DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Nakyoung | ko |
dc.contributor.author | Kim, Minkyung | ko |
dc.contributor.author | Choi, Jun Kyun | ko |
dc.date.accessioned | 2018-12-20T02:03:11Z | - |
dc.date.available | 2018-12-20T02:03:11Z | - |
dc.date.created | 2018-11-29 | - |
dc.date.created | 2018-11-29 | - |
dc.date.created | 2018-11-29 | - |
dc.date.issued | 2018-10-09 | - |
dc.identifier.citation | 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE 2018), pp.136 - 137 | - |
dc.identifier.uri | http://hdl.handle.net/10203/247332 | - |
dc.description.abstract | For energy-related services and researches, not only the energy load data in the past but also the future are essential. In this paper, a short-term electricity consumption prediction method is proposed. The method utilizes Long-Short-Term-Memory (LSTM) network which takes a sequence of past consumption profiles to perform a month-ahead electricity consumption prediction as a sequence. For performance analysis, an experiment with a real dataset is done, and the experimental result validates that the proposed method performs well with the prediction accuracy of about 82.5%. The test accuracy can be improved with a longer period of training time and deliberate hyperparameter setting. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences | - |
dc.type | Conference | - |
dc.identifier.wosid | 000459859500041 | - |
dc.identifier.scopusid | 2-s2.0-85060288451 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 136 | - |
dc.citation.endingpage | 137 | - |
dc.citation.publicationname | 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE 2018) | - |
dc.identifier.conferencecountry | JA | - |
dc.identifier.conferencelocation | Nara Royal Hotel | - |
dc.identifier.doi | 10.1109/GCCE.2018.8574484 | - |
dc.contributor.localauthor | Choi, Jun Kyun | - |
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