Data-driven PV modules modelling: Comparison between equivalent electric circuit and artificial intelligence based models

摘要

This paper aims to contribute to the answering of the research question: "Are the modern models of DC power output forecast of PV modules, which are based on artificial intelligence, able to beneficially replace the classical equivalent electric circuits models?" This is a pertinent question as it is nowadays commonly accepted that the next phase of RES integration in the power system will be based on PV power. The success of this integration will depend crucially on the accuracy of PV power forecasts. The classical PV performance models - simplified fast estimate (FE), 1 diode and 3 parameters (1D + 3P), 1 diode and 5 parameters (1D + 5P), and modern ANN based models are reviewed. Both the classical and modern models are validated against datasheet information and experimental data of PV modules. The conclusion of this study points to the fact that the modern model performs competitively. In the validation against experimental results, the errors associated with the modern model are lower than the ones achieved by the classical models. The computation time is nevertheless higher, but still acceptable. The drawback of the modern model is that it is unable to explain the physical nature of the phenomena associated with PV electricity generation.