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- W2242415544 abstract "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, 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 one 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 modelled by cubic B-splines. Due to the model’s flexibility, the amount of explanatory variables becomes huge. Therefore, we utilize a time-saving lasso method for estimation. Particularly, a relatively newly developed iteratively reweighted lasso is applied that also incorporates heteroscedasticity. We compare our model to several benchmarks. One of them is the somewhat more advanced ARFIMA(p,d,q)APARCH(P,Q) process. The results show that the exploitation of spatial information and the re-weighted lasso method increases the forecasting accuracy tremendously, in comparison to models in use so far." @default.
- W2242415544 created "2016-06-24" @default.
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- W2242415544 date "2015-01-26" @default.
- W2242415544 modified "2023-09-27" @default.
- W2242415544 title "Using the lasso method for space-time short-term wind speed predictions" @default.
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