Due to the large effort in terms of time and equipment necessary for reliability testing of photovoltaic (PV) modules, the PV community has always endeavored to obtain service life estimates, based on an extrapolation of measurement and characterization data from accelerated aging tests or modeling. The research project addresses the potential of innovative and complex statistical and machine learning data processing methods for digital analysis and improved modeling of the time and stress-dependent performance (degradation and reliability) of PV modules.
Coordinator: Austrian Institute of Technology GmbH (AIT), 7 partners in total
TU Wien team: Barbara Brune, Peter Filzmoser
Program / Call: e!MISSION
Funding: The Austrian Research Promotion Agency (FFG)
Start: January 1, 2021