# Attention Boosted Autoencoder for Building Energy Anomaly Detection

arXiv: https://arxiv.org/abs/2303.16097 · HTML: https://mlsanity.com/paper/2303.16097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold](https://mlsanity.com/paper/2404.04311/) (2024)
- [A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System](https://mlsanity.com/paper/2405.02574/) (2024)

## Abstract (arXiv)

Leveraging data collected from smart meters in buildings can aid in developing policies towards energy conservation. Significant energy savings could be realised if deviations in the building operating conditions are detected early, and appropriate measures are taken. Towards this end, machine learning techniques can be used to automate the discovery of these abnormal patterns in the collected data. Current methods in anomaly detection rely on an underlying model to capture the usual or acceptable operating behaviour. In this paper, we propose a novel attention mechanism to model the consumption behaviour of a building and demonstrate the effectiveness of the model in capturing the relations using sample case studies. A real-world dataset is modelled using the proposed architecture, and the results are presented. A visualisation approach towards understanding the relations captured by the model is also presented.