Abstract
This paper addresses the challenge of adjusting energy consumption data for weather variations by introducing a novel General Weather Indicator (GWI). The GWI combines multiple weather variables, including temperature, wind, sunlight, rain, and cloudiness, using a novel econometric approach that applies K-means for threshold identification and LASSO for variable selection. Through an empirical analysis of sectoral electricity and natural gas consumption in France, we demonstrate that the GWI outperforms the standard HDD approach by addressing three main concerns: the lack of statistical criteria for defining the base temperature, the reliance solely on temperature as the weather variable, and the assumption of a constant base temperature over time and space. Based on these results, we propose an analysis of the sectoral functional form and an estimation of weather elasticities for energy demand in France at both the monthly and daily levels.
Keywords
Get full access to this article
View all access options for this article.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
