Wind Energy Production Forecasting

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)


This paper presents a forecasting system of short-term wind energy production. The system is a feedforward neural network that uses genetic algorithm as a learning method of forecasting the wind energy production of the following day. The data concerns four different wind power plants. The neural network forecasts are compared with the predictions made by a neural network that uses Quasi-Newton learning algorithm and are evaluated based on the four common statistical measures. Both models perform satisfactorily as far as the prediction of the following day’s wind energy production is concerned.


Genetic Algorithm Root Mean Square Error Wind Power Wind Energy Mean Absolute Percentage Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Production Engineering and ManagementTechnical University of CreteChaniaGreece

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