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

TL;DR

This paper presents a real-time anomaly detection method for smart meters using a deep learning autoencoder, improving power management efficiency with battery storage and electric vehicles.

Contribution

It introduces a novel deep learning-based anomaly detection approach combining graph convolutional and LSTM networks for smart meter data analysis.

Findings

Reduced electricity costs with anomaly detection

Decreased power supply from the grid

Enhanced power management efficiency

Abstract

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.