Articles | Volume 6, issue 1
https://doi.org/10.5194/asr-6-55-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks
Cited articles
Costa, A.: Mathematical/Statistical and Physical/Meteorological Models for Short-term Prediction of Wind Farms Output, Ph.D. thesis, Escuela Técnica Superior de Ingenieros Industriales (Universidad Politécnica de Madrid), 2005.
Cutler, N., Kay, M., Jacka, K., and Nielsen, T. S.: Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT, Wind Energy, 10, 453–470, 2007.
Giebel, G.: The state of the art in short-term prediction of wind power – A literature overview, Tech. rep., ANEMOS EU project, 2003.
Greaves, B., Collins, J., Parkes, J., and Tindal, A.: Temporal Forecast Uncertainty for Ramp Events, Wind Engineering, 33, 309–320, 2009.
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–366, 1989.