DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hyeon, Jonghwan | ko |
dc.contributor.author | Lee, HyeYoung | ko |
dc.contributor.author | Ko, Bowon | ko |
dc.contributor.author | Choi, Ho-Jin | ko |
dc.date.accessioned | 2020-10-08T01:55:19Z | - |
dc.date.available | 2020-10-08T01:55:19Z | - |
dc.date.created | 2020-09-21 | - |
dc.date.created | 2020-09-21 | - |
dc.date.created | 2020-09-21 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | JOURNAL OF ENGINEERING-JOE, v.2020, no.13, pp.639 - 642 | - |
dc.identifier.issn | 2051-3305 | - |
dc.identifier.uri | http://hdl.handle.net/10203/276487 | - |
dc.description.abstract | With the advent of various electronic products, the household electric energy consumption is continuously increasing, and therefore it becomes very important to predict the household electric energy consumption accurately. Energy prediction models also have been developed for decades with advanced machine learning technologies. Meanwhile, the deep learning models are still actively under study, and many newer models show the state-of-the-art performance. Therefore, it would be meaningful to conduct the same experiment with these new models. Here, the authors predict the household electric energy consumption using deep learning models, known to be suitable for dealing with time-series data. Specifically, vanilla long short-term memory (LSTM), sequence to sequence, and sequence to sequence with attention mechanism are used to predict the electric energy consumption in the household. As a result, the vanilla LSTM shows the best performance on the root-mean-square error metric. However, from a graphical point of view, it seems that the sequence-to-sequence model predicts the energy consumption patterns best and the vanilla LSTM does not follow the pattern well. Also, to achieve the best performance of each deep learning model, vanilla LSTM, sequence to sequence, and sequence to sequence with attention mechanism should observe past 72, 72, and 24 h, respectively. | - |
dc.language | English | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Deep learning-based household electric energy consumption forecasting | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 2020 | - |
dc.citation.issue | 13 | - |
dc.citation.beginningpage | 639 | - |
dc.citation.endingpage | 642 | - |
dc.citation.publicationname | JOURNAL OF ENGINEERING-JOE | - |
dc.identifier.doi | 10.1049/joe.2019.1219 | - |
dc.contributor.localauthor | Choi, Ho-Jin | - |
dc.contributor.nonIdAuthor | Lee, HyeYoung | - |
dc.contributor.nonIdAuthor | Ko, Bowon | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article; Proceedings Paper | - |
dc.subject.keywordAuthor | power consumption | - |
dc.subject.keywordAuthor | time series | - |
dc.subject.keywordAuthor | learning (artificial intelligence) | - |
dc.subject.keywordAuthor | mean square error methods | - |
dc.subject.keywordAuthor | load forecasting | - |
dc.subject.keywordAuthor | power engineering computing | - |
dc.subject.keywordAuthor | recurrent neural nets | - |
dc.subject.keywordAuthor | vanilla LSTM | - |
dc.subject.keywordAuthor | deep learning model | - |
dc.subject.keywordAuthor | energy prediction models | - |
dc.subject.keywordAuthor | advanced machine learning technologies | - |
dc.subject.keywordAuthor | sequence-to-sequence model | - |
dc.subject.keywordAuthor | energy consumption patterns | - |
dc.subject.keywordAuthor | household electric energy consumption forecasting | - |
dc.subject.keywordAuthor | time-series data | - |
dc.subject.keywordAuthor | vanilla long short-term memory | - |
dc.subject.keywordAuthor | root-mean-square error metric | - |
dc.subject.keywordAuthor | sequence to sequence with attention mechanism | - |
dc.subject.keywordAuthor | time 72 | - |
dc.subject.keywordAuthor | 0 hour | - |
dc.subject.keywordAuthor | time 24 | - |
dc.subject.keywordAuthor | 0 hour | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.