This document summarizes a study evaluating the accuracy of using modeled meteorological data from a geographical information system (PVGIS) to predict the energy production of PV modules. Monthly averages of measured solar irradiance and temperature data from Ispra, Italy were used as inputs to the PVGIS algorithm. The predicted annual energy production of two PV module types agreed with measured outdoor values to within 1%. However, individual monthly predictions sometimes differed more, averaging out over the full year. The authors conclude the method provides sufficiently accurate predictions for periods of one year or more for practical energy modeling.

1. Subject number: 5.1 (PV Modules and Components of PV Systems)
Energy Rating of PV Modules Based on PVGIS Irradiance and Temperature Database
Robert P. Kenny, Thomas A. Huld, Susana Iglesias Puente
European Commission, Joint Research Centre, Renewable Energies Unit
TP 450, I-21020 Ispra (VA), Italy
*Tel: +39(0332)789287, FAX: +39(0332)789268, e-mail: robert.kenny@jrc.it
Purpose of work:
A method for the Energy Rating of PV modules based on performance surfaces uses the module
temperature and incident irradiance as independent variables which has been successful in prediction
of energy production for crystalline Si (c-Si) modules when used with locally obtained meteorological
data [1]. However, to be practical an Energy Rating method must be able to perform satisfactorily
using synthesized meteorological data, such as those that are modeled in the geographical information
system (PVGIS) [2]. Here the performance of c-Si PV modules is predicted over large geographical
regions based on solar radiation and temperature geospatial data and a relation for the conversion
efficiency of c-Si PV modules at varying conditions. In order to evaluate the accuracy of these
predictions, a study has been made of the algorithm used to calculate daily variations of both
irradiance and temperature from the monthly means contained in the geographical information system.
Approach:
To perform the evaluation, the real outdoor measured performance of a poly-Si and a mono-Si module
measured over one year at Ispra, North Italy, was compared with the estimated performance based on
the algorithm. In order to avoid having to compare a specific year (in this case 2003) with the 10-year
averages contained in the PVGIS database, monthly averages from locally measured irradiance and
temperature data were calculated and used by the algorithm. In this way, the algorithm was studied on
a comparable dataset.
Scientific innovation and relevance:
The accurate prediction of the real energy production of PV modules in any required location is crucial
for the growth of solar electricity generation since the return on investment is directly linked to the
kWh produced. The combination of a proposed energy rating procedure for PV modules with a web
based geographical information system of meteorological data will help meet this need, and its
accuracy is verified in this work.
Results:
During the year studied and for both c-Si module types, the predicted and measured energy production
agree to better than 1 %. However, it is noted that individual months can have larger discrepancies,
which have been found to average out over the year.
Conclusions:
The overall prediction accuracy of better than 1 % over periods up to one year leads us to believe that
the method is sufficiently accurate for practical PV performance prediction. The individual monthly
variations may often be larger however, so it is expected that one year period would be the minimum
useful prediction period to be supported at this point in the development of the method. This is not
expected to be a limitation given that energy production over a module lifetime of up to 30 years is
needed.
[1] R. P. Kenny, E. D. Dunlop, H. Ossenbrink, H. Müllejans, "A Practical Method for the Energy Rating of c-Si PV Modules
based on Standard Tests", Progress in Photovoltaics, in press, DOI: 10.1002/pip.658.
[2] T. Huld, M. Šúri, R. P. Kenny, E. D. Dunlop, "Estimating PV Performance Over Large Geographical Regions", 31st IEEE
Photovoltaic Specialists Conference, 3-7 January 2005, Lake Buena Vista, Florida, USA.

2. Kenny et al, Energy Rating of PV Modules Based on PVGIS Irradiance and Temperature Database
Supporting information:
Module Energy Rating using Performance Surface
Figure 1 shows the measured performance surface of the poly-Si module used in the energy prediction.
This surface is used to calculate instantaneous power outputs according to irradiance and temperature,
which are then integrated to obtain the energy output over the period in question.
AI01 Indoor
Rank 196 Eqn 1132 z=(a+blnx+cy)/(1+dlnx+e(lnx)^2+fy)
r^2=0.99974319 DF Adj r^2=0.99972731 FitStdErr=0.2585647 Fstat=76302.371
a=-0.93125496 b=0.40257114 c=-0.0040224985
d=-0.26053276 e=0.017375655 f=0.00010847924
60 60
50 50
40 40
Pmax [W]
Pmax [W]
30 30
20 20
10 10
0 0
55 9
8 00
50 7 00
45 0 6 00 00
Tem 4 5
p [º 35 4 00 00 ]
C] 30
3
2 00 00 / m²
25 1 00 Irr [W
20 0
Figure 1. Measured & fitted performance surface, poly-Si.
Construction of Solar Radiation Database for European subcontinent
A brief description of the approach used in constructing the solar radiation database within PVGIS
follows. The computational approach is based on a solar radiation model r.sun, and the spline
interpolation techniques s.surf.rst and s.vol.rst that are implemented within the open-source GIS
software GRASS [3]. The r.sun model algorithm uses the equations published in the European Solar
Radiation Atlas [4]. The model estimates beam, diffuse and reflected components of the clear-sky and
real-sky global irradiance/irradiation on horizontal and inclined surfaces. The total daily irradiation
(Wh/m2) is computed by the integration of the irradiance values (W/m2) that are calculated at a time
step of 15 minutes from sunrise to sunset.
The necessary input components for calculating average monthly and yearly irradiation were:
• Monthly values of the Linke atmospheric turbidity;
• Digital elevation model (DEM);
• Monthly averages of daily sums of global irradiation available for meteorological stations in the
region;
• Monthly averages of the ratio of diffuse to global irradiation at the same number of the ground
stations (for irradiation on inclined planes).
The data from 566 meteorological stations are monthly averages of measurements over the period
1981-1990.
Figure 2 shows the solar irradiation map for the European subcontinent based on these monthly
averages.

3. Figure 2. Solar irradiation map for the European subcontinent based on monthly averages.
Outdoor Verification Measurements
Long term outdoor measurements are essential for Energy Rating studies for the following reasons:
• Predictions based on indoor laboratory measurements regarding energy performance need to be
validated with real field data.
• Outdoor measurements may reveal interesting behaviour discrepancies between the different
material technologies that are not evident from indoor flash measurements.
• They provide information about how module performance is affected by environmental conditions
such as cloudy skies resulting in a large portion of diffuse light, a continuous change of the incident
solar angle and gusts of wind and precipitation.
The test rack used for the long term outdoor measurements of the poly-Si and mono-Si modules is
shown in Figure 3.
Figure 3. Long term outdoor measurements of poly- and mono-Si modules
Sample results for poly-Si module
The measured energy and comparisons to energy predictions for the poly-Si module for the year 2003
are shown in Table I. The reference irradiance sensor used was a pyranometer mounted in the plane of
the module, and the energy predictions are made by integrating the instantaneous power values. The
first estimate is calculated using the measured module temperature, while the second estimate is based
on a calculated module temperature obtained from the module Nominal Operating Cell Temperature

4. (NOCT) value and the irradiance and ambient temperature. That this calculation does not introduce
unacceptable errors is important since for the application using the PVGIS database, the ambient
temperature will be used in the same way. The final error in the estimate over the whole year is seen
to be little over 1 %, which is very acceptable.
Annual energy [W·h] Relative error [%]
Measured value 70345 —
Prediction: Pyran 70740 0.53
Prediction: Pyran & NOCT 71162 1.16
Table I. Total measured energy and predictions for poly-Si module in 2003.
At this point our aim is to check if we can use monthly averages and still obtain good predictions.
Following the same procedure as in the previous calculations, but in this case using monthly averages
of the measured irradiances and ambient temperatures, the energy production for the same poly-Si
module in 2003 has been predicted. Using the simplification that for every day in a single month the
energy is the same, daily variations at in-plane irradiance and ambient temperature were calculated
using the same procedure as usually employed in the PVGIS. The energy predictions for the poly-Si
module for every month in 2003, as well as the measured and predicted values using the irradiance
given by the pyranometer, are shown in Table II.
Measured energy Estimate Relative error Estimate Relative error
Month
[W·h] PV-GIS [W·h] [%] Pyran [W·h] [%]
Jan 4500 4518 0.4 4648 3.3
Feb 6007 5545 -7.7 6214 3.4
Mar 7831 6958 -11.1 8018 2.4
Apr 6751 5941 -12.0 6854 1.5
May 8659 7427 -14.2 8593 -0.8
Jun 7006 7750 10.6 6925 -1.2
Jul 8202 7671 -6.5 8136 -0.8
Aug 7471 7694 3.0 7354 -1.6
Sep 5554 6562 18.1 5513 -0.7
Oct 3546 4776 34.7 3553 0.2
Nov 1328 1429 7.7 1339 0.8
Dec 3513 3920 11.6 3594 2.3
TOTAL 70367 70195 -0.2 70740 0.5
Table II. Comparison based on average monthly irradiance and ambient temperature data for poly-Si in 2003.
When it comes to evaluate the closeness of the estimates using monthly average irradiance and
ambient temperature values, i.e. the estimates calculated in the same way the predictions on the
PVGIS web site are carried out, we can see that the errors may be large for some months although the
total error for the whole year is small. We can therefore conclude that the PVGIS type prediction is
valid over a long period of time such as one year but less appropriate for single months.
[3] M. Neteler, H. Mitasova, Open Source GIS: A GRASS GIS Approach. Kluwer Academic Publishers, Boston, 2002.
[4] The European Solar Radiation Atlas, Vol. 2: Database and Exploitation Software, Paris, Les Presses de l'École des
Mines, 2000.