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A data distillation enhanced autoencoder for detecting anomalous gas consumption
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Zhou, Yujue, Jiang, Jie, Yang, Shuang-Hua, He, Ligang, Ding, Yulong, Liu, Kai, Zhu, Guozhong and Qing, Yali (2024) A data distillation enhanced autoencoder for detecting anomalous gas consumption. IEEE Internet of Things Journal, 11 (2). pp. 3473-3483. doi:10.1109/jiot.2023.3296538 ISSN 2327-4662.
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Official URL: https://doi.org/10.1109/jiot.2023.3296538
Abstract
The number of natural gas users has been growing rapidly in China due to the promotion of clean energy and the economic benefits of natural gas, especially in businesses and industries. Though the infrastructures for gas supplies have been highly improved, gas providers are still suffering from various problems such as malfunctioning gas meters, gas leakage, gas theft etc. With the development of the Internet of Things, smart gas meters have been widely adopted by gas providers to collect real-time gas consumption data for billing purposes which can also serve as a basis for anomaly detection. One challenge of using such data for anomaly detection is that it is difficult to obtain sufficient labelled data for model training. To address this challenge, we propose DAE, a data distillation enhanced autoencoder for detecting anomalous gas consumption, which consists of three modules. The first module preprocesses the raw meter readings and carries out a rule-based anomaly detection. The second module extracts the normal gas usage patterns via an integration of correlation and clustering based consistency evaluation methods. The extracted normal usage patterns are then used in the third module to train an autoencoder for anomaly detection. DAE intends to provide a method to detect anomalous gas consumption induced by various causes such that manual inspection can be largely reduced. Moreover, DAE does not require user-specific information and can be applied to different types of gas users. Based on a real-world gas consumption dataset, we carry out a set of experiments and show that DAE outperforms the existing and improves the F1 score by an average of 7.4% for restaurant users and 5.7% for canteen users.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Anomaly detection (Computer security), Energy consumption -- Forecasting , Time-series analysis, Gas industry -- China -- Forecasting | |||||||||||||||||||||
Journal or Publication Title: | IEEE Internet of Things Journal | |||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||
ISSN: | 2327-4662 | |||||||||||||||||||||
Official Date: | 15 January 2024 | |||||||||||||||||||||
Dates: |
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Volume: | 11 | |||||||||||||||||||||
Number: | 2 | |||||||||||||||||||||
Page Range: | pp. 3473-3483 | |||||||||||||||||||||
DOI: | 10.1109/jiot.2023.3296538 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Re-use Statement: | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Date of first compliant deposit: | 21 September 2023 | |||||||||||||||||||||
Date of first compliant Open Access: | 25 September 2023 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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