Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention

TL;DR

This paper introduces an unsupervised anomaly detection method using Variational Recurrent Autoencoders with attention, effectively handling noisy, unlabeled smart meter data to identify anomalies in energy systems.

Contribution

It presents a novel deep learning approach that pre-detects missing data and global anomalies, outperforming traditional methods in complex, noisy smart meter datasets.

Findings

Outperforms baseline VAE and other unsupervised methods in accuracy.

Effectively detects anomalies in real-world water supply temperature data.

Enhances robustness to noise and missing data in smart meter analysis.

Abstract

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.