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Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms

Authors :
Luis Prieto
Sancho Salcedo-Sanz
Pedro Antonio Gutiérrez
Emilio G. Ortiz-García
César Hervás-Martínez
Source :
Neural Computing and Applications. 21:993-1005
Publication Year :
2011
Publisher :
Springer Science and Business Media LLC, 2011.

Abstract

Combinations of physical and statistical wind speed forecasting models are frequently used in wind speed prediction problems arising in wind farms management. Artificial neural networks can be used in these models as a final step to obtain accurate wind speed predictions. The aim of this work is to determine the potential of evolutionary product unit neural networks (EPUNNs) for improving the accuracy and interpretation of these systems. Traditional neural network and EPUNN approaches have been used to develop different wind speed prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared with standard neural networks used to solve this regression problem. One of the main advantages of the application of these EPUNNs has been the possibility of obtaining some interpretation of the non-linear relation predicted by the model, as will be shown in real data of a wind farm in Spain.

Details

ISSN :
14333058 and 09410643
Volume :
21
Database :
OpenAIRE
Journal :
Neural Computing and Applications
Accession number :
edsair.doi...........fc7126362eeefb55b007b0f410c2f3b7
Full Text :
https://doi.org/10.1007/s00521-011-0582-x