English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sky-image-based solar forecasting using deep learning has been recognized asa promising approach in reducing the uncertainty in solar power generation.However, one of the biggest challenges is the lack of massive and diversifiedsky image samples. In this study, we present a comprehensive survey ofopen-source ground-based sky image datasets for very short-term solarforecasting (i.e., forecasting horizon less than 30 minutes), as well asrelated research areas which can potentially help improve solar forecastingmethods, including cloud segmentation, cloud classification and cloud motionprediction. We first identify 72 open-source sky image datasets that satisfythe needs of machine/deep learning. Then a database of information aboutvarious aspects of the identified datasets is constructed. To evaluate eachsurveyed datasets, we further develop a multi-criteria ranking system based on8 dimensions of the datasets which could have important impacts on usage of thedata. Finally, we provide insights on the usage of these datasets for differentapplications. We hope this paper can provide an overview for researchers whoare looking for datasets for very short-term solar forecasting and relatedareas.

Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey