In this paper we consider the forecasting performance of a range of semi- and non-parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financial econometrics literature, where volatility clustering is common. However, electricity time series tend to exhibit higher volatility on a daily basis, but within a mean reverting framework, albeit with occasional large ’spikes’. In this paper we compare the existing forecasting performance of some popular parametric methods, notably GARCH ARMAX, with approaches that are new to this area of applied econometrics, in particular, Artificial Neural Networks (ANN); Linear Regression Trees, Local Regressions and Generalised Additive Models. Section 2 presents the characteristics of the data used which in this case are spot electricity prices from the Californian market 07/1999-12/2000. This period includes the ’crisis’ months of May- August 2000 where extreme volatility was observed. Section 3 presents the results and ranking of methods on the basis of forecasting performance. Section 4 concludes. JEL CLASSIFICATIONS: C14, C45, C53 1117