# A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System

arXiv: https://arxiv.org/abs/2405.02574 · HTML: https://mlsanity.com/paper/2405.02574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 9 of 9)

- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [On the Nature and Types of Anomalies: A Review of Deviations in Data](https://mlsanity.com/paper/2007.15634/) (2020)
- [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)
- [A Typology of Data Anomalies](https://mlsanity.com/paper/2107.01615/) (2021)
- [Attention Boosted Autoencoder for Building Energy Anomaly Detection](https://mlsanity.com/paper/2303.16097/) (2023)

## Abstract (arXiv)

Building operations consume 30% of total power consumption and contribute 26% of global power-related emissions. Therefore, monitoring, and early detection of anomalies at the meter level are essential for residential and commercial buildings. This work investigates both supervised and unsupervised approaches and introduces a dynamic anomaly detection system. The system introduces a supervised Light Gradient Boosting machine and an unsupervised autoencoder with a dynamic threshold. This system is designed to provide real-time detection of anomalies at the meter level. The proposed dynamical system comes with a dynamic threshold based on the Mahalanobis distance and moving averages. This approach allows the system to adapt to changes in the data distribution over time. The effectiveness of the proposed system is evaluated using real-life power consumption data collected from smart metering systems. This empirical testing ensures that the system's performance is validated under real-world conditions. By detecting unusual data movements and providing early warnings, the proposed system contributes significantly to visual analytics and decision science. Early detection of anomalies enables timely troubleshooting, preventing financial losses and potential disasters such as fire incidents.