LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences

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dc.contributor.authorKim, Nakyoungko
dc.contributor.authorKim, Minkyungko
dc.contributor.authorChoi, Jun Kyunko
dc.date.accessioned2018-12-20T02:03:11Z-
dc.date.available2018-12-20T02:03:11Z-
dc.date.created2018-11-29-
dc.date.created2018-11-29-
dc.date.created2018-11-29-
dc.date.issued2018-10-09-
dc.identifier.citation2018 IEEE 7th Global Conference on Consumer Electronics (GCCE 2018), pp.136 - 137-
dc.identifier.urihttp://hdl.handle.net/10203/247332-
dc.description.abstractFor 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.languageEnglish-
dc.publisherIEEE-
dc.titleLSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences-
dc.typeConference-
dc.identifier.wosid000459859500041-
dc.identifier.scopusid2-s2.0-85060288451-
dc.type.rimsCONF-
dc.citation.beginningpage136-
dc.citation.endingpage137-
dc.citation.publicationname2018 IEEE 7th Global Conference on Consumer Electronics (GCCE 2018)-
dc.identifier.conferencecountryJA-
dc.identifier.conferencelocationNara Royal Hotel-
dc.identifier.doi10.1109/GCCE.2018.8574484-
dc.contributor.localauthorChoi, Jun Kyun-
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EE-Conference Papers(학술회의논문)
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