# A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications

arXiv: https://arxiv.org/abs/2012.01059 · HTML: https://mlsanity.com/paper/2012.01059/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy](https://mlsanity.com/paper/2104.12419/) (2021)
- [SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting](https://mlsanity.com/paper/2111.14507/) (2021)
- [Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?](https://mlsanity.com/paper/2211.02108/) (2022)
- [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)
- [Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions](https://mlsanity.com/paper/2206.03207/) (2022)

## Abstract (arXiv)

Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this study introduces an image-based Sun tracking algorithm to localise the Sun in the image when it is visible and interpolate its daily trajectory from past observations. We validate the method on a set of sky images collected over a year at SIRTA's lab. Experimental results show that the proposed method provides robust smooth Sun trajectories with a mean absolute error below 1% of the image size.