# Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

arXiv: https://arxiv.org/abs/2206.03207 · HTML: https://mlsanity.com/paper/2206.03207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)
- [On Aliased Resizing and Surprising Subtleties in GAN Evaluation](https://mlsanity.com/paper/2104.11222/) (2021)
- [Deep Photovoltaic Nowcasting](https://mlsanity.com/paper/1810.06327/) (2018)
- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy](https://mlsanity.com/paper/2104.12419/) (2021)
- [Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting](https://mlsanity.com/paper/2005.11246/) (2020)
- [A deep learning approach to solar-irradiance forecasting in sky-videos](https://mlsanity.com/paper/1901.04881/) (2019)
- [Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data](https://mlsanity.com/paper/1911.04932/) (2019)
- [SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting](https://mlsanity.com/paper/2111.14507/) (2021)
- [A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications](https://mlsanity.com/paper/2012.01059/) (2020)
- [Time series modeling and large scale global solar radiation forecasting from geostationary satellites data](https://mlsanity.com/paper/1401.4644/) (2014)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting. Both deterministic and probabilistic predictions are evaluated in different weather conditions (clear-sky, cloudy, overcast) and with different input configurations (sky images, satellite observations and/or past irradiance values). Our results show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting. This study lays the groundwork for future novel approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.