The validation of a new explicit empirical model for general photovoltaic devices, providing current and voltage at Maximum Power Point (MPP) and current-voltage/power-voltage characteristics under arbitrary conditions of temperature and irradiance, is presented. One of the main advantages of this model is the fact that the equivalent circuit parameters - such as series and shunt resistance, dark-saturation currents, etc. - are not needed, as the sole model input data are the device parameters commonly reported in the datasheets. Moreover, the model is explicit so that its application is very affordable from the computational standpoint. The model is applied to three different types of photovoltaic modules representing some of the most widely diffused technologies in the current market: multi-crystalline silicon, CdTe and CIGS. The calculated voltages, currents and powers at maximum power point are compared with the ones measured for three modules working at the photovoltaic test facility of the University of Trieste. A statistical analysis is presented in order to prove the effectiveness and reliability of the model at maximum power point. Finally, the results of the new explicit model are compared with those obtained by a polynomial regression, Artificial Neural Network (ANN), the well-known single-diode model and an additional, different explicit model. This work shows that the electric performance of a photovoltaic module can be predicted with a high degree of accuracy on the sole basis of parameters that are always found in the photovoltaic device's datasheet
