To reduce operational costs and to ensure security of supply, gas distribution networks require accurate forecasts of the demand for gas. Among domestic and commercial customers, demand relates primarily to the weather and patterns of life and work. Public holidays have a pronounced effect which often spreads into neighbouring days. We call this spread the ‘proximity effect’. Traditionally, the days over which the proximity effect is felt are prespecified in fixed windows around each holiday, allowing no uncertainty in their identification. We are motivated by an application to modelling daily gas demand in two large British regions. We introduce a novel model which does not fix the days on which the proximity effect is felt. Our approach uses a four‐state, non‐homogeneous hidden Markov model, with cyclic dynamics, where the classification of days as public holidays is observed, but the assignment of days as ‘pre‐holiday’, ‘post‐holiday’ or ‘normal’ days is unknown. The number of days to the preceding and succeeding holidays guide transitions between states. We apply Bayesian inference and illustrate the benefit of our modelling approach. A version of the model is now being used by one of the UK's regional distribution networks.

中文翻译：

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确定公共假期对天然气日需求的影响

为了降低运营成本并确保供应安全，天然气分销网络需要准确预测天然气需求。在家庭和商业客户中，需求主要与天气以及生活和工作方式有关。公共假期有明显的影响，通常会扩散到临近的日子。我们称此价差为“邻近效应”。传统上，在每个假期前后的固定窗口中预先指定了会感觉到接近效果的日期，因此对其确定没有不确定性。我们受到了一个应用程序的启发，该应用程序为两个英国大地区的每日天然气需求建模。我们介绍了一种新的模型，该模型无法确定感觉到邻近效应的日子。我们的方法使用具有循环动力学的四态非均匀隐马尔可夫模型，在这里，可以将日期分类为公共假日，但是将日期分配为“节假日前”，“节假日后”或“正常”天数是未知的。前后假期的天数指导各州之间的转换。我们应用贝叶斯推断并说明我们的建模方法的好处。英国的一个地区分销网络正在使用该模型的一个版本。