Machine Learning Assisted Selective Emitter Design for Solar Thermophotovoltaic System

study showcases the application of machine learning techniques for designing and optimizing a selective emitter within a solar thermophotovoltaic (STPV) system. Specifically, we utilize random forest (RF) and genetic algorithm (GA) approaches to refine the emitter's structure, aiming to achieve high emissivity in the solar spectrum while minimizing emissivity in the thermal spectrum. Additionally, we analyze the impact of emitter design on the current-voltage and power-voltage characteristics of the STPV system, illustrating the direct influence of emitter configuration on system performance. This research underscores the effectiveness of machine learning in enhancing the performance of solar thermophotovoltaic energy conversion technologies.