# Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather

arXiv: https://arxiv.org/abs/2407.19663 · HTML: https://mlsanity.com/paper/2407.19663/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Retentive Network: A Successor to Transformer for Large Language Models](https://mlsanity.com/paper/2307.08621/) (2023)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [What drives the accuracy of PV output forecasts?](https://mlsanity.com/paper/2111.02092/) (2021)

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

- [A Survey of Retentive Network](https://mlsanity.com/paper/2506.06708/) (2025)

## Abstract (arXiv)

Solar energy is one of the most promising renewable energy resources. Forecasting photovoltaic power generation is an important way to increase photovoltaic penetration. However, the difficulty in qualifying the uncertainty of PV power generation, especially during hazy weather, makes forecasting challenging. This paper proposes a novel model to address the issue. We introduce a modified entropy to qualify uncertainty during hazy weather while clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy, respectively. Hyperparameters were adjusted using an optimization algorithm. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy compared to existing models.