基于自编码网络的城市天然气日用量数据异常检测研究OA
Anomaly Detection of Daily Natural Gas Consumption Data in Cities Based on Autoencoder Networks
城市天然气日用量数据的异常检测对能源系统安全运行至关重要.针对其非线性强、异常样本稀缺和变量间依赖复杂等问题,本文提出一种预测增强型卷积自编码异常检测(PE-CAEAD)模型.该模型由表征网络(卷积自编码器、反卷积解码器)和预测网络组成,可在无监督条件下实现城市天然气日用量数据的异常检测.在某城市天然气日用量数据集上的实验结果表明,该模型的精确率、召回率、ROC 曲线下的面积、F1 分数分别为 0.94、0.96、0.96、0.95,验证了其在城市天然气日用量数据异常检测中的有效性与适用性.
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.
朱敏;季艳;刘紫薇
云和县天然气有限公司,浙江 丽水 323600浙江浙能数字科技有限公司,浙江 杭州 310012山东理工职业学院,山东 济宁 272000
能源科技
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LSTM
urban natural gasanomaly detectiontime seriesautoencodercross attention mechanismBi-LSTM
《自动化与信息工程》 2026 (3)
8-15,8
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