Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jinwoong | - |
dc.contributor.author | Moon, Jihoon | - |
dc.contributor.author | Jung, Seungmin | - |
dc.contributor.author | Hwang, Eenjun | - |
dc.date.accessioned | 2021-08-30T19:50:34Z | - |
dc.date.available | 2021-08-30T19:50:34Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/54498 | - |
dc.description.abstract | Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | ARTIFICIAL NEURAL-NETWORK | - |
dc.subject | PREDICTION | - |
dc.subject | ENSEMBLE | - |
dc.subject | MODELS | - |
dc.title | Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Eenjun | - |
dc.identifier.doi | 10.3390/rs12142271 | - |
dc.identifier.scopusid | 2-s2.0-85088654615 | - |
dc.identifier.wosid | 000556988500001 | - |
dc.identifier.bibliographicCitation | REMOTE SENSING, v.12, no.14 | - |
dc.relation.isPartOf | REMOTE SENSING | - |
dc.citation.title | REMOTE SENSING | - |
dc.citation.volume | 12 | - |
dc.citation.number | 14 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordPlus | ARTIFICIAL NEURAL-NETWORK | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | ENSEMBLE | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | smart island | - |
dc.subject.keywordAuthor | solar energy | - |
dc.subject.keywordAuthor | solar radiation forecasting | - |
dc.subject.keywordAuthor | light gradient boosting machine | - |
dc.subject.keywordAuthor | multistep-ahead prediction | - |
dc.subject.keywordAuthor | feature importance | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.