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The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs. • Forecasting methods of wind energy and speed at hourly and sub-hourly horizons are studied. • One linear regression of NWP outputs and one adding measurements are developed. • They are compared to classical ANN, ARMA and persistence using endogenous parameters. • Combining NWP with recent measurements outperforms all methods for all time horizons. [ABSTRACT FROM AUTHOR]