Rеvuе des Energies Renouvelables
Volume 14, Numéro 2, Pages 329-342
2011-06-30
Using Multilayered Neural Networks For Determining Global Solar Radiation Upon Tilted Surface In Fianarantsoa Madagascar
Authors : Razafiarison I.a.j. . Andriazafimahazo L.f.g. . Ramamonjisoa B.o.a. . Zeghmati B. .
Abstract
The knowledge of the local solar radiation characteristics is indispensable in the survey of any system exploiting solar energy in any location. The author is particularly interested by the global solar radiance upon tilted surface per time unit to help operators using solar energy in their work. The target is, among others, helping solar drying operators that need while tuning drying system the knowledge of the global solar radiation that could be received on inclined solar captors in implantation site. The aim of this paper is to use neural network method to search for solar radiation upon a tilted surface. Multilayered neural networks (MNN) trained by gradient back-propagation are used to determine numeric values of monthly means and hourly variations of the global solar radiation upon a titled surface per time unit. The numerical calculations are made with the geographical and meteorological parameters (latitude, longitude and clearness index) of the location of Fianarantsoa, Madagascar.
Keywords
Neural network, Back-propagation, Simulation, Global solar radiation.
Les articles similaires
Saad Saoud L.
.
Rahmoune F.
.
Tourtchine V.
.
Baddari K.
.
pages 173-185.
Lantri F.
.
Bachari N.e.i.
.
Belbachir H.
.
pages 65-76.
Benkaciali S.
.
Gairaa K.
.
pages 219-228.
Soulouknga M.h.
.
Falama R.z.
.
Doka S.y.
.
Kofane T.c.
.
pages 209-217.
