Modeling and Simulation of PV Modules Based on ANFIS

This work presents an optimized method to simulate the modeling of photovoltaic (PV) modules with measured data of PV array. The current-voltage (I-V) characteristics are estimated via adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS method takes advantages of no need of internal parameters of PV model and can achieve a more accurate estimation of PV characteristics. By compared with Villalva's model, radial basis function neural networks (RBFNN) and support vector machine (SVM) method, the results predicted by the proposed ANFIS approach show the best estimation performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R-2).