Daily Load Forecasting and Maximum Demand Estimation using ARIMA and GARCH

Climate change has been widely blamed to have caused an increase in unstable weather events such as storm, temperature extremes, severe precipitation and unseasonal weather. In the last decade, some notable weather events have occurred, e.g. high temperature in 1995 and 2003 created significantly increased electricity demand compared with the long term average. Such events pose a very challenging task for energy forecasters who must provide high quality predictions to utility decision makers. This paper outlines a methodology using autoregressive integrated moving average (ARIMA) model to predict daily load patterns. The purpose of this work serves as an initial step to investigate the impacts of climate change and weather extremes on electricity demand patterns and the electricity network. It is necessary for us not just to develop a robust methodology to predict the daily load accurately for planning purposes like most conventional models, but more importantly, to provide a reliable long range forecast. Our model takes account of four future climate change scenarios and socioeconomic scenarios to project 90 years (2011-2100) of future load demand patterns. The forecasted load will be used as an input to our transmission network model to study security and grid reinforcement of the power network as the result of climate change. As extreme weather occurs on short time scale, as such the model also incorporates the concept of generalised autoregressive conditional heteroscedasticity GARCH to model the residual in the student-t distribution and to estimate the maximum load demand that would be likely to occur within a finite time series with each estimated demand level corresponding to accepted levels of risk. The model has fitted to an in sample training data from 1970-1998 and the out of sample results were then verified with actual electricity data from 1999-2003. The mean absolute percentage error (MAPE) for each month generally lies within 1-3%