Identifying the effect of public holidays on daily demand for gas
10th November 2017, 2:00 pm – 3:00 pm
Main Maths Building, SM3
Gas distribution networks need to ensure the supply and demand for gas are balanced at all times. In practice, this is supported by a number of forecasting exercises which, if performed accurately, can substantially lower operational costs, for example through more informed preparation for severe winters. Amongst domestic and commercial customers, the demand for gas is strongly related to the weather and patterns of life and work. In regard to the latter, public holidays have a pronounced effect, which often extends into neighbouring days. In the literature, the days over which this protracted effect is felt are typically pre-specified as fixed windows around each public holiday. This approach fails to allow for any uncertainty surrounding the existence, duration and location of the protracted holiday effects. We introduce a novel model for daily gas demand which does not fix the days on which the proximity effect is felt. Our approach is based on a four-state, non-homogeneous hidden Markov model with cyclic dynamics. In this model the classification of days as public holidays is observed, but the assignment of days as ``pre-holiday'', ``post-holiday'' or ``normal'' is unknown. Explanatory variables recording the number of days to the preceding and succeeding public holidays guide the evolution of the hidden states and allow smooth transitions between normal and holiday periods. To allow for temporal autocorrelation, we model the logarithm of gas demand at multiple locations, conditional on the states, using a first-order vector autoregression (VAR(1)). We take a Bayesian approach to inference and consider the problem of specifying a prior distribution for the autoregressive coefficient matrix of a VAR(1) process which is constrained to lie in the stationary region. We summarise the results of an application to data from Northern Gas Networks (NGN), the regional network serving the North of England, a preliminary version of which is already being used by NGN in its annual medium-term forecasting exercise.