# Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

arXiv: https://arxiv.org/abs/2411.10921 · HTML: https://mlsanity.com/paper/2411.10921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [Deep Photovoltaic Nowcasting](https://mlsanity.com/paper/1810.06327/) (2018)
- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China](https://mlsanity.com/paper/1909.11711/) (2019)
- [A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning](https://mlsanity.com/paper/2112.13974/) (2021)
- [Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations](https://mlsanity.com/paper/2206.10795/) (2022)
- [Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data](https://mlsanity.com/paper/2403.01653/) (2024)
- [CloudCast -- Total Cloud Cover Nowcasting with Machine Learning](https://mlsanity.com/paper/2410.21329/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [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)

Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with attention that focus on important regions of an image have been applied with success in many computer vision applications. However, whether such methods provide meaningful benefits for cloud movement forecasting, and how such improvements propagate through to downstream solar generation forecasting accuracy, remains under-explored. In this study, we conduct a large-scale empirical investigation of the impact of attention-based cloud forecasting on solar generation forecasting, addressing a gap that has been overlooked in the literature. To this end, we develop a pipeline that incorporates an attention-enhanced convolutional long short-term memory network and an existing self-attention-based video prediction method to forecast cloud movement using satellite imagery. The effectiveness of the resulting cloud forecasts is evaluated through their downstream impact on solar forecasting across 50 PV sites in Australia. We further provide insights into the cloud conditions under which attention-based cloud forecasting methods yield the most significant improvements in downstream solar forecasting accuracy. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.