# Estimating Solar Irradiance Using Sky Imagers

arXiv: https://arxiv.org/abs/1910.04981 · HTML: https://mlsanity.com/paper/1910.04981/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [A semantic web approach to uplift decentralized household energy data](https://mlsanity.com/paper/2208.10265/) (2022)
- [SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models](https://mlsanity.com/paper/2502.10307/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)

## Abstract (arXiv)

Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations.