  Photovoltaic power forecasting (PVPF) is a critical area in time series
forecasting (TSF), enabling the efficient utilization of solar energy. With
advancements in machine learning and deep learning, various models have been
applied to PVPF tasks. However, constructing an optimal predictive architecture
for specific PVPF tasks remains challenging, as it requires cross-domain
knowledge and significant labor costs. To address this challenge, we introduce
AutoPV, a novel framework for the automated search and construction of PVPF
models based on neural architecture search (NAS) technology. We develop a brand
new NAS search space that incorporates various data processing techniques from
state-of-the-art (SOTA) TSF models and typical PVPF deep learning models. The
effectiveness of AutoPV is evaluated on diverse PVPF tasks using a dataset from
the Daqing Photovoltaic Station in China. Experimental results demonstrate that
AutoPV can complete the predictive architecture construction process in a
relatively short time, and the newly constructed architecture is superior to
SOTA predefined models. This work bridges the gap in applying NAS to TSF
problems, assisting non-experts and industries in automatically designing
effective PVPF models.
