# Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle

arXiv: https://arxiv.org/abs/2207.09784 · HTML: https://mlsanity.com/paper/2207.09784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources](https://mlsanity.com/paper/2209.07744/) (2022)
- [Power Management in Smart Residential Building with Deep Learning Model for Occupancy Detection by Usage Pattern of Electric Appliances](https://mlsanity.com/paper/2209.11520/) (2022)
- [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)

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time was obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was performed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.