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How are day-ahead prices informative for predicting the next day’s consumption of natural gas?

Author

Listed:
  • Arthur Thomas

    (IFPEN - IFP Energies nouvelles, LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes)

  • Olivier Massol

    (IFP School, IFPEN - IFP Energies nouvelles, LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, City University of London)

  • Benoît Sévi

    (GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - AGROCAMPUS OUEST - Institut National de l'Horticulture et du Paysage)

Abstract

The purpose of this paper is to investigate, for the first time, 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 and apply it to the case of France. Our results document the existence of a long-run relation between demand and spot prices and provide estimates of the marginal impacts that these price variables have on observed demand levels. We also provide evidence of the pivotal role of the spark ratio in the short run 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.
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Suggested Citation

  • Arthur Thomas & Olivier Massol & Benoît Sévi, 2019. "How are day-ahead prices informative for predicting the next day’s consumption of natural gas?," Post-Print hal-04319359, HAL.
    • Handle: RePEc:hal:journl:hal-04319359
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