Use case: energy and utilities
Forecasting renewable power generation
Satellite data improve solar forecasts in two ways. Geostationary weather satellites observe cloud fields over wide areas, and forecasters extrapolate the cloud motion to predict irradiance, typically four to six hours ahead. For longer horizons, satellite observations feed the numerical weather prediction models that forecast from several hours to days ahead.
- Reference
- UC-12
- Technologies
- Challenge
- Operations
- Updated
- Published by
- LETO SPACE
01When you need it
Why forecasts matter for the grid
Solar and wind power vary with the weather. The resulting fluctuations in supply strain grid stability, and grid balancing has become a major issue as these intermittent sources expand Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu).
Near-term forecasts help operators decide when to charge or discharge storage and when to ramp other generation up or down Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu). They also support generator scheduling and reduce the need for costly fast-ramping plants Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org). Assessing renewable production in real time from space data helps anticipate near-term energy prices as well Source: EU Agency for the Space Programme (EUSPA), 2024EU Agency for the Space Programme (EUSPA)EUSPA EO and GNSS Market Report, Issue 2, 20242024Open source (opens euspa.europa.eu).
02Technology
How it works
A forecast is built from different inputs depending on how far ahead it looks Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org):
| Horizon | Main input | How it works |
|---|---|---|
| Up to a few hours, in relatively stable sky conditions | Measurements at the plant | Statistical or machine learning models extend the measured time series; when the sky is variable, they rarely perform well |
| 10 to 20 minutes | Sky cameras on the ground | Images of the local sky show clouds approaching the plant |
| Typically 4 to 6 hours | Geostationary weather satellites | Cloud motion vectors extrapolate the observed cloud field, with updates every 10 to 30 minutes |
| Several hours to days or weeks | Numerical weather prediction | Physical models simulate the atmosphere, including the formation and dissipation of clouds |
Satellites play two roles in this chain. Geostationary satellites observe clouds over wide areas, and their images are the main input for solar forecasts up to several hours ahead Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org). Satellite observations also enter the weather models: at ECMWF they make the biggest contribution to forecast quality of all observation types Source: European Centre for Medium-Range Weather Forecasts (ECMWF), 6 July 2020European Centre for Medium-Range Weather Forecasts (ECMWF)Fact sheet: ECMWF's use of satellite observations6 July 2020Open source (opens ecmwf.int).
State-of-the-art PV forecasting services do not rely on one model. Intraday systems combine online measurements, satellite-based forecasts and weather model forecasts, and use statistical or machine learning methods to optimise the result Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org).
03Data
Which data and satellites
Satellite-derived irradiance
The Copernicus Atmosphere Monitoring Service (CAMS) provides global horizontal, direct and diffuse solar irradiation; the all-sky values are derived from Meteosat and Himawari observations. The record starts in February 2004 for the Meteosat field of view and is available from 1-minute to monthly resolution. CAMS names the monitoring of solar energy systems and their integration into supply grids among the uses Source: Copernicus Atmosphere Monitoring Service (CAMS), Atmosphere Data Store, accessed 6 October 2026Copernicus Atmosphere Monitoring Service (CAMS), Atmosphere Data StoreCAMS solar radiation time-series (dataset overview)accessed 6 October 2026Open source (opens ads.atmosphere.copernicus.eu). Using such data to plan a new plant is covered under site selection.
Production estimates
During operation, Earth observation supports yield estimation and short-term production forecasts for solar plants, and for hydropower it monitors precipitation, snowpack, soil moisture, river extent and reservoir levels to predict inflows Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu).
Climate information for energy
The Copernicus Climate Change Service delivers climate indicators relevant to the energy sector, such as electricity demand and renewables production Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu).
04Deliverables
What you get
- intraday forecasts of PV output for a plant or a portfolio, typically at hourly or 15-minute resolution, which matters for intraday power markets Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org)
- day-ahead forecasts that combine several weather models with statistical or machine learning methods Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org)
- estimates of regionally aggregated PV power, which grid operators need for marketing PV power and for grid management Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org)
- satellite-derived irradiance time series for monitoring the plants you operate Source: Copernicus Atmosphere Monitoring Service (CAMS), Atmosphere Data Store, accessed 6 October 2026Copernicus Atmosphere Monitoring Service (CAMS), Atmosphere Data StoreCAMS solar radiation time-series (dataset overview)accessed 6 October 2026Open source (opens ads.atmosphere.copernicus.eu)
05Limits
Limits
Limit 01
Satellites cover the next hours
Satellite cloud motion forecasts typically reach four to six hours ahead. Beyond that, numerical weather prediction is the main approach Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org).
Limit 02
No forecast without measurements
Time-series models need on-site measurements to be trained Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org). Satellite data do not replace your own metering.
Limit 03
Accuracy depends on the climate
The errors of weather model forecasts depend strongly on climatic conditions; the relative error is typically smaller in sunny areas Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org). Ask providers for error figures from sites with a climate like yours.
06Next steps
How we help
Whether a better forecast pays off depends on what forecast errors cost you today, in balancing, storage or trading. We help you quantify that, define the required horizon, resolution and error measure, and compare providers on the same terms in a requirements and procurement project.
More on implementation and pilots
The marketplace of Spacegraph, our discovery platform, has a category for energy and utilities.
07Use cases
Related use cases
- Selecting sites for solar parks and wind farmsHow satellite data help choose generation sites.
- Precise timing for power gridsWhy grid measurements depend on GNSS time.
- Automating renewable energy salesWhich data help automate the sale of renewable energy systems.
- Energy and utilitiesAll use cases for energy companies and utilities.
Frequently asked questions
Q1What data do we need to bring?
The location and technical data of the plants, and their measured output. Forecasts built from local measurements need on-site observations of irradiance or PV power Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org), and measured output is also what you test any forecast against. For forecasts of a whole region, a common difficulty is that PV power is not measured at sufficient resolution for most plants in many countries Source: International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16, 2024International Energy Agency, Photovoltaic Power Systems Programme (IEA PVPS), Task 16Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024)2024Open source (opens iea-pvps.org).
Q2Do satellite data help with wind power forecasts too?
Yes. Space data and services track weather patterns, cloud cover, solar irradiance and wind speed, which supports near-term forecasts of renewable production Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu). Radar data from Sentinel-1 are used for offshore wind resource mapping Source: EU Agency for the Space Programme (EUSPA), 26 May 2026EU Agency for the Space Programme (EUSPA)EU Space Market Report 2026, Issue 126 May 2026Open source (opens euspa.europa.eu); that question belongs to site selection.
Q3How do we start a pilot?
Pick one plant or one portfolio where forecast errors already cost money, for example through balancing energy or curtailment, and agree on the error measure you will use. Our consulting overview shows the path from the problem to operation, and an initial call shows whether a pilot is worth it.
Sources (5)
- EU Space Market Report 2026, Issue 1 (opens euspa.europa.eu)
- EUSPA EO and GNSS Market Report, Issue 2, 2024 (opens euspa.europa.eu)
- Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, Fourth Edition (Report IEA-PVPS T16-6:2024) (opens iea-pvps.org)
- CAMS solar radiation time-series (dataset overview) (opens ads.atmosphere.copernicus.eu)
- Fact sheet: ECMWF's use of satellite observations (opens ecmwf.int)
Contact
Where do forecast errors cost you money today? That is where we start.
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Tell one of our experts about your project. Together we work out where you stand, whether satellite data and AI can help and what a sensible next step would be.
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