Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey
Sky-image-based solar forecasting using deep learning has been recognized as
a promising approach in reducing the uncertainty in solar power generation.
However, one of the biggest challenges is the lack of massive and diversified
sky image samples. In this study, we present a comprehensive survey of
open-source ground-based sky image datasets for very short-term solar
forecasting (i.e., forecasting horizon less than 30 minutes), as well as
related research areas which can potentially help improve solar forecasting
methods, including cloud segmentation, cloud classification and cloud motion
prediction. We first identify 72 open-source sky image datasets that satisfy
the needs of machine/deep learning. Then a database of information about
various aspects of the identified datasets is constructed. To evaluate each
surveyed datasets, we further develop a multi-criteria ranking system based on
8 dimensions of the datasets which could have important impacts on usage of the
data. Finally, we provide insights on the usage of these datasets for different
applications. We hope this paper can provide an overview for researchers who
are looking for datasets for very short-term solar forecasting and related
areas.