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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.

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