Experiments and Comparison of Digital Twinning of Photovoltaic Panels by Machine Learning Models and a Cyber-Physical Model in Modelica
Federico Delussu, Davide Manzione, Rosa Meo, Gabriele Ottino, Mark Asare
Abstract
We present two approaches for digital twinning in the context of the forecast of power production by photovoltaic panels. We employ two digital models that are complementary: the first one is a cyber-physical system, simulating the physical properties of a photovoltaic panel, built by the open- source object-oriented modeling language Modelica. The second model is data-driven, obtained by the application of machine learning techniques on the data collected in an installation of the equipment. Both approaches make use of data from the weather forecast of each day. We compare the results of the two approaches. Finally, we integrate them in more sophisticated hybrid systems that get the benefits of both.