DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jaeeun | ko |
dc.contributor.author | Kim, Jangkyum | ko |
dc.contributor.author | Lee, Sanghyun | ko |
dc.contributor.author | Choi, Jun Kyun | ko |
dc.date.accessioned | 2022-06-27T09:01:01Z | - |
dc.date.available | 2022-06-27T09:01:01Z | - |
dc.date.created | 2022-06-27 | - |
dc.date.created | 2022-06-27 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.citation | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v.134, pp.1 - 12 | - |
dc.identifier.issn | 0167-739X | - |
dc.identifier.uri | http://hdl.handle.net/10203/297102 | - |
dc.description.abstract | This paper proposes a novel one-day-ahead photovoltaic (PV) energy prediction scheme using on-site temperatures based on ensemble of convolutional neural networks (POST-enCNN). Even meteorological information from weather station can help predict PV energy, additional information is necessary to describe on-site weather condition. Therefore, the proposed scheme takes advantage of on-site weather data along with the meteorological information to predict PV energy generation accurately. The proposed prediction process is divided into two stages. In the first stage, the day-ahead on-site temperatures are predicted based on meteorological information and the past on-site temperatures data. Specifically, incremental trend of past on-site temperatures are analyzed and transformed into a numerical values. These values are applied to ensemble of CNNs to increase prediction accuracy of the day-ahead on-site temperatures. In the second stage, the predicted day-ahead on-site temperatures are combined with meteorological information. Finally, using the long short-term memory (LSTM) based neural network, the day-ahead PV energy generation is predicted using the combined weather information. Through the simulation results, we show that the proposed POST-enCNN achieve an 27% reduction of prediction error compared to a conventional method that predicts the day-ahead PV energy generation depending solely on meteorological information. | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.title | Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data | - |
dc.type | Article | - |
dc.identifier.wosid | 000806815000001 | - |
dc.identifier.scopusid | 2-s2.0-85127656349 | - |
dc.type.rims | ART | - |
dc.citation.volume | 134 | - |
dc.citation.beginningpage | 1 | - |
dc.citation.endingpage | 12 | - |
dc.citation.publicationname | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | - |
dc.identifier.doi | 10.1016/j.future.2022.03.028 | - |
dc.contributor.localauthor | Choi, Jun Kyun | - |
dc.contributor.nonIdAuthor | Lee, Sanghyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Photovoltaic energy | - |
dc.subject.keywordAuthor | Meteorological information | - |
dc.subject.keywordAuthor | IoT sensor | - |
dc.subject.keywordAuthor | On-site temperatures | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordPlus | POWER PREDICTION | - |
dc.subject.keywordPlus | MANAGEMENT | - |
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