We announce the publication of the paper ”Experiments and Comparison of Digital Twinning of Photovoltaic Panels by Machine Learning Models and A Cyber-Physical Model in Modelica” in the scientific journal “IEEE Transactions on Industrial Informatics“.

The DOFWARE team, in collaboration with UNITO, has implemented, as part of the HOME project, a model capable of reproducing the behavior and the productivity of photovoltaic panels.

The approach is the result of the cooperation between physical and Machine Learning models.

By means of Modelica, a digital twin has been developed of the photovoltaic panel focused on the reproduction of its physical properties, while, with the application of Machine Learning techniques, the on-field data were exploited to generate an alternative digital twin. The best result was obtained by integrating the two approaches, achieving very accurate and reliable predictions.

This solution opens the way to new development approaches, where physical-numerical and machine-learning models can contribute to improvements quickly and effectively not only in the photovoltaic field.

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