# A review on physical and data-driven based nowcasting methods using sky images

arXiv: https://arxiv.org/abs/2105.02959 · HTML: https://mlsanity.com/paper/2105.02959/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Photovoltaic Nowcasting](https://mlsanity.com/paper/1810.06327/) (2018)
- [Smart Microgrids: Overview and Outlook](https://mlsanity.com/paper/1304.3944/) (2013)

## Built on by (most cited first, 2 of 2)

- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [A Meta-Analysis of Solar Forecasting Based on Skill Score](https://mlsanity.com/paper/2208.10536/) (2022)

## Abstract (arXiv)

Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted and reliable power supply to serve the energy demand. There are several approaches to perform solar irradiance forecasting, for instance satellite-based methods, sky image-based methods, machine learning-based methods, and numerical weather prediction-based methods. In this paper, we present a review on short-term intra-hour solar prediction techniques known as nowcasting methods using sky images. Along with this, we also report and discuss which sky image features are significant for the nowcasting methods.