Uncertainty of satellite-based solar resource data

1. Uncertainty of satellite-based
solar resource data
Marcel Suri and Tomas Cebecauer
GeoModel Solar, Slovakia
4th PV Performance Modelling and Monitoring Workshop, Köln, Germany
22-23 October 2015

2. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 2
About GeoModel Solar
Solar resource, meteorological and photovoltaic simulation data, software
and expert services for solar electricity industry
SolarGIS online database and PV software
• Planning and project development
• Asset management
• Forecasting
Bankable consultancy and project studies
• Solar resource assessment
• Photovoltaic performance assessment
• Regional solar mapping and monitoring
http://solargis.info
http://geomodelsolar.eu

3. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 3
Requirements for solarresource data in PV
Historical data
• Prospecting
• Planning and due diligence
Recent data
• Monitoring
• Performance evaluation and asset management
Forecasting
• Intraday
• Day ahead

4. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 4
Requirements for solarresource data in PV
Historical data
• Prospecting
• Planning and due diligence
Recent data
• Monitoring
• Performance evaluation and asset management
Forecasting
• Intraday
• Day ahead

5. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 5
Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

6. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 6
Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

7. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 7
Historicaldata: old ground measurements
• Limited number of high-grade measuring sites
• Large number of lower-accuracy sites
• Many sites stopped operation
• Older data may not represent well the recent climate
Typical features (lower accuracy sites)
• Lower accuracy equipment
• Less strict procedures: maintenance, calibration, cleaning
• Less rigorous or missing quality control and gap filling
• High uncertainty
Difficult to evaluate if data not available (at least) at hourly time step

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Historicaldata: old satellitemodels
• NASA the only global database
• Regional initiatives, e.g. NREL/SWERA
Typical features
• Simple methods, simple inputs
• Low resolution
• Low accuracy (limited or no validation)
• Only monthly averages
• Inconsistency: spatial, time
• Static (no updates or sporadic)
GHI difference (yearly)
between NASA SSE and SolarGIS

9. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 9
Old practices:Historicaldata for longtermassessment
• TMY for selected sites (NSRDB in the US):
• Mix of measured and modeled data
• Monthly values of ground-measured data
• Spatial interpolation
• Monthly values of modeled data
• Synthetic hourly data
Most common method of evaluation
• Expert-based weighted average of data from several sources
• Subjective
• Cannot be validated
• Missing continuity
• Missing interannual variability
• Deviation in longterm annual assessment ±10% to ±15% or more in GHI

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Old practices:Historicaldata for longtermassessment
TMY2 (NSRDB) Satellite-modelled data (SolarAnywhere)
Source: Solar Today 6/2012

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Old practices:Recentdata forperformance evaluation
Typical situation
• Low accuracy sensors are installed
• Mistakes in installation
• Little maintenance
• Insufficient cleaning
• No rigorous data quality control
• Problematic gap filling
=> High (unknown) uncertainty
=> Disputable results

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Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

13. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 13
Requirements for solarresource data
• Global (continental) coverage
• Long climate record
• Validated accuracy (based on at least hourly data)
• High temporal resolution (at least hourly)
• High spatial resolution (at least 4-5 km)
• Continuity
• Climate history for longterm assessment
• Recent data for performance assessment
• Nowcasting and forecasting of solar power
Way to go: modelled data supported by high-quality ground measurements

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How to acquire solarresource data
On-site measurements Satellite-based solar models
Forecasting: + numerical weather models
Source: GeoSUN Africa
Source: SolarGIS
Source: NOAA

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Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

16. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 16
Ground (on-site) measurements
ADVANTAGES LIMITATIONS
High frequency measurements (sec. to min.)
Higher accuracy, if properly managed
Limited geographical representation
Limited time availability
Costs for acquisition and operation
Maintenance and calibration
Data quality control
Source: GeoSUN Africa

17. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 17
Ground (on-site) measurements
ADVANTAGES LIMITATIONS
High frequency measurements (sec. to min.)
Higher accuracy, if properly managed
Limited geographical representation
Limited time availability
Costs for acquisition and operation
Maintenance and calibration
Data quality control
Source: GeoSUN Africa

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Ground measurements:Instruments
Instruments and their accuracy1
DNI
RSR2
SPN1 Pyrheliometers
First class
±4.5% ±5% ±1.0%
GHI
RSR2
SPN1 Pyranometers
Second class First class Secondary standard
±3.5% ±5% ±10% ±5% ±2%
Source: Delta-T Devices, K.A.CARE, Pontificia Universidad Católica de Chile
1 Theoretical uncertainty for daily summaries, at 95% confidence level
2 Approximately, after post processing

19. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 19
Ground measurements: Instruments
Instruments and their accuracy1
1 Theoretical uncertainty for daily summaries, at 95% confidence level
2 Approximately, after post processing
DNI
RSR2
SPN1 Pyrheliometers
First class
±3.5% ±5% ±1%
GHI
RSR2
SPN1 Pyranometers
Second class First class Secondary standard
±3.5% ±5% ±10% ±5% ±2%

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Ground measurements:Quality control
Identified issues Possible reasons
• Missing data
• Unrealistic values
• Time shifts
• Shading
• Artificial trends
• Problems with data logger
• Missing power
• Data transmission
• Time is not aligned
• Nearby objects + terrain
• Insufficient cleaning
• Misaligned sensors or tracker
• Calibration
• …
Physical limits,
Consistency
Data passed QC
Night-time
Shading
Other issues

21. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 21
Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

22. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 22
ADVANTAGES LIMITATIONS
Continuous geographical coverage
Spatial resolution approx. 3+ km
Frequency of measurements 15 and 30 minutes
Spatial and temporal consistency
Calibration stability
High availability (gaps are filled)
Up to 21+ years history − variability of weather
Lower accuracy of high frequency estimates
Modern satellite-basedmodels
Data inputs: JMA, ECMWF, NOAA,
SRTM
Source: SolarGIS

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Modern satellite-basedsolarresourcedata:
Interannualvariability
Yearly GHI: Standard deviation
(1999 to 2014)

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Modern satellitesolar resourcedata: Models
Models used in operational calculations
• Typically semi-empirical models
• Scientifically validated
• Tuned for different geographies
• Fast and stable results
Differences between approaches
• Satellite and atmospheric data preprocessing (radiometry and geometry)
• Multispectral and multiparametric cloud detection
• Management of various phenomena (high albedo, low angles…)
• Integration of atmospheric data into clear-sky model
• DNI and transposition models
• Correct management of terrain effects

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Modern satellitesolar resourcedata: Data inputs
Input data
• Cloud index: satellite data
• Aerosols, water vapour, ozone
• Correct representation of spatial and time variability
Differences between approaches
• Preprocessing
• Adapted for the specific models
• Geographical and temporal stability:
• Meteorological models are constantly changing
• Satellite sensors are degrading and upgrading

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Satellitedata: Availability(SolarGIS)
PRIME IODC GOES East Pacific GOES West
0° 57.5° -75° 145° -135°
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
GOES10,11,15
MSG1,2,3MFG4-7
MFG5,7
GOES8,12,13,14
MTSAT1,2
GOES 9
GMS5
Source: NOAA, EUMETSAT, JMA

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Satellitedata: spatialand time resolution
Cloud index
• Time resolution 15 and 30 minutes
• Spatial resolution 3 to ~7 km
GHI and DNI is affected primarily by cloud transmissivity
Source: EUMETSAT
Further from the image center pixel geometry is distorted
(for better visualization 100-km blocks are shown)

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Aerosol data:Daily time resolution
MACC-II AOD (aerosols) vs. AERONET ground measurements
Solar Village (Riyadh), Saudi Arabia
Ilorin, Nigeria
Source: ECMWF, AERONET, SolarGIS

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Terrain
Terrain altitude and shading is modelled with high accuracy
NASA SSE MSG native resolution Disaggregated with DEM
1° 4 x 5 km 250 x 250 m

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Why satellitedata do not match perfectlythe ground
measurements?
Ground measurements may deviate from satellite data, because of:
• Size of the satellite pixel and sampling rate
• Resolution and limitations of the input atmospheric data
• Imperfections of the models
• Site specific microclimate
• Issues in ground measurements
Example: SolarGIS (Peru)
Source: SolarGIS

31. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 31
Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

32. 4th PV Performance Modelling and Monitoring Workshop, Köln, Germany, 22-23 October 2015 32
Model uncertainty: Validationmetrics
• Bias: systematic model deviation
• Root Mean Square Deviation (RMSD) and Mean Average Deviation (MAD):
spread of deviation of values
• Correlation coefficient (R)
• Kolmogorov-Smirnoff index (KSI): representativeness of distribution of
values
High-accuracy ground measurements are to be used

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Model uncertainty: Validationmetrics
• Bias: systematic model deviation
• Root Mean Square Deviation (RMSD) and Mean Average Deviation (MAD):
spread of deviation of values
• Correlation coefficient (R)
• Kolmogorov-Smirnoff index (KSI): representativeness of distribution of
values
High-accuracy ground measurements are to be used

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Bias: SolarGISuncertaintyof yearlyestimate
GHI
±3.9%**
±7.6%**
* 68.27% occurrence: standard deviation (STDEV) assuming simplified assumption of normal distribution
** 80% occurrence: calculated as 1.28155 STDEV − can be used for an estimate of P90 values
DNI
Source: SolarGIS

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Root-Mean Square Deviation:GHI
Uncertaintyof hourly, daily and monthly values
Global Horizontal Irradiation: DLR-PSA Almeria, Spain
RMSD Values Bias RMSD
Hourly Daily Monthly
[W/m2] [%] [%] [%] [%]
GHI 23005 2.8 0.6 12.0 5.4 1.5
DNI 21645 -14.5 -2.6 22.3 13.1 3.7 Source: DLR-PSA, SolarGIS
RMSD Daily RMSD MonthlyRMSD Hourly

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Root-Mean Square Deviation:DNI
Uncertaintyof hourly, daily and monthly values
Direct Normal Irradiation: DLR-PSA Almeria, Spain
RMSD Values Bias RMSD
Hourly Daily Monthly
[W/m2] [%] [%] [%] [%]
GHI 23005 2.8 0.6 12.0 5.4 1.5
DNI 21645 -14.5 -2.6 22.3 13.1 3.7
RMSD Daily RMSD MonthlyRMSD Hourly
Source: DLR-PSA, SolarGIS

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Model uncertaintyfor Global HorizontalIrradiation
Hourly values Daily Monthly Yearly
SolarGIS high uncertainty
• High latitudes
• High mountains
• Variable aerosols
• Reflecting surfaces
• Snow and ice
• Rain tropical region
SolarGIS low uncertainty
• Arid and semiarid regions
• Low aerosols
• Values are indicative, based on the analysis of 200+ sites
• Uncertainty for ground sensors considers that they are well maintained, calibrated and data are quality controlled
±4 to ±8%

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Model uncertaintyfor Direct Normal Irradiation
Hourly values Daily Monthly Yearly
SolarGIS high uncertainty
• High latitudes
• High mountains
• Variable aerosols
• Reflecting surfaces
• Snow and ice
• Rain tropical region
SolarGIS low uncertainty
• Arid and semiarid regions
• Low aerosols
±8 to ±15%
• Values are indicative, based on the analysis of 130+ sites
• Uncertainty for ground sensors considers that they are well maintained, calibrated and data are quality controlled

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Contents
Historical approaches
Solar resource data needs in PV
Ground measurements
Satellite-based solar resource modelling
Uncertainty of satellite-based models
Conclusions

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Conclusions 1/2
How SolarGIS data compare to ground measurements?
Limits
• Uncertainty of instantaneous values lower than solar sensors
• Inherent discrepancy, mainly high frequency measurements (e.g. 15-minute)
Advantages
• Uncertainty of aggregated values
• Comparable to lower accuracy sensors
• Better than data from insufficiently managed ground monitoring
• Radiometric stability and continuity
• Historical data available (from 1994 onwards) + recent data + forecasting
• Model can be adapted by ground measurements

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Conclusions 2/2
SolarGIS data uncertainty
Without Site adaptation
• GHI: ±4 to ±8%
• DNI: ±8 to ±15%
After site adaptation (best achievable):
• GHI: ±2.5
• DNI: ±3.5

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Thankyouforattention!
http://solargis.info
http://geomodelsolar.eu
Source: SolarGIS