Electricity demand forecasting plays an important role in long-term planning for future generation facilities and transmission augmentation. It is a challenging problem because of the different uncertainties including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. But the most challenging part is that we often want to forecast the peak demand rather than the average demand. Consequently, it is necessary to adopt a probabilistic view of potential peak demand levels in order to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty.
I will describe my experiences in addressing these problems via the Monash Electricity Forecasting Model, a semiparametric additive model designed to take all the available information into account, and to provide forecast distributions up to a few decades ahead. The approach is being used by energy market operators and supply companies to forecast the probability distribution of electricity demand in various regions of Australia.
I will also briefly demonstrate an open-source R package to implement the model. The package allows for ensemble forecasting of demand based on simulations of future sample paths of temperatures and other predictor variables.