an article by Sarah E. Heaps, Malcolm Farrow and Kevin J. Wilson (Newcastle University, Newcastle upon Tyne, UK) published in Journal of the Royal Statistical Society: Statistics in Society Series A Volume 183 Issue 2 (February 2020)
Summary
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 pre-specified 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.
Labels:
calendar_effects, forecasting, gas_consumption, hidden_Markov_model, time_series,
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment