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Nonlinear impact of temperature change on electricity demand: estimation and prediction using partial linear model

Authors
Park, JiwonSeo, Byeongseon
Issue Date
10월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
electricity; nonlinear impact; partial linear model; temperature
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.32, no.5, pp.703 - 720
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
32
Number
5
Start Page
703
End Page
720
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62667
DOI
10.5351/KJAS.2019.32.5.703
ISSN
1225-066X
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.
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College of Life Sciences and Biotechnology > Department of Food and Resource Economics > 1. Journal Articles

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Seo, Byeong seon
생명과학대학 (식품자원경제학과)
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