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Accurate wind power forecasts depend on reliable wind speed forecasts.Numerical Weather Predictions (NWPs) utilize huge amounts of computing time,but still have rather low spatial and temporal resolution. However, stochasticwind speed forecasts perform well in rather high temporal resolution settings.They consume comparably little computing resources and return reliableforecasts, if forecasting horizons are not too long. In the recent literature,spatial interdependence is increasingly taken into consideration. In this paperwe propose a new and quite flexible multivariate model that accounts forneighbouring weather stations' information and as such, exploits spatial dataat a high resolution. The model is applied to forecasting horizons of up to oneday and is capable of handling a high resolution temporal structure. We use aperiodic vector autoregressive model with seasonal lags to account for theinteraction of the explanatory variables. Periodicity is considered and ismodelled by cubic B-splines. Due to the model's flexibility, the number ofexplanatory variables becomes huge. Therefore, we utilize time-saving shrinkagemethods like lasso and elastic net for estimation. Particularly, a relativelynewly developed iteratively re-weighted lasso and elastic net is applied thatalso incorporates heteroscedasticity. We compare our model to severalbenchmarks. The out-of-sample forecasting results show that the exploitation ofspatial information increases the forecasting accuracy tremendously, incomparison to models in use so far.

Using the lasso method for space-time short-term wind speed predictions