  The photovoltaics (PV) technology landscape is evolving rapidly. To predict
the potential and scalability of emerging PV technologies, a global
understanding of these systems' performance is essential. Traditionally,
experimental and computational studies at large national research facilities
have focused on PV performance in specific regional climates. However,
synthesizing these regional studies to understand the worldwide performance
potential has proven difficult. Given the expense of obtaining experimental
data, the challenge of coordinating experiments at national labs across a
politically-divided world, and the data-privacy concerns of large commercial
operators, however, a fundamentally different, data-efficient approach is
desired. Here, we present a physics-guided machine learning (PGML) scheme to
demonstrate that: (a) The world can be divided into a few PV-specific climate
zones, called PVZones, illustrating that the relevant meteorological conditions
are shared across continents; (b) by exploiting the climatic similarities,
high-quality monthly energy yield data from as few as five locations can
accurately predict yearly energy yield potential with high spatial resolution
and a root mean square error of less than 8 kWhm$^{2}$, and (c) even with
noisy, heterogeneous public PV performance data, the global energy yield can be
predicted with less than 6% relative error compared to physics-based
simulations provided that the dataset is representative. This PGML scheme is
agnostic to PV technology and farm topology, making it adaptable to new PV
technologies or farm configurations. The results encourage physics-guided,
data-driven collaboration among national policymakers and research
organizations to build efficient decision support systems for accelerated PV
qualification and deployment across the world.
