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In the digitization of energy systems, sensors and smart meters areincreasingly being used to monitor production, operation and demand. Detectionof anomalies based on smart meter data is crucial to identify potential risksand unusual events at an early stage, which can serve as a reference for timelyinitiation of appropriate actions and improving management. However, smartmeter data from energy systems often lack labels and contain noise and variouspatterns without distinctively cyclical. Meanwhile, the vague definition ofanomalies in different energy scenarios and highly complex temporalcorrelations pose a great challenge for anomaly detection. Many traditionalunsupervised anomaly detection algorithms such as cluster-based ordistance-based models are not robust to noise and not fully exploit thetemporal dependency in a time series as well as other dependencies amongstmultiple variables (sensors). This paper proposes an unsupervised anomalydetection method based on a Variational Recurrent Autoencoder with attentionmechanism. with "dirty" data from smart meters, our method pre-detects missingvalues and global anomalies to shrink their contribution while training. Thispaper makes a quantitative comparison with the VAE-based baseline approach andfour other unsupervised learning methods, demonstrating its effectiveness andsuperiority. This paper further validates the proposed method by a real casestudy of detecting the anomalies of water supply temperature from an industrialheating plant.

Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention