# Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

arXiv: https://arxiv.org/abs/2310.17356 · HTML: https://mlsanity.com/paper/2310.17356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 26 Oct 2023.

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

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [A deep learning approach to solar-irradiance forecasting in sky-videos](https://mlsanity.com/paper/1901.04881/) (2019)

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

- [FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting](https://mlsanity.com/paper/2402.05823/) (2024)
- [Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints](https://mlsanity.com/paper/2403.12873/) (2024)
- [Precise Forecasting of Sky Images Using Spatial Warping](https://mlsanity.com/paper/2409.12162/) (2024)

## Abstract (arXiv)

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.