Predicting renewable energy generation using LSTM for risk assessment of local level power networks
DC Field | Value | Language |
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dc.contributor.author | Ryu H.-S. | - |
dc.contributor.author | Lee Y.-R. | - |
dc.contributor.author | Kim M.-K. | - |
dc.date.available | 2020-07-13T04:20:55Z | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 1975-8359 | - |
dc.identifier.issn | 2287-4364 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/41770 | - |
dc.description.abstract | Low uncertainty is essential when operating the power system in a stable state. Recently, the uncertainty in the power systems has increased due to the growth of renewable energy. This paper proposes a method to reduce the uncertainty of the power systems including renewable energy by using Long Short-term Memory (LSTM) algorithm. Through repeated simulation, the optimal LSTM model of each renewable unit is created. probabilistic scenario is created by monte-carlo simulation and k-means clustering algorithm, and then we assess risk for each scenario through a test system created with reference to the actual system. To validate the superiority of the proposed method, the risk assessment are conducted through local level test system. The results demonstrate that the optimal LSTM model reduces the risk index compared to other predicted models. © 2020 Korean Institute of Electrical Engineers. All rights reserved. | - |
dc.format.extent | 9 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | Korean Institute of Electrical Engineers | - |
dc.title | Predicting renewable energy generation using LSTM for risk assessment of local level power networks | - |
dc.title.alternative | Local level 전력네트워크의 리스크 평가를 위한 LSTM을 활용한 재생에너지 발전량 예측 모델 개발 | - |
dc.type | Article | - |
dc.identifier.doi | 10.5370/KIEE.2020.69.6.783 | - |
dc.identifier.bibliographicCitation | Transactions of the Korean Institute of Electrical Engineers, v.69, no.6, pp 783 - 791 | - |
dc.identifier.kciid | ART002592435 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85086412166 | - |
dc.citation.endPage | 791 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 783 | - |
dc.citation.title | Transactions of the Korean Institute of Electrical Engineers | - |
dc.citation.volume | 69 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Local Level Network | - |
dc.subject.keywordAuthor | Long Short-Term Memory | - |
dc.subject.keywordAuthor | Renewable Power Forecasting | - |
dc.subject.keywordAuthor | Severity Risk Index | - |
dc.subject.keywordAuthor | Uncertainty Modeling | - |
dc.subject.keywordPlus | Electric power transmission networks | - |
dc.subject.keywordPlus | Intelligent systems | - |
dc.subject.keywordPlus | K-means clustering | - |
dc.subject.keywordPlus | Monte Carlo methods | - |
dc.subject.keywordPlus | Renewable energy resources | - |
dc.subject.keywordPlus | Risk assessment | - |
dc.subject.keywordPlus | Risk perception | - |
dc.subject.keywordPlus | A-stable | - |
dc.subject.keywordPlus | Actual system | - |
dc.subject.keywordPlus | Power networks | - |
dc.subject.keywordPlus | Predicted models | - |
dc.subject.keywordPlus | Renewable energies | - |
dc.subject.keywordPlus | Renewable energy generation | - |
dc.subject.keywordPlus | Risk indices | - |
dc.subject.keywordPlus | Test systems | - |
dc.subject.keywordPlus | Long short-term memory | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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