自动化与信息工程2026，Vol.47Issue(3)：8-15,8.DOI:10.12475/aie.20260302
基于自编码网络的城市天然气日用量数据异常检测研究
Anomaly Detection of Daily Natural Gas Consumption Data in Cities Based on Autoencoder Networks
朱敏 1季艳 2刘紫薇3
作者信息
- 1. 云和县天然气有限公司,浙江 丽水 323600
- 2. 浙江浙能数字科技有限公司,浙江 杭州 310012
- 3. 山东理工职业学院,山东 济宁 272000
- 折叠
摘要
Abstract
Anomaly detection in urban daily natural gas consumption data is crucial for the safe operation of energy systems.To address the challenges of strong nonlinearity,scarce anomaly samples,and complex inter-variable dependencies,this paper proposes a prediction-enhanced convolutional autoencoder anomaly detection model,named PE-CAEAD.The model consists of a representation network,including a convolutional autoencoder and a deconvolutional decoder,as well as a prediction network,enabling unsupervised anomaly detection for urban daily natural gas consumption data.Experimental results on an urban daily natural gas consumption dataset show that the proposed model achieves a precision of 0.94,a recall of 0.96,an area under the ROC curve of 0.96,and an F1-score of 0.95,demonstrating its effectiveness and applicability in anomaly detection for urban daily natural gas consumption data.关键词
城市天然气/异常检测/时间序列/自编码器/交叉注意力机制/双向LSTMKey words
urban natural gas/anomaly detection/time series/autoencoder/cross attention mechanism/Bi-LSTM分类
能源科技引用本文复制引用
朱敏,季艳,刘紫薇..基于自编码网络的城市天然气日用量数据异常检测研究[J].自动化与信息工程,2026,47(3):8-15,8.
