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The comparison between simulated and experimental signals of the SAPV gave good results. The ANN model predictions allow the users of SAPV systems to predict the different signals for each model and identify the output current of the system for different climatological conditions. These signals have been used for the training and testing the developed models, one for each component of the system and a global model of the complete system. Different measured signals of solar radiation sequences and electrical parameters (photovoltaic voltage and current) from a SAPV system installed at the south of Algeria have been recorded during a period of 5-years. In addition, a new procedure proposed for SAPV system sizing is presented in this work. A global model can identify the SAPV characteristics by knowing only the climatological conditions. The modeling of the complete SAPV system is achieved by combining the models of the different components of the system (PV-generator, battery and regulator).

SAPV systems are widely used in renewable energy source (RES) applications and it is important to be able to evaluate the performance of installed systems. The ANN combines the Levenberg–Marquardt algorithm (LM) with an infinite impulse response (IIR) filter in order to accelerate the convergence of the network.

This paper presents an adaptive artificial neural network (ANN) for modeling and simulation of a Stand-Alone photovoltaic (SAPV) system operating under variable climatic conditions.

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