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Forecasting day-ahead high-resolution natural-gas demand and supply in Germany

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  • Chen, Ying
  • Chua, Wee Song
  • Koch, Thorsten

Abstract

Forecasting natural gas demand and supply is essential for an efficient operation of the German gas distribution system and a basis for the operational decisions of the transmission system operators. The German gas market is moving towards more short-term planning, in particular, day-ahead contracts. This increases the difficulty that the operators in the dispatching centre are facing, as well as the necessity of accurate forecasts. This paper presents a novel predictive model that provides day-ahead forecasts of the high resolution gas flow by developing a Functional AutoRegressive model with eXogenous variables (FARX). The predictive model allows the dynamic patterns of hourly gas flows to be described in a wide range of historical profiles, while also taking the relevant determinants data into account. By taking into account a richer set of information, FARX provides stronger performance in real data analysis, with both accuracy and high computational efficiency. Compared to several alternative models in out-of-sample forecasts, the proposed model can improve forecast accuracy by at least 12% and up to 5-fold for one node, 3% to 2-fold and 2-fold to 4-fold for the other two nodes. The results show that lagged 1-day gas flow and nominations are important predictors, and with their presence in the forecast model, temperature becomes insignificant for short-term predictions.

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  • Chen, Ying & Chua, Wee Song & Koch, Thorsten, 2018. "Forecasting day-ahead high-resolution natural-gas demand and supply in Germany," Applied Energy, Elsevier, vol. 228(C), pages 1091-1110.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:1091-1110
    DOI: 10.1016/j.apenergy.2018.06.137
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