An Application of LSTNet in Deep Learning-based Non-intrusive Load Monitoring Framework

Im-Gyu Kim; Hyun-Cheol Kim; Sang-Yong Shin

doi:10.22696/jkiaebs.20210068

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2021 Vol.15, Issue 6 Preview Page Next Page

Research Article

An Application of LSTNet in Deep Learning-based Non-intrusive Load Monitoring Framework 딥러닝 기반 비침입 부하 모니터링 프레임워크에서의 LSTNet 응용

30 December 2021. pp. 806-817

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Abstract

In this study, a process of measuring and preprocessing power data for 4 types of home appliances and a deep training-based NILM technique were proposed. Active power of 4 types of home appliances (refrigerator, induction, TV, washer) was measured for about 3 weeks. The power data of each home appliance was measured to be aggregated in a smart meter. In order to disaggregate energy using LSTNet, four types of home appliances and smart meter power data were constructed as a training dataset. In the training process, we performed a parametric study to extract the optimal hyperparameter with the highest validation accuracy metric among the major parameters of LSTNet to verify the feasibility of NILM application of LSTNet.

Keywords

Energy disaggregation

Non-Intrusive Load Monitoring (NILM)

LSTNet

Smart Grid

키워드

에너지 분해

비침입 부하 모니터링(NILM)

LSTNet

스마트 그리드

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Information

Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
Publisher(Ko) :한국건축친환경설비학회
Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
Journal Title(Ko) :한국건축친환경설비학회논문집
Volume : 15
No :6
Pages :806-817
Received Date : 2021-12-06
Revised Date : 2021-12-15
Accepted Date : 2021-12-15
DOI :https://doi.org/10.22696/jkiaebs.20210068

Journal of Korean Institute of Architectural Sustainable Environment and Building Systems ISSN:1976-6483(Print) 2586-0666(Online) 한국건축친환경설비학회논문집

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