electricity leakage due to equipment failure or personal behavior. Based on this background, this paper first uses k-means clustering algorithm to classify electricity consumption data according to user usage habits. This method can effectively avoid misjudgment and missing judgment of electricity data. Finally, the method of combining LOF and isolation forest is used to realize the anomaly detection of user side power consumption data. By comparing the single algorithm (LOF and isolation forest), it is proved that the anomaly monitoring model combined with the two algorithms can better identify the abnormal power consumption data at the user side.

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

Citation Download Citation

Lu Han, Yuan Gao, and Xuejian Zheng "Anomaly detection and identification of power consumption data based on LOF and isolation forest", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307134 (28 February 2024); https://doi.org/10.1117/12.3025688

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