| Autor: |
Sangkeum Lee, Sarvar Hussain Nengroo, Hojun Jin, Yoonmee Doh, Chungho Lee, Taewook Heo, Dongsoo Har |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
ETRI Journal, Vol 45, Iss 4, Pp 650-665 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1225-6463 |
| DOI: |
10.4218/etrij.2022-0135 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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