The shelf life of the solar panels can vary based on its exposure to several weather conditions. In a recent study, a research team at Turkey, the Gebze Technical University and the Case Western Reserve University, used data science to verify and estimate the consequences of exposure to weather and other circumstances on the solar panel’s materials.
According to the research team, the use of data science to estimate the weakening of such materials can result in new approaches to extend its lifetime. Laura Bruckman, Professor at Case Western Reserve addressed the study with the Research Associate at the Gebze Technical University, Abdulkerim Gok. The team believes that this can make the price of solar energy easier and better to comprehend.
As the team believes, if the solar modules can sustain for 50 years and if science can support it, it is possible to make the solar energy more inexpensive by reducing the dollar/watt of electricity production. The study is published recently in the journal PLoS One and is titled as “Predictive models of poly (ethylene terephthalate) film degradation under multi-factor accelerated weathering exposures.”
The study has merged statistical data analytics and engineering epidemiology to design a predictive model for applications that are exposed to the environment. The solar energy system’s sustainability and reliability have remained a major challenge even though the solar energy market has witnessed a significant growth. The polymeric component’s stability is crucial for the service shelf-life of a solar panel during its open-air usage.
Polyethylene terephthalate (PET), from which the plastic bottles are made, is a widespread polymer. The polymer on the module’s back in the solar panels functions as an environmental barricade and safeguards individuals from receiving an electrical shock, in case they touch the module. The samples of PET were exposed to numerous accelerated weathering conditions. Mixed- and fixed-effects modeling was utilized to determine the deterioration reaction of diverse PET films.
In the research paper, the team has illustrated the PET degradation, which is essential for the prediction of solar panel’s service lifetime. What do you think about this approach of using data science?