Article citationsMore>>

Martí Perez, I., Nielsen, T.S., Madsen, H., Navarro, J., Roldán, A., Cabezón, D. and Barquero, C.G. (2001) Prediction Models in Complex Terrain. Proceedings of the European Wind Energy Conference, Copenhagen, 2001, 875-878.

has been cited by the following article:

• TITLE: Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting

  AUTHORS: Zhongxian Men, Eugene Yee, Fue-Sang Lien, Hua Ji, Yongqian Liu

  KEYWORDS: Artificial Neural Network, Bootstrap Resampling, Numerical Weather Prediction, Super-Ensemble, Wind Speed, Power Forecasting

  JOURNAL NAME: Energy and Power Engineering, Vol.6 No.11, October 9, 2014

  ABSTRACT: The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.