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Novel Technique of Sizing the Stand-Alone Photovoltaic Systems Using the Radial Basis Function Neural Networks: Application in Isolated Sites
Abstract
The objective of this work is to investigate the Radial Basis
Function Neural Networks (RBFN) to identifying and
modeling the optimal sizing couples of stand-alone
photovoltaic (PV) system using a minimum of input data,
These optimal couples allow to the users of stand-alone PV
systems to determine the number of solar panel modules
and storage batteries necessary to satisfy a given
consumption. The advantage of this model is to estimate of
the sizing PV system in any site in Algeria particularly in
isolated sites, where the global solar radiation data is not
always available. A RBFN has been trained by using 200
known sizing couples data corresponding to 200 locations.
In this way, it was trained to accept and even handle a
number of unusual case, known sizing couples were
subsequently used to investigate the accuracy of prediction
the training of the RBFN model was performed with
adequate accuracy. Subsequently, the unknown validation
sizing couples set produced very set accurate predictions
with the correlation coefficient between the actual and the
RBFN model identified data of 98% was obtained. This
result indicates that the proposed method can be
successfully used for estimating of optimal sizing couples
of PV systems for any locations in Algeria, but the
methodology can be generalized using different locations in
the world.
Citation
Mellit, A.; Benghanme, M.; Arab, A. H.; Guessoum, A. (2004). Novel Technique of Sizing the Stand-Alone Photovoltaic Systems Using the Radial Basis Function Neural Networks: Application in Isolated Sites. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from https : / /hdl .handle .net /1969 .1 /4634.