Nonlinear impact of temperature change on electricity demand: estimation and prediction using partial linear model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jiwon | - |
dc.contributor.author | Seo, Byeongseon | - |
dc.date.accessioned | 2021-09-01T04:58:27Z | - |
dc.date.available | 2021-09-01T04:58:27Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.issn | 1225-066X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/62667 | - |
dc.description.abstract | The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.title | Nonlinear impact of temperature change on electricity demand: estimation and prediction using partial linear model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Seo, Byeongseon | - |
dc.identifier.doi | 10.5351/KJAS.2019.32.5.703 | - |
dc.identifier.wosid | 000531008700005 | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF APPLIED STATISTICS, v.32, no.5, pp.703 - 720 | - |
dc.relation.isPartOf | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.title | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.volume | 32 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 703 | - |
dc.citation.endPage | 720 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002520838 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordAuthor | electricity | - |
dc.subject.keywordAuthor | nonlinear impact | - |
dc.subject.keywordAuthor | partial linear model | - |
dc.subject.keywordAuthor | temperature | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
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.