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23 September 2015 A data science approach to understanding photovoltaic module degradation
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Abstract
The expected lifetime performance and degradation of photovoltaic (PV) modules is a major issue facing the levelized cost of electricity of PV as a competitive energy source. Studies that quantify the rates and mechanisms of performance degradation are needed not only for bankability and adoption of these promising technologies, but also for the diagnosis and improvement of their mechanistic degradation pathways. Towards this goal, a generalizable approach to degradation science studies utilizing data science principles has been developed and applied to c-Si PV modules. By combining domain knowledge and data derived insights, mechanistic degradation pathways are indicated that link environmental stressors to the degradation of PV module performance characteristics. Targeted studies guided by these results have yielded predictive equations describing rates of degradation, and further studies are underway to achieve this for additional mechanistic pathways of interest.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicholas R. Wheeler, Abdulkerim Gok, Timothy J. Peshek, Laura S. Bruckman, Nikhil Goel, Davis Zabiyaka, Cara L. Fagerholm, Thomas Dang, Christopher Alcantara, Mason L. Terry, and Roger H. French "A data science approach to understanding photovoltaic module degradation", Proc. SPIE 9563, Reliability of Photovoltaic Cells, Modules, Components, and Systems VIII, 95630L (23 September 2015); https://doi.org/10.1117/12.2209204
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