Accurate and bankable solar irradiation data

Increase the value of your project by reducing your yield uncertainty with accurate and bankable solar irradiation data

Higher accuracy, reduced uncertainty

Our solar dataset achieved an normalized mean bias error of +0.29% with a standard deviation of 2.47%, leading to higher P90 value for your project estimates.

+
0.29
%
Mean Bias Error
2.47
%
Standard Deviation

Optimise energy performance with sub-hourly resolutions

Access Typical Meteorological Year (TMY) & Historical Time Series data with time resolutions as precise as 15mn, 10mn, and 5mn, generating TMY files for exceedance scenarios of P75, P80, P85, P90, and P95.

Get your data in less than 5 minutes

Enjoy seamless access to real-time data with lightning-fast delivery in under 5 minutes for instant analysis.

Seamlessly integrate with your PV tools

Get file format's that are compatible for analysis with your existing software's.

PVSyst
SAM
PlantPredict
Helioscope
ieco.io

95% of validated sites with GHI MBE between -2.5 and +2.5

The map below shows the detailed statistics for each validation site. Click on the site marker to see the values. The detailed validation results are also available in our whitepaper below.

Independently Validated

Eurac Research's Institute for Renewable Energy validated our high-accuracy solar data against 420+ years of European ground measurements.

Data Accessible in PVSyst

You can easily access 3E solar data in PVSyst and import 3E TMY hourly data in a recognized format.

Reliable data across the world

Comprehensive Parameters

Global Horizontal (GHI) & Diffuse Horizontal (DIF)

Direct Normal (DNI) & Global Tilted (GTI)

Air temperature, wind speed & wind direction

Relative humidity

Rigorously Validated

Mean bias error of +0.29%

Standard deviation of 2.47%

7 years of validation

Higher accuracy of data leads to lower uncertainty for your projects

3E
PVGIS
Meteonorm
Download wait time
1-5 mins
1 min
1 min
Mean Bias Error
+0.29 %
0.86 %
N/A
Standard Deviation
2.47 %
3.08 %
N/A
90% of sites have bias (GHI) in between (assuming normal distribution)
-3.77 - 4.35
4.21 - 5.93
N/A
Mean RMSE (GHI)
17.18
N/A
N/A
Comprehensive, global, independent validation
Validation Sites
77
16
N/A
Satellite based estimation as primary method
Validation years
7 years of validation
1-2 years of validation
Not available
Near real time data
10+ years of historical data
Time resolution
5, 10, 15 min, hourly, daily & monthly
Hourly, daily & monthly
Hourly & monthly

Ready to fast track your project's success?

Book a demo or access our data for free now to reduce yield uncertainty and drive success.

Resources

Related Resources

Explore our knowledge hub for insightful articles, guides, and solutions

Modeling Methodology

Satellite Imagery

The cloud, radiation and precipitation properties are retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of four different geostationary weather satellites:

  • Meteosat-11 (PRIME), covering Europe and Africa with a 3x3 km² resolution. Data is available as from 2004.
  • Meteosat-8 (IODC), covering the Europe, Africa, South-West Asia and the Indian Ocean with a 3x3 km² resolution. Data is available as from 2019.
  • GOES-East' Northern Hemisphere Extension, covering North- and Central-America with a 2x2 km² resolution. Data is available as from 2019
  • Himawari-8 Real-Time, covering Oceania and South-East Asia with a 2x2 km² resolution. Data is available as from 2021.


Identifying clouds, radiation & precipitation using CPP

The Cloud Physical Properties (CPP) algorithm is developed at KNMI to derive meteorological products from the Meteosat Second Generation (MSG) satellite. Based on the MSG-CPP, we identify the cloud, radiation & precipitation properties in 4 distinct steps:

First, cloud-free pixels are separated from cloud-contaminated & cloud-filled pixels with the GEO v2018 algorithms developed by the NWC SAF.

Second, the cloud optical and microphysical properties (i.e. top temperature, phase, optical thickness & particle size) are derived using algorithms developed in the CM SAF (Benas et al. 2017; Roebeling et al. 2006). The retrieval of most these properties relies on observations of solar backscattered radiation, and is thus limited to daytime (solar zenith angle smaller than 84°).

Third, the total surface solar irradiance and its direct & diffuse components are derived using the methods described in Greuell et al. 2013.

At last, the precipitation intensity is estimated based on the retrieved cloud properties during daytime (Roebeling & Holleman 2009) and based on statistical relationships with the observed infrared brightness temperatures (Brasjen et al. 2015).


Parallax Correction

A parallax correction corrects the derived irradiance properties for the relative position of both the satellite and the sun.


TMY Pxx

P50 or Pxx typical meteorological years (TMY) are created based on Cebecauer & Suri 2015, but different weighing actors are applied to tailor the creation of TMY's to the application of solar power simulations.

Solar Time Series Data Sheet

Geographic Coverage
Temporal Coverage
Satellite Series
Data available since
Data available to
MSGPRIME
01/02/2004
H-40 min
GOES-East
01/07/2019
H-40 min
IODC
01/09/2019
H-40 min
HIMAWARI
01/06/2021
H-40 min
Temporal Resolution
5 min, 10 min, 15 min, 30 min, hourly, daily, monthly, annually
Spatial Resolution
Satellite Series
Spatial resolution
MSGPRIME
3x3 km
GOES-East
2x2 km
IODC
3x3 km
HIMAWARI
2x2 km
Tracking Configuration
Global Horizontal Irradiance (GHI) (Wh/m²)
Diffuse Horizontal Irradiance (DIF) (Wh/m²)
Direct Normal Irradiance (DNI) (Wh/m²)
Global Tilted Irradiance (GTI) (Wh/m²)
Air temperature at 2m (°C)
Wind speed at 10m [m/s]
Wind direction at 10m [degrees]
Relative humidity at @ 2m [%]
Total precipitation, including rain and snow [mm/h] [1]
Snowfall [mm/h] [1]
Data Parameters
Fixed
1-axis
2-axis
incl. Backtracking
File Formats
3E Standard
3E-PVsyst
Data Formats
CSV
JSON [2]
Data Access
Through API for near real time, project based [3]
Through subscription on our SaaS platform SynaptiQ for long term historical time series
Data Access
Typically between 1 to 5 min depending on the requested period duration
  • [1] Available in SynaptiQ Project Development
  • [2] Only through API access
  • [3] API documentation available upon request

Typical Meteorological Year (TMY) Data Sheet

Temporal Coverage
From 1/2/2004 to H-40
Temporal Resolution
5 min, 10 min, 15 min, 30 min, hourly, daily, monthly
Spatial Resolution
3x3 km
Data Parameters
GlobalHorizontal Irradiance (GHI) (Wh/m²)
DiffuseHorizontal Irradiance (DIF) (Wh/m²)
Direct Normal Irradiance (DNI) (Wh/m²)
Global Tilted Irradiance (GTI) (Wh/m²)
Air temperature at 2 m (°C)
Wind speed at 10 m [m/s]
Wind direction at 10 m [degrees]
Relative humidity at @ 2 m [%]
Total precipitation, including rain and snow [mm/h]
Snowfall [mm/h]
Tracking Configuration
Fixed
1-axis
2-axis
Data Formats
3E Standard
TMY3
3E-PVsyst
Data Formats
CSV
Data Access
Through subscription on our SaaS platform SynaptiQ for long term historical time series
Download Waiting Time
Typically 6-8 min

Have a question?

Request a Demo

Validation & Accuracy

Experience the difference of meticulously validated data. Dive into in-depth insights by downloading our whitepapers today.

2022 external validation by Eurac: solar irradiation data

The high accuracy of our data has been confirmed by the Institute for Renewable Energy of Eurac research center. In its validation report, Eurac compared 3E's solar data to more than 420 years of ground measured data from the best public meteo stations in Europe. They concluded their paper by "the 3E solar dataset achived an overall normalized mean bias error of 0.48% with a standard deviation of 2.3%. The normalized root mean square error ranges from 18% for hourly data to 2.16% for annual data". Those excellent results make 3E solar irradiation data one of the most accurate datasets in Europe.

2022 internal validation: satellite-based irradiation data

At 3E, we take data validation seriously. The mean bias error for our data is close to zero for the different validations, i.e. respectively 0.3% and 0.7% for the global or European stations. The absence of bias, combined with a low uncertainty of the bias, makes 3E’s satellite- based service a bankable source of irradiation data for long-term yield assessments (or LTYA) of photovoltaic systems. In this paper, we describe the permanent internal validation framework and the achieved results in detail.

2018 external validation: TÜV Rheinland

TÜV Rheinland was asked to perform a detailed 3rd-party validation of 3E’s satellite derived solar irradiation data. They have validated 3E’s satellite based solar irradiation data over 35 meteo stations in Germany. After processing, filtering and quality control of the data, the spatial aggregated results are: a mean bias of 0.7% with a standard deviation of 2.5% a monthly and daily RMSE of respectively 4.6% and 10.6%. TÜV Rheinland concluded that "this high accuracy from the results over all years confirms the excellent quality of 3E’s solar irradiation data (GHI) in the validated moderate-climate region."

Join the Future Energy Movement: Sign up for our newsletter and be at the forefront of a sustainable world. Stay informed about the latest breakthroughs in solar, wind, and green fuels. Together, let's ignite a cleaner, brighter future.
Subscribe
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
By subscribing you agree to receive updates from our company. For more information please read our Privacy Policy
© 2023 3E. All rights reserved. Any reproduction, modification or distribution of all or part of the content,
graphics, logos, text, database, layout, or design of the website is prohibited without the prior written consent of 3E NV/SA.