This document evaluates satellite-derived solar irradiation data from several databases at over 200 sites in Western Europe compared to ground measurements from 2011-2015. The physical cpp model from KNMI consistently achieved the highest accuracy with the lowest errors, though all databases showed potential for improved bias correction. Basic statistical modeling was able to significantly reduce errors for most databases, particularly for Macc-rad and HelioClim-3 versions. The evaluation demonstrates the suitability of different databases for various applications like yield assessment, performance monitoring, and sensor validation.

1. ©3E | | www.3E.eu
EVALUATION OF SATELLITE
IRRADIATION DATA AT 200 SITES IN
WESTERN EUROPE
22 October 2015 Karel De Brabandere | Karel.DeBrabandere@3E.eu

2. 3E in a nutshell
2
• Founded in 1999
• 120 experts, 18
nationalities
• Projects in over 30
countries, 5
continents
• Offices in (a.o.)
• Brussels
• Toulouse
• Cape Town
• London

3. Need for accurate irradiation data
3
Long term yield assessment
 Long term average
 Yearly variation
Performance evaluation
 Yearly reference yield
 Monthly reference yield
Fault detection
 Daily reference yield
 Hourly reference yield

4. Need for accurate irradiation data
4
On-site Irradiance
sensors can have
excellent accuracy
But in reality:
• Sensor type
• Dome / Flat
• Si / Thermopile
• Soiling
• Shading
• Orientation
• Degradation
• Availability
• …  Regular validation of sensors is a necessity!

5. Satellite irradiation data
5
Meteosat-8/9/10 (MSG) satellite imagery
• SEVIRI instrument
• Coverage: Europe, Africa
• Temporal resolution: 15 min
• Nadir spatial resolution: 3 x 3 km²
• Start: January 2004

6. Satellite irradiation data
6
Services evaluated
• macc-rad (SoDa)
• HelioClim-3 v3, v4, v5 (SoDa)
• cpp (KNMI)
• gsip (NOAA)
‘Empirical’ models
• Image  Cloud mask  Irradiance
• macc-rad and HelioClim-3
‘Physical’ models
• Image  Cloud properties  Irradiance
• cpp and gsip
Are the (rather new) physical models better than the empirical models?

7. Satellite irradiation data
7
Data evaluated
• Global horizontal irradiation
• Hourly / Daily / Monthly aggregated
• 2011 - 2015

8. Evaluation at 203 sites in Western Europe
8
Pyranometer data from national meteo stations
France: 160 MétéoFrance stations
Belgium: 12 RMI (KMI / IRM) stations
• Daily aggregated global horizontal irradiation
• 2012 - 2013
Netherlands: 31 KNMI stations
• Hourly global horizontal irradiation
• 2011 - 2015

9. Evaluation metrics
9
• Root Mean Square Error
(RMSE)
• Standard Deviation of Error
(SDE)
• Bias
RMSE [%]
𝑅𝑀𝑆𝐸2
= 𝑆𝐷𝐸2
+ 𝐵𝑖𝑎𝑠2
Daily data
Monthly data
Individual site 𝐸 𝑥 =
𝑥2
𝑁

10. Evaluation: 2012
10
Macc-rad: high bias
Hc3: improvement
with every version
Cpp: Best in class

11. Evaluation: 2013
11
Macc-rad: high bias
Hc3: improvement
with every version
Cpp: Best in class
Very similar results for
2013

12. Evaluation: Netherlands 2011
12
Why focus on
Netherlands?
• Consistent high
quality of meteo
• Almost no horizon
shading
• Almost no snow
• Hourly
• Long history up till
yesterday
• Free & verifiable
Hourly Data

13. Evaluation: Netherlands 2012
13
Why focus on
Netherlands?
• Consistent high
quality
• No horizon
shading
• Almost no snow
• Hourly
• Long history
• Free & verifiable

14. Evaluation: Netherlands 2013
14
Why focus on
Netherlands?
• Consistent high
quality
• No horizon
shading
• Almost no snow
• Hourly
• Long history
• Free & verifiable

15. Evaluation: Netherlands 2014
15
Why focus on
Netherlands?
• Consistent high
quality
• No horizon
shading
• Almost no snow
• Hourly
• Long history
• Free & verifiable

16. Evaluation: Netherlands 2014 April – 2015 Feb
16
NOAA gsip
• Data from March
2014 onwards
Evaluation on 11
months!
Hourly, daily:
rather poor accuracy
Monthly:
good accuracy

17. Potential for improvement?
17
Average Bias
• MACC-RAD ~10%
• HC3v3 ~2.5%
• Other 0 - 2%
• Mostly positive
 Bias correction!
Average bias of standard
deviation
• Minor systematic
deviations
 MOS correction Average Bias ≠ RMS Bias !!!

18. Potential for improvement?
18
Daily Bias pattern
(Bias grouped by
‘hour of day’)
• Systematic
patterns for all
databases
• Huge impact on
Plane-of-Array
irradiation!
 More advanced
MOS correction

19. Potential for improvement?
19
Basic MOS correction
of:
• Bias
• Bias of SD
𝑌 = 𝑏 ∙ 𝑋 𝑝
 Only 2 parameters
• Factor 𝑏
• Exponent 𝑝

20. Potential for improvement?
20
Macc-rad:
• Huge reduction of
Bias
• Smaller reductions
in SDE
Small, but significant
improvements for
HC3v3 and cpp
• monthly RMSE
cpp: 4%  3%

21. Conclusions
3E Profile: Full Scope Consultancy & Software Services in Sustainable Energy 21
Fault detection (hourly, daily)
 sensor, satellite if no sensor present
Follow-up on plant performance (monthly, yearly)
 good satellite or validated sensor
Expected yield (design)
 very good sensor or good satellite
Sensor validation
 good satellite
Data web service in development for processed
cpp data: www.3E.eu/datacentre

22. Acknowledgements
22
SoDa macc-rad, HelioClim3: http://www.soda-pro.com/
KNMI msg-cpp: http://msgcpp.knmi.nl/
NOAA gsip: http://www.class.ncdc.noaa.gov/
KNMI hourly data: http://knmi.nl/nederland-nu/klimatologie/uurgegevens
RMI: http://www.meteo.be/
Météo-France: http://www.meteofrance.com/
3E provides:
• cpp data for performance analysis
• Sensor validation
• Data web service at www.3E.eu/datacentre (soon)
www.3E.eu

23. Thank you
23
Brussels
Toulouse
Gent
Louvain-La-Neuve
Beijing
Istanbul
Cape Town
London
Milano
www.3E.eu