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Detail
Journal Article
Anomaly detection of smart metering system for power management with battery storage system/electric vehicle
- Authors
- Sangkeum Lee, Sarvar Hussain Nengroo, Hojun Jin, Yoonmee Doh, Chungho Lee, Taewook Heo, Dongsoo Har
- Issue Date
- 2023-08
- Citation
- ETRI Journal, v.45, no.4, pp.650-665
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Battery storage system, Bidirectional Long Short-Term Memory, Convolutional networks, Electricity cost, Learning model, Long short-term memory network, Long-short term memory(LSTM), Missing values, Multi-objective optimization(MOP), Real-Time, Smart Metering System
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
