Sizing of stand-alone photovoltaic systems using neural network adaptive model

In this paper, we investigate using an adaptive radial basis function (RBF) network with infinite impulse response (IIR) filter in order to find a suitable model for sizing coefficients of the stand-alone photovoltaic (PV) systems, based on minimum of input data. These sizing coefficients allow to the users of stand-alone PV systems to determine the number of solar panel and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available. Obtained results by feed-forward MLP, RBF and an adaptive RBF-IIR model have been compared with real sizing coefficients. The adaptive RBFIIR has been trained by using 200 known sizing coefficients values corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and even handle a number of unusual cases. The unknown validation sizing coefficients set produced very set accurate estimation with the correlation coefficient between the actual and the RBF-IIR model estimated data of 97% was obtained. This result indicates that the proposed method can be successfully used for estimating of optimal sizing coefficients of PV systems for any locations in Algeria, but the methodology can be generalized using different locations in the world.