# Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting

arXiv: https://arxiv.org/abs/2005.11246 · HTML: https://mlsanity.com/paper/2005.11246/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 12 of 12)

- [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)
- [A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning](https://mlsanity.com/paper/2112.13974/) (2021)
- [SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks](https://mlsanity.com/paper/2206.00747/) (2022)
- [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)
- [SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT](https://mlsanity.com/paper/2306.11682/) (2023)
- [Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series](https://mlsanity.com/paper/2503.00250/) (2025)
- [Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions](https://mlsanity.com/paper/2206.03207/) (2022)
- [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)
- [Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting](https://mlsanity.com/paper/2605.19074/) (2026)
- [Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models](https://mlsanity.com/paper/2609.11135/) (2026)

## Abstract (arXiv)

Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energy supply. A promising approach to anticipate irradiance changes consists of modeling the cloud cover dynamics from ground taken or satellite images. This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables. We evaluate the models on a set of irradiance measurements and corresponding sky images collected in Palaiseau (France) over 8 months with a temporal resolution of 2 min. To outline the learning of neural networks in the context of short-term irradiance forecasting, we implemented visualisation techniques revealing the types of patterns recognised by trained algorithms in sky images. In addition, we show that training models with past samples of the same day improves their forecast skill, relative to the smart persistence model based on the Mean Square Error, by around 10% on a 10 min ahead prediction. These results emphasise the benefit of integrating previous same-day data in short-term forecasting. This, in turn, can be achieved through model fine tuning or using recurrent units to facilitate the extraction of relevant temporal features from past data.