Weather Effects in Energy Seasonal Adjustment: An Application to France Energy Consumption

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. JEL Classification: C32, E61, P28, Q47

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Publication Details

Journal
The Energy Journal
Published
2025-05-06
DOI
https://doi.org/10.1177/01956574251330845
Citations
1
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.73
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article

Weather Effects in Energy Seasonal Adjustment: An Application to France Energy Consumption

Ronan Le Saout, Arthur Thomas, Marie Bruguet
1 citations
The Energy Journal
Energy Load and Power Forecasting
0.73
article

Weather Effects in Energy Seasonal Adjustment: An Application to France Energy Consumption

Ronan Le Saout, Arthur Thomas, Marie Bruguet
article en
1 citations

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. JEL Classification: C32, E61, P28, Q47

The Energy JournalVol. 46(5)
Centre National de la Recherche Scientifique (FR), Université Paris Sciences et Lettres (FR), École Nationale de la Statistique et de l'Analyse de l'Information (FR), Centre de Recherche en Économie et Statistique (FR), Institut de Recherche pour le Développement (FR), Université de Rennes (FR), Université Paris Dauphine-PSL (FR), ENSAE Paris (FR)
Affordable and clean energy
Openalex Percentile: Top 29%
Energy Load and Power Forecasting
0.73
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