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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.