Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach

In this paper, we propose a methodology to forecast residential and commercial natural gas consumption which combines natural gas demand estimation with a stochastic temperature model. We model demand and temperature processes separately and derive the distribution of natural gas consumption conditional on temperature. Natural gas consumption and local temperature processes are estimated using daily data on natural gas consumption and temperature for Istanbul, Turkey. First, using the derived conditional distribution of the natural gas consumption we obtain confidence intervals of point forecasts. Second, we forecast natural gas consumption by using temperature and consumption paths generated by Monte Carlo simulations. We evaluate the forecast performance of different model specifications by comparing the realized consumption values with the model forecasts by backtesting method. We utilize our analytical solution to establish a relationship between the traded temperature-based weather derivatives, i.e. HDD/CDD futures, and expected natural gas consumption. This relationship allows for partial hedging of the demand risk faced by the natural gas suppliers via traded weather derivatives.