Using Bayesian Optimization to Improve Solar Panel Performance by Developing Antireflective, Superomniphobic Glass (Research Track)
Sajad Haghanifar (University of Pittsburgh); Bolong Cheng (SigOpt); Mike Mccourt (SigOpt); Paul Leu (University of Pittsburgh)
Photovoltaic solar panel efficiency is dependent on photons transmitting through the glass sheet covering and into the crystalline silicon solar cells within. However, complications such as soiling and light reflection degrade performance. Our goal is to identify a fabrication process to produce glass which promotes photon transmission and is superomniphobic (repels fluids), for easier cleaning. In this paper, we propose adapting Bayesian optimization to efficiently search the space of possible glass fabrication strategies; in this search we balance three competing objectives (transmittance, haze and oil contact angle). We present the glass generated from this Bayesian optimization strategy and detail its properties relevant to photovoltaic solar power.