Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon B.-S. | - |
dc.contributor.author | Park R.-J. | - |
dc.contributor.author | Song K.-B. | - |
dc.date.available | 2020-05-26T08:05:02Z | - |
dc.date.created | 2020-05-26 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 1975-0102 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/36581 | - |
dc.description.abstract | Short-term load forecasting (STLF) is essential for power system operation. STLF based on deep neural network using LSTM layer is proposed. In order to apply the forecasting method to STLF, the input features are separated into historical and prediction data. Historical data are input to long short-term memory (LSTM) layer to model the relationships between past observed data. The outputs of the LSTM layer are incorporated with outputs of fully-connected layer in which prediction data, for instance weather information for forecasting day, are input. The optimal parameters of the proposed forecasting method are selected following several experiment. The proposed method is expected to contribute to stable power system operation by providing a precise load forecasting. © 2020, The Korean Institute of Electrical Engineers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Korean Institute of Electrical Engineers | - |
dc.relation.isPartOf | Journal of Electrical Engineering and Technology | - |
dc.title | Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s42835-020-00424-7 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Journal of Electrical Engineering and Technology, v.15, no.4, pp.1501 - 1509 | - |
dc.identifier.kciid | ART002603421 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000531784200002 | - |
dc.identifier.scopusid | 2-s2.0-85084510208 | - |
dc.citation.endPage | 1509 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1501 | - |
dc.citation.title | Journal of Electrical Engineering and Technology | - |
dc.citation.volume | 15 | - |
dc.contributor.affiliatedAuthor | Song K.-B. | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Deep neural networks | - |
dc.subject.keywordAuthor | Long short-term memory | - |
dc.subject.keywordAuthor | Short-term load forecasting | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Electric power plant loads | - |
dc.subject.keywordPlus | Long short-term memory | - |
dc.subject.keywordPlus | Multilayer neural networks | - |
dc.subject.keywordPlus | Network layers | - |
dc.subject.keywordPlus | Forecasting methods | - |
dc.subject.keywordPlus | Historical data | - |
dc.subject.keywordPlus | Input features | - |
dc.subject.keywordPlus | Load forecasting | - |
dc.subject.keywordPlus | Optimal parameter | - |
dc.subject.keywordPlus | Power system operations | - |
dc.subject.keywordPlus | Short term load forecasting | - |
dc.subject.keywordPlus | Weather information | - |
dc.subject.keywordPlus | Weather forecasting | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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