Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

ABSTRACT: 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. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation m

SUBMITTER: Fan Y 

PROVIDER: S-EPMC10866472 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature