How are Day-ahead Prices Informative for Predicting the Next Day's Consumption of Natural Gas? Evidence from France
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1 septembre 2022
- ISIDORE Id: 10670/1.237f26...
- hal: hal-03521140
- doi: 10.5547/01956574.43.5.atho
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info:eu-repo/semantics/altIdentifier/doi/10.5547/01956574.43.5.atho
info:eu-repo/semantics/OpenAccess
Mots-clés
Natural Gas Markets Day-Ahead Prices Demand Price Elasticity Load Forecasting L - Industrial Organization/L.L9 - Industry Studies: Transportation and Utilities/L.L9.L95 - Gas Utilities • Pipelines • Water Utilities Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q41 - Demand and Supply • Prices Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q4 - Energy/Q.Q4.Q47 - Energy Forecasting C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation MethodsSujets proches
Prediction Forecasts FuturologyCiter ce document
Arthur Thomas et al., « How are Day-ahead Prices Informative for Predicting the Next Day's Consumption of Natural Gas? Evidence from France », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.5547/01956574.43.5.atho
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Résumé
The purpose of this paper is to investigate whether the next day’s consumption of natural gas can be accurately forecast using a simple model that solely incorporates the information contained in dayahead market data. Hence, unlike standard models that use a number of meteorological variables, we only consider two predictors: the price of natural gas and the spark ratio measuring the relative price of electricity to gas. We develop a suitable modeling approach that captures the essential features of daily gas consumption and in particular the nonlinearities resulting from power dispatching. We use the case of France as an application as this is, as far as is known, the very first attempt to model and predict the country’s daily gas demand. Our results document the existence of a long-run relation between demand and spot prices and provide estimates of the own- and cross-price elasticities. We also provide evidence of the pivotal role of the spark ratio which is found to have an asymmetric and highly nonlinear impact on demand variations. Lastly, we show that our simple model is sufficient to generate predictions that are considerably more accurate than the forecasts published by infrastructure operators.
