In this paper we suggest the use of robust GM-SETAR (Self Exciting Threshold AutoRegressive) processes to model and forecast electricity prices observed on deregulated markets. The robustness of the model is achieved by extending to time series the generalized M-type (GM) estimator first introduced for independent multivariate data. As it has been shown in a very recent paper [1], the polynomial weighting function over-performs the classical ordinary least squares method when extreme observations are present. The main advantage of estimating robust SETAR models is the possibility to capture two very well-known stylized facts of electricity prices: nonlinearity produced by changes of regimes and the presence of sudden spikes due to inelasticity of demand. The forecasting performance of the model applied to the Italian electricity market (IPEX) is improved by the introduction of predicted demand as an exogenous regressor. The availability of this regressor is a particular feature of the Italian market. By means of prediction performance indexes and tests, it will be shown that this regressor plays a crucial role and that robust methods improve the overall forecasting performance of the model.