Abstract
This work describes a hybrid soft-computing modeling technique that facilitates the modeling of newly installed solar cells, or solar cells with few historical measured data, over a range of expected operating conditions. The technique uses neuro-fuzzy models to predict solar cell short-circuit current and open-circuit voltage, followed by coordinate translation of a measured current-voltage response. The model can be extended beyond the bounds of measured data by incorporating a priori knowledge derived from theory and manufacturer's data. The solar cell model is developed and validated against measured data. The model requires fewer data than pure neural network models, and matches measured data more accurately than conventional solar cell models.