# Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models

arXiv: https://arxiv.org/abs/2601.04751 · HTML: https://mlsanity.com/paper/2601.04751/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 87 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data](https://mlsanity.com/paper/1911.04932/) (2019)
- [Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude](https://mlsanity.com/paper/2212.11745/) (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)
- [Surface solar radiation: AI satellite retrieval can outperform Heliosat and generalizes well to other climate zones](https://mlsanity.com/paper/2409.16316/) (2024)

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

- [Characterization and forecasting of national-scale solar power ramp events](https://mlsanity.com/paper/2603.26596/) (2026)
- [Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting](https://mlsanity.com/paper/2610.16110/) (2026)

## Abstract (arXiv)

We present a novel framework for spatiotemporal photovoltaic (PV) power forecasting and use it to evaluate the reliability, sharpness, and overall performance of seven intraday PV power nowcasting models. The model suite includes satellite-based deep learning and optical-flow approaches and physics-based numerical weather prediction models, covering both deterministic and probabilistic formulations. Forecasts are first validated against satellite-derived surface solar irradiance (SSI). Irradiance fields are then converted into PV power using station-specific machine learning models, enabling comparison with production data from 6434 PV stations across Switzerland. To our knowledge, this is the first study to investigate spatiotemporal PV forecasting at a national scale. We additionally provide the first visualizations of how mesoscale cloud systems shape national PV production on hourly and sub-hourly timescales. Our results show that satellite-based approaches outperform the Integrated Forecast System (IFS-ENS), particularly at short lead times. Among them, SolarSTEPS and SHADECast deliver the most accurate SSI and PV power predictions, with SHADECast providing the most reliable ensemble spread. The deterministic model IrradianceNet achieves the lowest root mean square error, while probabilistic forecasts of SolarSTEPS and SHADECast provide better-calibrated uncertainty. Forecast skill generally decreases with elevation. At a national scale, satellite-based models forecast the daily total PV generation with relative errors below 10% for 82% of the days in 2019-2020, demonstrating robustness and their potential for operational use.