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Solar System Uptime/Field Services Optimization Driven by Predictive Analytics

Original event:

Yang Hu, PhD, Senior Data Engineer at GE Power

Digital solutions will play a key role in the future viability of utility-scale solar. Through analyzing data drawn from the infrastructure, we’ll be able to accelerate the learning curve of applying new technology and, in doing so, reduce the perceived risk. Profitability in a solar farm is dependent on two factors, availability and O&M expenditure, since the resource is free. The aim is to maximize availability by better managing O&M, as just one faulty part can take hours or even days to identify, locate and replace – which can have a heavy impact on plant availability. In the past, managing risk inevitably contributed to pushing up maintenance costs. With the use of digital tools, risk is managed, maintenance expenditure reduced while maximizing energy output.

About our speaker:

Yang Hu is responsible for developing the predictive analytics for the digital solar product and assessing PV inverter’s performance using statistical models.  He received his Ph.D. degree in Materials Science and Engineering from Case Western Reserve University (CWRU), Cleveland, OH, USA, in January 2017. During the graduate study, he has been working in Solar Durability and Lifetime Extension (SDLE) research center. His Ph.D. research was focused on commercial PV systems power degradation under diverse climate conditions. He developed a machine learning algorithm quantitatively evaluate the degradation of more than one thousand PV systems across different climate zones and different PV modules technology and manufactures. He resides in San Ramon, CA.