Autoencoder

Smart grid

Deep learning

Electrical engineering

Physics

Automotive engineering

Energy (signal processing)

Smart meter

Mathematics

Wireless

Energy management

Automatic meter reading

Statistics

Engineering

Artificial intelligence

Power (physics)

Computer science

Battery (electricity)

Quantum mechanics

Real-time computing

Energy management system

Electric power system

Metering mode

Electricity meter

Anomaly detection

Telecommunications

Mechanical engineering

Electronics and Telecommunications Research Institute Journal

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 were 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 executed 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.

Anomaly detection of smart metering system for power management with battery storage system/electric vehicle