Modeling and Short-Term Forecasting of New South Wales Electricity System Load

Abstract

This article employs Bayesian semiparametric regression methodology to model intraday electricity load data and obtain short-term load forecasts. The role of such forecasts in the New South Wales wholesale electricity market is discussed and the method applied to New South Wales system load data. The semiparametric regression model used identifies daily periodic, weekly periodic, and temperature-sensitive components of load. Each component is decomposed as a linear combination of basis functions, with a nonzero probability mass that the corresponding coefficients are exactly zero. Three possible models for the errors are also considered, including independent, autoregressive, and first-differenced autoregressive models. A moving window of data is used to overcome the slow time-varying nature of the temperature and periodic effects. The entire model is estimated using a Bayesian Markov chain Monte Carlo approach, and forecasts are obtained using a Monte Carlo sample from the joint predictive distribution of future system load. It is demonstrated how accurate temperature forecasts can result in accurate intraday system load forecasts for even quite long forecast horizons.