# Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder

arXiv: https://arxiv.org/abs/2112.03275 · HTML: https://mlsanity.com/paper/2112.03275/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Intrusion Prediction with System-call Sequence-to-Sequence Model](https://mlsanity.com/paper/1808.01717/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset](https://mlsanity.com/paper/2303.09703/) (2023)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)
- [Evasion of IoT Malware Detection via Dummy Code Injection](https://mlsanity.com/paper/2602.08170/) (2026)

## Abstract (arXiv)

Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical task to enhance reliability, stability, and efficiency of the power system. This paper presents an anomaly detection process to find outliers observed in the smart metering system. In the proposed approach, bi-directional long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous data point. It calculates the reconstruction error through autoencoder with the non-anomalous data, and the outliers to be classified as anomalies are separated from the non-anomalous data by predefined threshold. Anomaly detection method based on the BiLSTM autoencoder is tested with the metering data corresponding to 4 types of energy sources electricity/water/heating/hot water collected from 985 households.