Yangxin Fan

PhD Student in Computer & Data Science, SDLE Research Center
PhD Student, SDLE Research Center

Using Spatio-Temporal Graph Neural Networks to Estimate Fleet-Wide Photovoltaic Performance Degradation Patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry.

Hein Htet Aung

PhD Student in Materials Science & Engineering, SDLE Research Center
With a background in Mechanical Engineering and Materials Science, Hein loves applying data science to gain insights from systems at macro and micro levels. For his Master’s research, he worked on data-driven modeling for degradation of acrylic polymers and released an R software package. After his internship with Lawrence Livermore National Lab during his Ph.D. program, Hein is working to expand his research in DIW systems to model the error behavior of the build platform during the printing process and contribute towards the development of data-driven digital twins. Hein aspires to work in an interdisciplinary research area where he can explore novel ideas through data science.
Subscribe to