# Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints

arXiv: https://arxiv.org/abs/2403.12873 · HTML: https://mlsanity.com/paper/2403.12873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction](https://mlsanity.com/paper/2003.01460/) (2020)
- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning](https://mlsanity.com/paper/2310.17356/) (2023)
- [ACLNet: An Attention and Clustering-based Cloud Segmentation Network](https://mlsanity.com/paper/2207.06277/) (2022)

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

- [SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models](https://mlsanity.com/paper/2502.10307/) (2025)

## Abstract (arXiv)

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The output irradiance values are transformed to focus on unknown short-term dynamics. Inspired by control theory, a noise input is used to reflect unmeasured variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen test data, the model achieves a mean absolute error of 74.34 W/m2 compared to a baseline 134.35 W/m2 using the persistence of cloudiness model.