使用带动态阈值的卷积自编码器进行实时异常检测
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
AI总结：
针对智能计量能耗数据中的异常影响序列估计的问题，提出结合统计学与带动态阈值卷积自编码器的实时表计级检测方法，实现异常早期预警。
摘要：
现代消费级能源的大部分由实时智能计量系统生成。这些数据经常包含异常，从而妨碍对序列演化进行可靠估计。本工作提出一种混合建模方法，将统计学与带动态阈值的 Convolutional Autoencoder（卷积自编码器）相结合。该阈值基于 Mahalanobis 距离和移动平均值确定。该方法已使用从智能计量系统收集的真实能耗数据进行测试。该解决方案包括一个实时、表计级的异常检测系统，可连接到先进监测系统。它通过检测异常数据变动并提供早期预警作出重要贡献。早期检测及随后的故障排除可在经济上使组织和消费者受益，并防止灾难发生。
英文摘要：
The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
