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

TL;DR

This paper introduces a new framework for intraday spatiotemporal PV power forecasting at a national scale, comparing satellite-based models with traditional weather models and demonstrating high accuracy and reliability across Switzerland.

Contribution

It is the first study to evaluate spatiotemporal PV forecasting at a national scale and visualizes cloud impacts on PV production hourly and sub-hourly.

Findings

Satellite-based models outperform IFS-ENS at short lead times.

SHADECast provides the most reliable ensemble spread.

Forecast errors are below 10% for 82% of days in 2019-2020.

Abstract

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.