Articles | Volume 8, issue 1
https://doi.org/10.5194/asr-8-143-2012
© Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.
27 Jul 2012
The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation
Cited articles
Alessandrini, S., Decimi, G., and Ronzio, D.: Studio della relazione energia producibile/prodotta per un campo eolico su terreno complesso con modelli meteorologici dinamici, Rapporto di Ricerca RSE no. 10000262, 2010.
Alessandrini, S., Pinson, P., Hagedorn, R., Decimi, G., and Sperati, S.: An application of ensemble/multi model approach for wind power production forecasting, Adv. Sci. Res., 6, 35–37, https://doi.org/10.5194/asr-6-35-2011, 2011.
Cali, Ue., Lange, B., Dobschinski, J., Kurt, M., Moehrlen, C., and Ernst, B.: Artificial neural network based wind power forecasting using a multi-model approach, 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, Madrid, 2008.
ECMWF internal report: Horizontal resolution increase (www.ecmwf.int), 2010.
Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., and Draxl, C.: The State of the Art in Short-Term Prediction of Wind Power, Anemos.plus deliverable report, 2011.
