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

  • Sangkeum Lee (Environment ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Sarvar Hussain Nengroo (Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology) ;
  • Hojun Jin (Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology) ;
  • Yoonmee Doh (Environment ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Chungho Lee (Environment ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Taewook Heo (Environment ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Dongsoo Har (Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology)
  • Received : 2022.04.05
  • Accepted : 2022.07.06
  • Published : 2023.08.10
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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 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.

Keywords

Acknowledgement

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20191210301580 and 20192010107290).

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