Sectoral electricity consumption modeling with D-vine quantile regression: The US electricity market case

Evkaya O., Yilmaz B., YÜKSEL HALİLOĞLU E.

Energy Sources, Part B: Economics, Planning and Policy, cilt.18, sa.1, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/15567249.2022.2160523
  • Dergi Adı: Energy Sources, Part B: Economics, Planning and Policy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Greenfile, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Climate change, cooling degree days (CDD), D-vine quantile regression, electricity demand forecasting, heating degree days (HDD), linear quantile regression
  • Gazi Üniversitesi Adresli: Evet

Özet

Efficient electricity demand planning is crucial for energy market actors. However, it is difficult as a consequence of climate change. We aim at investigating how climate variables (heating and cooling degree days) may affect electricity demand. By examining electricity consumption in various US sectors, we explore this relationship using parametric and non-parametric D-vine quantile regression models that exploits the dependence between covariates and allows sequential covariate selection. The results are compared against the classical linear quantile regression. We find a positive effect of the climatic variables on electricity consumption that is as heating and cooling degree days increase electricity demand rises in all sectors, and cooling need has a greater impact than heating need. Evidence suggests that residential and commercial electricity consumptions are affected the most, while industrial and transport sector consumptions are less sensitive. The D-vine quantile regression performs better than the linear quantile regression for almost all sectors.