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Because of the highly variable forecast errors of wind power generation, a thorough uncertainty model describing both the predictive distribution and dependent structure of forecast errors is essential.
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Towards a correction of systematic prediction errors an analysis of the temporal structure of the forecast error is performed.
The dependence structure of the forecast errors is reflected by the Gaussian copula, which is estimated using the copula-based DCCMR model.
3) The dependence structure of the forecast errors is well represented by the Gaussian copula, and it is not necessary to make any assumption on the distributions of the errors.
The dependence structure of the forecast errors is well represented by the Gaussian copula, and it is not necessary to make any assumption on the distributions of the errors.
Therefore, a K-dimensional Gaussian copula is selected to model the dependence structure of the spot forecast errors.
We describe the underlying structure of the new forecast and policy model used at the Reserve Bank of New Zealand and evaluate its ability to explain New Zealand data.
The recursive structure of the BIG forecasting algorithm is shown in Fig. 2.
Under this background, researchers are inspired to develop feasible models for the correlation structure of wind power forecasting [10, 11, 12, 13, 14, 15, 16].
This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble.
The structure of the two proposed forecasting models are derived from basic principles, which include a combination of persistence and daily maximum air temperature as input variables.
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