This study evaluates two types of models for wind speed forecasting. The first is models with multiple causal factors, such as offsite readings of wind speed and meteorological variables. These can be estimated using either regressions or neural networks. The second is state transition and the closely related class of regime-switching transition models. These are attractive in that they can be used to predict outlying fluctuations or large ramp events. The regime-switching model uses a persistence forecast during periods of high wind speed, and regressions for low and intermediate speeds. These techniques are tested on three databases. Two main criteria are used to evaluate the outcomes, the number of high and low states than can be predicted correctly and the mean absolute percent error of the forecast. Neural nets are found to predict the state transitions somewhat better than logistic regressions, although the regressions do not do badly. Three methods all achieve about the same degree of forecast accuracy: multivariate regressions, state transition and regime-switching models. If the states could be predicted perfectly, the regime-switching model would improve forecast accuracy by an additional 2.5 to 3 percentage points. Analysis of the density functions of wind speed and the forecasting models finds that the regime-switching method more closely approximates the distribution of the actual data. Copyright © 2009 John Wiley & Sons, Ltd.