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 EPE  Vol.6 No.11 , October 2014
Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting
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.
Cite this paper: Men, Z. , Yee, E. , Lien, F. , Ji, H. and Liu, Y. (2014) Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting. Energy and Power Engineering, 6, 340-348. doi: 10.4236/epe.2014.611029.
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