# A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning

arXiv: https://arxiv.org/abs/2112.13974 · HTML: https://mlsanity.com/paper/2112.13974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 28 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [The color out of space: learning self-supervised representations for Earth Observation imagery](https://mlsanity.com/paper/2006.12119/) (2020)
- [Deep Photovoltaic Nowcasting](https://mlsanity.com/paper/1810.06327/) (2018)
- [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)

## Built on by (most cited first, 3 of 3)

- [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)
- [Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction](https://mlsanity.com/paper/2411.10921/) (2024)
- [Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks](https://mlsanity.com/paper/2210.12310/) (2022)

## Abstract (arXiv)

Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate -- the maximum rate that they can change their output -- is finite, solar's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warning to adjust thermal generator output in response to solar variations to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal data collected by the recently launched GOES-R series of satellites. Our model estimates a location's future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites and show that our approach yields errors close to that of a model using ground-truth observations.