Ground measured data vs meteo data sets:57 locations in India_01.01.2020

1
Comparing one-year data of 57 on-ground
sites Pyranometers with four most popular
meteo-data sets
Correlation of Meteo-Data Sets
with ground measured data
CONSULTING
Gensol Engineering Limited
31st December 2019

2
A. Introduction
B. Key Takeaways for Stakeholders
C. About Meteo-Databases
D. Understanding Correlation coefficient
D. Methodology
E. Analysis & Results
Index

3
Gensol has carried out an exercise to correlate the Actual Global
Tilted Irradiation (AGTI) (based on pyranometer) received with
respect to expected GTI (EGTI) from various Meteo-databases.
Gensol has collected AGTI databases of 57 sites of data from
operational sites spread across in India
A) Introduction
The market of solar PV energy has grown in vast ways and is
quite advanced. The radiation of solar PV energy plays a very
important role in the development of any solar project. Solar
energy generation is reliant on solar radiation of a particular
location. Good solar radiation (direct & diffused) results in the
higher generation and improved financial returns.
Thereby, current industry relies completely on meteo-
databases in the country to predict solar radiation namely;
▪ Meteonorm-7.2
▪ SolarGIS,
▪ NASA (National Aeronautics and Space Administration),
▪ NREL (National Renewable Energy Laboratory),
▪ Actual data measured from Pyranometer (located at
respective sites)
Meteo-databases are categorized in two ways: ground-based
(terrestrial) & satellite-based. All the databases have different
uncertainties, resolution and deviation % when compared
with actual data. Solar radiation varies location to location
depending on latitude & longitude, azimuth angle, weather
conditions etc.

4
❑ Significance of Correlation Co-efficient - Statistical measure that calculates the strength of the relationship between Actual
Global Tilted Irradiation (AGTI) w.r.t. to Expected Global Tilt Irradiation (EGTI) for month on month as well as annual data.
B) Key Takeaways for Stakeholders
Note: *SolarGIS dataset - 33 On ground sites
NREL, Meteonorm, NASA - 57 On ground sites
Co-efficient
Factor Range
Category SolarGIS Meteonorm NASA NREL
0.8-0.9 High 58% 58% 53% 58%
0.9-1.0 Extreme 27% 21% 16% 26%
Weightage of Sites in %
Standard
Deviation
Range
Category SolarGIS Meteonorm NASA NREL
<2.5% Low 39% 35% 16% 0%
>2.5 to <5% Medium 6% 28% 32% 7%
Weightage of Sites in %
❑ Significance of Standard Deviation- Statistical measure that calculates the amount of variation between annual AGTI and EGTI
for various meteo-dataset for different site

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❑ Meteo-Database Correlation Relativeness – NREL, Meteonorm & SolarGIS* are having maximum sites (58% of total sites) with
high correlation, which represents equal correlation factor for mentioned databases.
Correlation
Coefficient
Range
Relation
0.0 - 0.6 Low
0.6- 0.8 Medium
0.8 -0.9 High
0.9 - 1.0 Extreme
SolarGIS Meteonorm NASA NREL
Low 0% 0% 4% 0%
Medium 15% 21% 28% 16%
High 58% 58% 53% 58%
Extreme 27% 21% 16% 26%
0% 0% 4% 0%15%
21%
28%
16%
58% 58%
53%
58%
27%
21%
16%
26%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
SitesinPercentage(%)
Meteo-Database
Annual Solar Radiation Correlation – Sites in %
Meteo-database with extreme & high correlation coefficient is recommended.

6
❑ Meteo-Database Standard Deviation - Meteonorm & SolarGIS* are having maximum sites with lowest standard deviation.
Standard
Deviation
Relation
<2.5% Low
>2.5 to <5% Medium
>5% to
<7.5%
High
>7.5% Extreme
SolarGIS Meteonorm NASA NREL
Low 39% 35% 16% 0%
Medium 6% 28% 32% 7%
High 18% 23% 23% 21%
Extreme 36% 14% 30% 72%
39%
35%
16% 0%6%
28% 32%
7%
18%
23% 23% 21%
36%
14%
30%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
SitesinPercentage
Meteo-Database
Annual Solar Radiation Deviation- Sites in %
Meteo-database with low & medium standard deviation is recommended.

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Actual Global Tilted Irradiation(AGTI)
• Actual site radiation data of past one year (April 2018 – March
2019) for 57 locations (as seen on map) is collected on 15 minute
interval for comparative analysis.
• Pyranometer at sites are placed according to the actual orientation
of Plane of Array (PoA) of each project.
• Pyranometer used at sites are either of First Class, second class or
Secondary Standard with region-wise accuracy of bubble level
ranging between 0.1° to 0.2° and is properly calibrated as per ISO
standards and OEM guidelines
• Where actual radiation data was not available for certain time
period, AGTI values have been corrected considering near average
values across the particular time period.

8
Expected Global Tilted Irradiation(EGTI)
• For each project, PVSyst simulation has been run
considering actual tilt angle of the PV arrays installed at
sites and
• Four PV Syst considering a different meteo data set
have been run for each site and resultant global tilted
irradiation (EGTI) has been taken for each case.
• Since month on month GHI to GTI gain is following
similar trend for all sites and meteo-databases
considered, analysis carried on EGTI or Meteo Data
based GHI will not impact the results significantly
• P50 & P75 EGTI values have been considered for
evaluating correlation1.
[1] https://www.fourmilab.ch/rpkp/experiments/analysis/zCalc.html
-5.00%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Gainin%
Month-Wise Expected GHI VS Expected GTI Gain (%)
SolarGIS Deviation% Meteonorm Deviation %
NASA Deviation % NREL Deviation %

9
C) About Meteo-Databases
Data
Source Satellite-Based Satellite & Ground-Based Satellite-Based Satellite & Ground Based
No. of Meteo-
Stations
- 8350 - 1454
Time Period
- Since 1999 to 2018
depending on the satellite
data coverage
- 1981-1990 & 1991-2010 for
solar irradiation on a global scale
1983-2005 2000-2014
Temporal
Resolution2
(Time Step)
- Original 10/15/30
minutes depending on the
satellite region
- 1 minute and hourly modelled
data
3-hourly -Monthly
and annual average
daily total
30/60 minutes interval
It is essential to consider appropriate solar radiation database
in order to evaluate performance of any solar photovoltaic
power plants in India. In India, Meteonorm & SolarGIS
databases are considered as bankable and acceptable in the
market considering defined uncertainties
The energy generation from a Solar Power Plant (SPP) is
dependent on the solar radiation incident on earth’s surface which
further depends on many factors like the length of atmospheric path,
dust concentration, moisture content, scattering effect, etc. There
are many trusted databases which factor in the same.

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B) About Meteo-Databases
Data
Spatial Resolution3
(Solar radiation)
0.25km x 0.25km 8km X 8 km 111km X 111km
10kmX10km ( SUNY Model )
4kmX4km
Uncertainty in
Global Horizontal
Irradiation (GHI)
±4.0% 2% to 10% 6.86% to 11.29% -20.00% to +5%
Uncertainty in
Direct Normal
Irradiance (DNI)
±8.0% 3.5% to 20% (-4.06%) to 7.4% (-30.00%) to +8%
Bankability in
Country
High High Medium Low
[2] Temporal resolution =Revisit time of a satellite between two successive image acquisitions between the same area.
[3] Spatial resolution = Refers to the number of pixels utilized in construction of the image.

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C) Understanding Correlation Coefficient
• The correlation coefficient is a statistical measure that
calculates the strength of the relationship between the relative
movements of two variables or array.
• The Correlation Coefficient4 is calculated according to the
following formula:
S.No Correlation Coefficient Relativity
1 -1.00 to 0.00 No relation
2 0.00 to 0.60 Low Correlation
3 0.60 to 0.80 Medium Correlation
3 0.80 to 0.90 High Correlation
4 0.90 to 1.00 Extreme Correlation
[4] The_Correlation_Between_Renewable_Generation_and_Electricity_Demand_A_Case_Study_of_Portugal, March 2016
• Where ‘x’ & ‘y’ are two arrays of variables to be correlated and
‘n’ represents number of variables to be correlated. The
mentioned formula can be written simply using standard excel
function as follows:
• The coefficient ranges from −1 to 1, where the latter
indicates a positive linear correlation between the
variables y and x, i.e., both variables present the same
behavior (y increases as x increases), and −1 implies a
negative linear correlation.
• The value of 0 indicates no linear correlation between
the variables. The ranges have been categorized as
below:
• It should be noted that the correlation coefficient is
different from standard deviation. In statistics, the
standard deviation is a measure of the amount of
variation or dispersion of a set of values.= 𝐶𝑂𝑅𝑅𝐸𝐿(𝐴1: 𝐴15, 𝐵1: 𝐵15)

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D) Methodology
North
Zone
East
Zone
South
Zone
West
Zone
Correlation Evaluation
Correlation coefficient is calculated for the following two cases:
CASE 1 - Month-wise AGTI values are correlated with month- wise EGTI P50 values.
CASE 2 - Annual AGTI values are correlated with annual EGTI P50 & P75 values.
Unavailability of SolarGIS files for some of the sites.
Limitations

13
E) Analysis & Results (North, West & East Zone) (Case-1)
❑ EGTI Vs AGTI Correlation Coefficient Estimation: It has been observed that average correlation factor of NREL is marginally
highest followed by Meteonorm & SolarGIS.
Note :1) The SolarGIS correlation factor is not included for the sites where data is not available.
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Mansa Mansa FirozepurFirozepurFirozepur Jalaun Jalaun North
West
Delhi
Mirzapur
EGTI (P50) & AGTI Data Correlation Cofficient- North Zone
Actual Meteonorm NREL SolarGIS NASA
Punjab Uttar Pradesh
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Jodhpur Jodhpur Jodhpur Jodhpur Patan Patan Gaya
EGTI (P50) & AGTI Data Correlation Cofficient- West & East Zone
Rajasthan Gujarat Bihar

14
E) Analysis & Results (South Zone)(Case-1)
❑ EGTI Vs AGTI Correlation Coefficient Estimation: It has been observed that average correlation factor of NREL is marginally
higher followed by Meteonorm & SolarGIS.
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
EGTI (P50) & AGTI Data Correlation Coefficient- South Zone
Karnataka TelanganaAndhra Pradesh

15
E) Analysis & Results (Case -2)
❑ EGTI Vs AGTI Plotting: The trend plotted by AGTI is almost similar to NREL followed by Meteonorm (showing high correlation).
Whereas, at the same time, we can observe that the standard deviation is highest for NREL.
Districts in North Zone
1800.00
1850.00
1900.00
1950.00
2000.00
2050.00
2100.00
2150.00
2200.00
Mansa
Mansa
Firozepur
Firozepur
Firozepur
Jalaun
Jalaun
NorthW-Delhi
Mirzapur
GlobalTiltedRadiation(kwh/m2/annum)
Annual EGTI (P50 &P75) Vs AGTI
Actual SolarGIS P50 SolarGIS P75 Meteonorm P50 Meteonorm P75
NASA P50 NASA P75 NREL P50 NREL P75
Punjab Uttar Pradesh

16
E) Analysis & Results (Case - 2)
1850.00
1900.00
1950.00
2000.00
2050.00
2100.00
2150.00
2200.00
2250.00
2300.00
2350.00
2400.00
2450.00
2500.00
Kalaburagi
Bagalkot
Bijapur
Bijapur
Bijapur
Tumkur
Tumkur
Bidar
Raichur
Gulbarga
Bidar
Bijapur
Anantapur
Nagarkurnool
Rangareddi
Jagtial
Warangal
Nirmal
Kamareddy
Prakasam
Chittoor
Adilabad
sangareddy
Medchal
Adilabad
Karimnagar
Guntur
sangareddy
Jangaon
Nizamaba
Nalgonda
Sircilla
Bhuvanagiri
Peddapalli
Medak
Nellore
Peddapalli
Sircilla
Jangaon
Jagithyal
Nizamabad
GlobalTiltedRadiation(kwh/m2/annum)
Annual EGTI (P50 &P75) Vs AGTI
Karnataka
Andhra
Pradesh
Telangana
Districts in South Zone
2) Average correlation coefficient is considered for sites only where SolarGIS data is available for consistency.

17
E) Analysis & Results (Case - 2)
Districts in West Zone & East Zone
Note : 1) The SolarGIS correlation factor is not included for the sites where data is not available.
2) Average correlation coefficient is considered for sites only where SolarGIS data is available for consistency.
1850.00
1900.00
1950.00
2000.00
2050.00
2100.00
2150.00
2200.00
2250.00
2300.00
2350.00
2400.00
Jodhpur
Jodhpur
Jodhpur
Jodhpur
Patan
Patan
Gaya
GlobalTiltedRadiation(kwh/m2/annum
EGTI (P50 & P75) Vs AGTI
Rajasthan Gujarat Bihar

18
Annexure
Zone State Sr. No. Site Name District Name SolarGIS Meteonorm NASA NREL
North
Punjab
1 Mansa-1 Mansa 0.00 0.76 0.69 0.88
2 Mansa-2 Mansa 0.00 0.82 0.68 0.90
3 Mansa-3 Firozepur 0.00 0.83 0.59 0.90
4 Usmankhera Firozepur 0.00 0.89 0.67 0.90
5 Daulatpura Firozepur 0.00 0.91 0.62 0.93
Uttar Pradesh
6 Orai-1 Jalaun 0.00 0.75 0.92 0.79
7 Orai-2 Jalaun 0.00 0.78 0.93 0.79
8 Haidarpur North West Delhi 0.00 0.84 0.96 0.88
9 Mirzapur Mirzapur 0.00 0.83 0.85 0.87
Rajasthan
10 Khetusar Jodhpur 0.00 0.64 0.49 0.73
11 Bhadla Jodhpur 0.00 0.84 0.84 0.90
12 Khetusar Jodhpur 0.00 0.67 0.67 0.79
13 Bhadiachuran Ki Jodhpur 0.00 0.80 0.81 0.87
West Gujarat
14 Charanka Patan 0.98 0.98 0.94 0.93
15 Charanka Patan 0.97 0.99 0.94 0.94
Correlation factors between AGTI and EGTI

19
East Bihar 16 Jalsar & Chillam Gaya 0.83 0.82 0.75 0.91
South
Karnataka
17 Gulbarga Kalaburagi 0.00 0.87 0.89 0.87
18 Bagalkot Bagalkot 0.00 0.87 0.89 0.86
19 Sindagi Bijapur 0.00 0.89 0.88 0.88
20 Muddebihal Bijapur 0.00 0.78 0.73 0.71
21 Indi Bijapur 0.00 0.91 0.91 0.88
22 Pavagada 1-38 Tumkur 0.87 0.82 0.82 0.84
23 Pavagada 1-37 Tumkur 0.87 0.82 0.82 0.84
24 Chittaguppu Bidar 0.91 0.91 0.91 0.94
25 Raichur Raichur 0.90 0.87 0.86 0.87
26 Farhatabad Gulbarga 0.85 0.88 0.82 0.87
27 Bidar Bidar 0.92 0.94 0.92 0.94
28 Bijapur Bijapur 0.00 0.92 0.92 0.91
Andhra Pradesh 29 Veerabommana Halli Anantapur 0.92 0.88 0.85 0.92
Telangana
30 Veltoor Nagarkurnool 0.00 0.92 0.87 0.84
31 Gingurthy Rangareddi 0.90 0.92 0.90 0.91
32 Mallapur Jagtial 0.91 0.92 0.88 0.87
33 Waddekothapalle Warangal 0.83 0.84 0.78 0.90
34 Bhainsa Nirmal 0.00 0.74 0.76 0.78
Annexure

20
South Telangana
35 Amun, Kamareddy Kamareddy 0.00 0.90 0.86 0.88
36 Rudra Prakasam 0.00 0.87 0.83 0.88
37 Avaighna Chittoor 0.00 0.85 0.75 0.91
38 Beeravelly Adilabad 0.88 0.69 0.86 0.87
39 Sadashivpet sangareddy 0.85 0.85 0.84 0.89
40 Nagaram Medchal 0.91 0.88 0.90 0.89
41 Beeravelli Adilabad 0.86 0.82 0.87 0.78
42 Vettemula Karimnagar 0.85 0.82 0.89 0.79
43 Achampet Guntur 0.80 0.78 0.79 0.80
44 Gummadidala sangareddy 0.85 0.87 0.82 0.90
45 Ghanpur Jangaon 0.84 0.85 0.81 0.88
46 Renjal Nizamaba 0.84 0.88 0.80 0.89
47 Thukkapur Nalgonda 0.80 0.84 0.83 0.84
48 Sircilla Sircilla 0.91 0.91 0.89 0.92
49 Bhuvanagiri Bhuvanagiri 0.84 0.81 0.79 0.86
50 Kalvasrirampur Peddapalli 0.75 0.82 0.72 0.82
51 Medak Medak 0.91 0.92 0.90 0.95
52 Godhur Nellore 0.89 0.87 0.86 0.89
53 Manthani Peddapalli 0.85 0.88 0.82 0.85
54 Sircilla Sircilla 0.86 0.80 0.85 0.84
55 Jangaon Jangaon 0.80 0.79 0.78 0.86
56 Jagithyal Jagithyal 0.81 0.86 0.76 0.82
57 Padmajiwadi Nizamabad 0.62 0.85 0.83 0.88
Annexure

21
Email: solar@gensol.in | Web: www.gensol.in | Phone: +91 79 40068235 | Twitter: gensol_tweets
Gensol Engineering Limited
Corporate Office
A2, 12th Floor Palladium, Opp. to Vodafone House , Corporate Road, Prahladnagar, Ahmedabad, Gujarat. India - 380015

Ground measured data vs meteo data sets:57 locations in India_01.01.2020

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