# A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold

arXiv: https://arxiv.org/abs/2404.04311 · HTML: https://mlsanity.com/paper/2404.04311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [High-resolution synthetic residential energy use profiles for the United States](https://mlsanity.com/paper/2210.08103/) (2022)
- [Indoor environment data time-series reconstruction using autoencoder neural networks](https://mlsanity.com/paper/2009.08155/) (2020)
- [Building power consumption datasets: Survey, taxonomy and future directions](https://mlsanity.com/paper/2009.08192/) (2020)
- [Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle](https://mlsanity.com/paper/2207.09784/) (2022)
- [Attention Boosted Autoencoder for Building Energy Anomaly Detection](https://mlsanity.com/paper/2303.16097/) (2023)

## Abstract (arXiv)

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.