Abstract
This study presents a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting. Related research areas which could potentially help improve solar forecasting methods, including cloud segmentation, cloud classification, and cloud motion prediction are also considered. 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 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 potentially have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets in the open literature. We hope this paper provide an overview for researchers who are looking for datasets for training deep learning models for very short-term solar forecasting, cloud analysis, and atmospheric modeling.
Abstract (translated)
URL
https://arxiv.org/abs/2211.14709
