Estimation of energy yield from wind farms using artificial neural networks / Mabel, M. Carolin in IEEE transactions on energy conversion, Vol. 24 N° 2 (Juin 2009)  

Public ISBD [article]  inIEEE transactions on energy conversion > Vol. 24 N° 2 (Juin 2009) . - pp. 459 - 464 Titre : Estimation of energy yield from wind farms using artificial neural networks Type de document : texte imprimé Auteurs : Mabel, M. Carolin, Auteur ; E. Fernández, Auteur Année de publication : 2009 Article en page(s) : pp. 459 - 464 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Mathematics computing  mean square error methods  neural nets  power engineering wind plants Résumé : This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6times10-3. The results demonstrate that this work is an efficient energy yield estimation tool for wind farms. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4738388&sortType%3Das [...] [article] Estimation of energy yield from wind farms using artificial neural networks [texte imprimé] / Mabel, M. Carolin, Auteur ; E. Fernández, Auteur . - 2009 . - pp. 459 - 464. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N° 2 (Juin 2009) . - pp. 459 - 464 Mots-clés : Mathematics computing  mean square error methods  neural nets  power engineering wind plants Résumé : This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6times10-3. The results demonstrate that this work is an efficient energy yield estimation tool for wind farms. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4738388&sortType%3Das [...]

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