Spacetime short to mediumterm wind speed foreca

Authors: Daniel Ambach Carsten Croonenbroeck

Publish Date: 2015/10/26

Volume: 25, Issue: 1, Pages: 5-20

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Abstract

Accurate wind power forecasts depend on reliable wind speed forecasts Numerical weather predictions utilize huge amounts of computing time but still have rather low spatial and temporal resolution However stochastic wind speed forecasts perform well in rather high temporal resolution settings They consume comparably little computing resources and return reliable forecasts if forecasting horizons are not too long In the recent literature spatial interdependence is increasingly taken into consideration In this paper we propose a new and quite flexible multivariate model that accounts for neighbouring weather stations’ information and as such exploits spatial data at a high resolution The model is applied to forecasting horizons of up to 1 day and is capable of handling a high resolution temporal structure We use a periodic vector autoregressive model with seasonal lags to account for the interaction of the explanatory variables Periodicity is considered and is modelled by cubic Bsplines Due to the model’s flexibility the number of explanatory variables becomes huge Therefore we utilize timesaving shrinkage methods like lasso and elastic net for estimation Particularly a relatively newly developed iteratively reweighted lasso and elastic net is applied that also incorporates heteroscedasticity We compare our model to several benchmarks The outofsample forecasting results show that the exploitation of spatial information increases the forecasting accuracy tremendously in comparison to models in use so far

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