WaDGAN-AD를 이용한 전력 소비 패턴의 비지도 학습 기반 이상 탐지
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김혜연 | - |
dc.contributor.author | 김형석 | - |
dc.contributor.author | 강필성 | - |
dc.date.accessioned | 2022-11-05T03:42:03Z | - |
dc.date.available | 2022-11-05T03:42:03Z | - |
dc.date.created | 2022-11-04 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1225-0988 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/144763 | - |
dc.description.abstract | Anomaly detection in time series is essential because it can detect outlying patterns such as a breakdown in machines and fraudulent customers. Among many anomaly detection domains, detecting abnormal patterns in energy consumption is used to detect technical breakdown in factories, general buildings, or energy theft in households. To overcome the limitations of previous studies, this paper suggests WaDGAN-AD, which combines generative adversarial network (GAN) and Long Short-Term Memory (LSTM) and applies two structural improvements. WaDGAN-AD has stacked discriminator LSTM layers to more precisely learn feature representations of time series data. Also, it has different numbers of hidden units in each hidden layer of LSTM to consider multiple cycles appearing in a single time-series data. Experimental results based on synthetic datasets and real datasets show that WaDGAN-AD can better detect abnormal energy consumption than benchmark methods. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 대한산업공학회 | - |
dc.title | WaDGAN-AD를 이용한 전력 소비 패턴의 비지도 학습 기반 이상 탐지 | - |
dc.title.alternative | Unsupervised Anomaly Detection with Wider and Deeper LSTM-GAN for Energy Consumption Pattern | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 강필성 | - |
dc.identifier.bibliographicCitation | 대한산업공학회지, v.47, no.5, pp.421 - 432 | - |
dc.relation.isPartOf | 대한산업공학회지 | - |
dc.citation.title | 대한산업공학회지 | - |
dc.citation.volume | 47 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 421 | - |
dc.citation.endPage | 432 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002765537 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Time-series Anomaly Detection | - |
dc.subject.keywordAuthor | LSTM | - |
dc.subject.keywordAuthor | GAN | - |
dc.subject.keywordAuthor | Energy Consumption | - |
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