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Improving Short-term Forecasting During Ramp Events by Means of Regime-switching Artificial Neural Networks : Volume 6, Issue 1 (21/03/2011)

By Gallego, C.

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Book Id: WPLBN0003990194
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: Improving Short-term Forecasting During Ramp Events by Means of Regime-switching Artificial Neural Networks : Volume 6, Issue 1 (21/03/2011)  
Author: Gallego, C.
Volume: Vol. 6, Issue 1
Language: English
Subject: Science, Advances, Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Cuerva, A., Costa, A., & Gallego, C. (2011). Improving Short-term Forecasting During Ramp Events by Means of Regime-switching Artificial Neural Networks : Volume 6, Issue 1 (21/03/2011). Retrieved from

Description: Wind Energy Forecasting Group, CIEMAT. Avd. Complutense 22, 28040 Madrid, Spain. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

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.; Kil, R. M., Park, S. H., and Kim, S.: Optimum window size for time series prediction, Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, 4, 1421–1424, 1997.; Lippmann, R. P.: An introduction to computing with neural nets, IEEE ASSP magazine, 4, 4–22, 1987.; Madsen, H.: Time series analysis, Chapman & Hall, CRC, 2007.; Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H. A., and Nielsen, T. S.: Standardizing the performance evaluation of short-term wind power prediction models, Wind Engineering, 29, 475–489, 2005.; Peña, D.: Estadística Modelos y métodos, Vol. 2, Alianza Editorial, 2nd Edn., 1987.; Potter, C. W., Grimit, E., and Nijssen, B.: Potential Benefits of a Dedicated Probabilistic Rapid Ramp Event Forecast Tool, in: 2009 IEEE/PES Power Systems Conference and Exposition, Seattle, Vol. 1–3, 409–413, 2009.


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