# Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data

arXiv: https://arxiv.org/abs/1911.04932 · HTML: https://mlsanity.com/paper/1911.04932/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022)
- [A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning](https://mlsanity.com/paper/2112.13974/) (2021)
- [Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models](https://mlsanity.com/paper/2601.04751/) (2026)
- [Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions](https://mlsanity.com/paper/2206.03207/) (2022)
- [Local-Global Methods for Generalised Solar Irradiance Forecasting](https://mlsanity.com/paper/2303.06010/) (2023)
- [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)
- [Solar Power Prediction Using Satellite Data in Different Parts of Nepal](https://mlsanity.com/paper/2406.11877/) (2024)

## Abstract (arXiv)

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements is proposed. To do so, the model considers satellite-based measurements and weather-based forecasts, and employs a deep neural network structure that is able to generalize across locations; particularly, the network is trained only using a small subset of sites where ground data is available, and the model is able to generalize to a much larger number of locations where ground data does not exist. As a case study, 25 locations in The Netherlands are considered and the proposed model is compared against four local models that are individually trained for each location using ground measurements. Despite the general nature of the model, it is shown show that the proposed model is equal or better than the local models: when comparing the average performance across all the locations and prediction horizons, the proposed model obtains a 31.31% rRMSE (relative root mean square error) while the best local model achieves a 32.01% rRMSE.