How are Day-ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas? Evidence from France
研究了仅用日前市场价格（天然气价格和火花比率）预测次日天然气消费的可行性，发现该简单模型比运营商预测更准确，对能源交易者和政策制定者有参考价值。
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 day-ahead 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.