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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 6, issue 1
Adv. Sci. Res., 6, 55–58, 2011
https://doi.org/10.5194/asr-6-55-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Sci. Res., 6, 55–58, 2011
https://doi.org/10.5194/asr-6-55-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Mar 2011

21 Mar 2011

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

C. Gallego1, A. Costa1, and A. Cuerva2 C. Gallego et al.
  • 1Wind Energy Forecasting Group, CIEMAT. Avd. Complutense 22, 28040 Madrid, Spain
  • 2IDR/UPM, E.T.S.I. Aeronáuticos, Universidad Politécnica de Madrid, Pza. de Cardenal Cisneros 3, 28040 Madrid, Spain

Abstract. 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.

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