Uncertainty of satellite-based solar resource data · Uncertainty of satellite-based solar resource data Marcel Suri and Tomas Cebecauer GeoModel Solar, Slovakia 4th PV Performance

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

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


Requirements for solar resource data in PV

Historical data

• Prospecting

• Planning and due diligence

Recent data

• Monitoring

• Performance evaluation and asset management

Forecasting

• Intraday

• Day ahead


Requirements for solar resource data in PV

Historical data

• Prospecting

• Planning and due diligence

Recent data

• Monitoring

• Performance evaluation and asset management

Forecasting

• Intraday

• Day ahead


Contents

Historical approaches

Solar resource data needs in PV

Ground measurements

Satellite-based solar resource modelling

Uncertainty of satellite-based models

Conclusions


Contents



Ground measurements



Conclusions


Historical data: 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


Historical data: old satellite models

• 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


Old practices: Historical data for longterm assessment

• 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


Old practices: Historical data for longterm assessment

TMY2 (NSRDB) Satellite-modelled data (SolarAnywhere)

Source: Solar Today 6/2012


Old practices: Recent data for performance 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


Contents



Ground measurements



Conclusions


Requirements for solar resource 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


How to acquire solar resource data

On-site measurements Satellite-based solar models Forecasting: + numerical weather models

Source: GeoSUN Africa

Source: SolarGIS

Source: NOAA


Contents



Ground measurements



Conclusions


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



Ground (on-site) measurements


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



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


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%


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


Contents



Ground measurements



Conclusions



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-based models

Data inputs: JMA, ECMWF, NOAA, SRTM

Source: SolarGIS


Modern satellite-based solar resource data: Interannual variability

Yearly GHI: Standard deviation (1999 to 2014)


Modern satellite solar resource data: 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


Modern satellite solar resource data: 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


Satellite data: 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

GO

ES 1

0, 1

1, 1

5

MSG

1,2

,3M

FG 4

-7

MFG

5,7

GO

ES 8

,12

,13

,14

MTS

AT

1,2

GOES 9

GM

S 5

Source: NOAA, EUMETSAT, JMA


Satellite data: spatial and 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)


Aerosol data: Daily time resolution

MACC-II AOD (aerosols) vs. AERONET ground measurements

Solar Village (Riyadh), Saudi Arabia

Ilorin, Nigeria

Source: ECMWF, AERONET, SolarGIS


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


Why satellite data do not match perfectly the 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


Contents



Ground measurements



Conclusions


Model uncertainty: Validation metrics

• 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


Model uncertainty: Validation metrics

• 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


Bias: SolarGIS uncertainty of yearly estimate

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


Root-Mean Square Deviation: GHI Uncertainty of 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 Monthly RMSD Hourly


Root-Mean Square Deviation: DNI Uncertainty of 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 Monthly RMSD Hourly

Source: DLR-PSA, SolarGIS


Model uncertainty for Global Horizontal 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

• 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%


Model uncertainty for 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


Contents



Ground measurements



Conclusions


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


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


Thank you for attention!

http://solargis.info

http://geomodelsolar.eu

Source: SolarGIS

Documents

Uncertainty of satellite-based solar resource data · Uncertainty of satellite-based solar resource data Marcel Suri and Tomas Cebecauer GeoModel Solar, Slovakia 4th PV Performance