With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something that is difficult to obtain with other methods of inference. The method is applied to several multi-equation models of half-hourly total system load in New South Wales, Australia. A detailed model based on three years of data reveals trend, seasonal, bivariate temperature/humidity and serial correlation components that all vary intra-day, justifying the assumption of a multi-equation approach. Short-term forecasts from simple models highlight the gains that can be made if accurate temperature predictions are exploited. Bayesian predictive means for half-hourly load compare favourably with point forecasts obtained using iterated generalized least squares estimation of the same models
