Intern’l Conference on the Performance of Renewable Energy Systems (PRES 2017) July 11, 2017 Oldenburg, Germany

1. Paper Identification Number 287 1
Abstract—The necessity of renewable energy assessment is
increasing as the world is shifting towards more greener
technologies as compared to fossil fuel sources. On a global scale,
solar and wind are the most potential renewable energy sources
until now. In order to assess the solar and wind potential at the
same location, data was measured via a meteorological tower at
the university of Oldenburg for one week. The data were
analyzed and a framework was developed to estimate the annual
energy yield density (kWh/m2/year). It was found that the solar
energy yield density was in the range of 90.8-114.2 kWh/m2/year
whereas for wind, the energy density was found to be 80.6
kWh/m2/year. In spite of the limitation of the data, this paper
provides an insight into making valid comparisons between two
potential renewable energy sources.
Index Terms—Wind potential, solar potential, Meteorological
data, energy density, long term data correlation.
I. INTRODUCTION
HE world is moving towards an transition from using
conventional fossil fuel to more cleaner and environment
friendly renewable sources e.g. wind, solar etc. Assessment of
these sources is important to select the suitable technology
within shortest possible time. The energy potential assessment
of different sources is not straightforward. In this paper, the
wind and solar energy potential has been assessed for the site
of the University of Oldenburg, Germany with help of an
installed meteorological tower. Short term data of relevant
parameters were collected for one week and later analyzed to
provide the energy density of the wind and solar resource over
a year. In the end, the energy density of wind and solar energy,
as an indicator of the potential, was compared and discussed to
highlight on the limitation of the deployed evaluation
techniques and scope of further development.
II. THEORETICAL BACKGROUND
A. Solar Energy Potential
Energy from the sun is reduced at the Earth’s surface
mainly due to the solar geometry and atmospheric processes.
Scattering and absorption in the atmosphere significantly
decreases the direct irradiance from the sun while the
scattering process results in diffuse irradiance. Global
Horizontal Irradiance (GHI) is the summation of direct and
diffuse irradiance, which effectively is incident on the surface
[1]. GHI can be described by,
cos ,zGHI DHI DNI θ= + (1)
where, DHI is the diffuse horizontal irradiance, DNI is
direct normal irradiance and Ɵz is the solar zenith angle.
In order to estimate the solar energy potential of any
location, GHI is the most important parameter, given that solar
energy would be utilized with photovoltaic applications. In
case of thermal applications, DNI would be the most suitable
parameter to analyze. GHI could be measured by a
pyranometer or a reference solar cell. The working principles
of both these sensors are different.
A pyranometer is made of a thermopile, generating voltage
due to the temperature difference in the absorptive surface and
the ambient. The generated voltage is calibrated against the
incoming irradiance. The pyranometer holds a flat response
over a wide range of wavelengths, which indicates that the
pyranometer is also sensitive to long wave radiation
(>3000 nm) [2]. Nevertheless, spectral range of pyranometer
lies between 300-2800 nm (short wavelength radiation) [3].
On the other hand, the solar cells do not have a flat response
over the whole spectrum. The short circuit current of a solar
cell is generally proportional to the irradiance [4] . However,
the solar cells have selective absorption due to the band gap of
the material of which it is made of. If the energy content in the
incoming photons do not have enough energy, these photons
with low energy (high wavelength) will not be absorbed by the
solar cell. The solar cells used in Photovoltaic (PV) modules
in the field would have a selective response to the radiation
spectrum. Therefore, to estimate the energy potential, it could
be a good idea to use solar cells instead of pyranometers,
despite of underestimating the GHI.
In order to find out the energy potential for PV applications,
high energy density (kWh/m2
) is an important criterion for
conducting a feasibility study. However, calculation of energy
potential for PV in a certain location is not straightforward.
Professional analysis tools (e.g. HOMER Pro) are available to
study the energy potential for PV applications [5]. It is
recommended to have at least one year of data to find out the
yearly energy potential [6]. In practice, hourly GHI data is
used for this purpose. However, higher resolution would
produce more accurate results. In this paper, we used equation
[7] to find out the PV power produced in a time step.
,
,
. .( ). 1 ( ) .T
pv pv pv P c c STC
T STC
G
P Y f T T
G
α = + −  (2)
Where, Ypv, is the rated capacity of the PV array, meaning its
power output under standard test conditions [kWp]; fpv is the
PV derating factor (%) which is usually a constant ;ḠT is the
solar radiation incident on the PV array in the current time
step [kW/m2]; ḠT,TSC is the incident radiation at standard test
conditions [1 kW/m2
]; αp is the temperature coefficient of
power [%/°C] Tc, is the PV cell temperature in the current
Comparison of Solar and Wind Energy Potential
at University of Oldenburg, Germany
Dipta Majumder and Ahmed Altaif
Postgraduate Programme Renewable Energy, University of Oldenburg, Germany
T

2. Paper Identification Number 287 2
time step [°C] And Tc,STC is the PV cell temperature under
standard test conditions [25 °C]. This equation was used in
different time steps and then integrated to find out the energy
yield. If the areas of the PV cells required at the ground level
are known (without considering the shading effect), the energy
density could be calculated from the energy yield. Effect of
tilted surface on the incident solar irradiation incident (ḠT)
could be estimated by HDKR model. The HDKR model uses
an algorithm to find out the beam and diffuse horizontal
irradiance from global horizontal irradiance values and
clearness index. Effect of tilted surface on the incident solar
irradiation incident (ḠT) is given by equation 3 [8],
1+cosβ β 1-cosβ3G =(G +G A )R +G (1-A )( ) 1+fsin +Gρ ( )T b d di b i g
2 2 2
  
  
   (3)
Where, Ḡb and Ḡd are beam and diffuse irradiance, β is the
slope of surface, Rb is ground albedo, Ai is anisotropy index
characterizing circumsolar diffuse radiation and f is factor for
horizon brightening.
B. Wind Energy Potential assessment
Like Solar Energy, wind energy potential assessment is also
important before implementing projects. The wind speed is a
varying parameter which makes the wind energy an
intermittent source like solar. Theoretically, the maximum
extractable power from the wind that flows at a speed v
through an area A is given by,
3
,
1
.
2
Betz p BetzP Av cρ= (4)
The maximum extractable power is proportional to the air
density ρ, the cross-sectional area A (perpendicular to wind
speed) and the third power of the wind speed v, and the
maximum power coefficient, cP.Betz (0.59), which is sometimes
called the Betz limit). Real power coefficients cP are lower
than the Betz limit. For drag force driven rotors, cP is below
0.2, and for lift force driven rotors with good airfoil profiles cP
reach up to 0.5 [9]. The wind speed varies with the height
above ground, known as vertical wind profile (also called
wind shear or height profile), whereas all the parameters
except the wind speed in equation (4) are rather known. The
wind profile is influenced by the surface roughness, the nature
of the terrain, the topography and the thermal stratification.
The wind profile can be described by the Hellmann power law
[9]
2 2
1 1
( )
.
( )
v Z Z
v Z Z
α
 
=  
 
(5)
Where, v(Z1) and v(Z2) are the wind speeds in height Z1 and Z2
respectively. α is an exponent whose value depends on the
speed at the reference height, on the atmospheric stability and
the surface roughness. Typical values for α are 0.1 for open
water, 0.143 for Open land flat surface and 0.25 for forest (the
reference height is 2/3 of the trees height).
In most cases, the measured frequency distribution of varying
wind speeds can be expressed by a Weibull distribution
function, characterized by two parameters A and k. The
scaling factor A is a measure for the characteristic wind speed
of the considered time series. The shape factor k determines
the curve shape and is characterized by wind climate. Given
the Weibull factors, an estimation of the mean wind speed ū
can be done by equation (6),
1
(1 ).u A
k
= Γ + (6)
Where, Γ is the gamma function. Notably, A and k change
with the height above ground. If the Weibull scale parameter
A1 and shape parameter k1 is known at any reference height Z1,
then the values for the A2 and k2 at any other height Z2 are
estimated by equation (7) and (8), with an uncertainty of 2%
for reasonable height e.g. 3 ≤ Z≤ 100 m [10].
2 2
1 1
.
A Z
A Z
α
 
=  
 
(7)
( )12
1 2
1 0.0881ln /10
.
1 0.0881ln( /10)
Zk
k Z
− 
=  
− 
(8)
With the characterization of wind speeds at different heights
and conditions, a prediction of the energy yield is possible.
Using the measured wind speed and the derived histograms,
distribution functions, and the power curve of a wind turbine,
the energy can be calculated. For a wind speed histogram with
a given wind speed class width (bin), the relative frequency hi
= ti /T of the individual wind speed class vi follows from its
temporal share ti of the considered time period T i.e. the
normalized histogram. If now the power curve P(v) of the
wind turbine is discretized with the same wind speed class
width, the energy yield Ei contributed by the individual wind
speed class is,
i i iE t P= ⋅ (9)
Summing up the individual energy yield of all the classes
gives the total energy yield Etotal in the considered time period.
total i i iE E T h P= = ⋅ ⋅∑ ∑ (10)
The wind regime is described by a Weibull distribution
function hW(v). The same procedure can be applied with
histograms of wind speeds to estimate the energy yield. In
order to collect wind speed values, international standards
refer to the cup anemometer as the most suitable sensor type
for wind speed measurements. If a cup anemometer is used, it
is necessary to determine the wind direction using a wind
vane. In most cases, long term wind speed data (minimum 20
years) is not available, although it is important to know the
variations of the mean annual wind speed from year to year. If

3. Paper Identification Number 287 3
a long-term time series of wind speed and direction is
available for at least one nearby reference site, long term data
could be predicted by correlation between the reference site
and the site under consideration. There are few methods to
correlate the measurement; one of them is the Distribution
Scaling Method given by [9] ,
.ref long site long
ref short site short
ε ε
ε ε
− −
− −
= (11)
Where, ε is a parameter of wind conditions derived either from
long or short-term data and ‘ref’ refers to the reference site and
site to the location to be calculated.
III. METHODOLOGY
The meteorological tower is on the Wechloy campus at the
university of Oldenburg (Longitude 8.17°E and Latitude
53.15°N). The tower is closely surrounded by tall trees
surpassing the height of the tower at the northern and western
directions and some buildings at 50m distance in the eastern
direction. The sensors are wired to a switching cabinet with a
MODAS data logger (Model: 1217) which is located at the
bottom of the tower- approximately 2m above ground level.
Typically, there are three heights with installed sensors: 2m,
16m and 18m. Relevant parameters for estimating the energy
yield were taken into consideration. Associated sensor details
are listed in Table I.
TABLE I
SENSORS FOR RELEVANT DATA COLLECTION
Location Physical
quantity
Sensor Manufacturer (Model)
1 m Temperatur
e
Pt100 -
2 m
Wind Speed
Cup-
Anemometer
THIES(Classic
4.3303.21.000 )
16 m Temperatur
e
Radiation
Radiation
Pt100
Pyranometer
PV-cell
-
Kipp & Zonen (CM-11)
18 m Temperatur
e
Wind Speed
Wind
Direction
Pt100
Cup-
Anemometer
Wind vane
THIES(Classic
4.3303.21.000)
THIES (4.3303.21.000)
A. Evaluation method for solar energy yield
In order to find out the energy potential from PV, the
software HOMER Pro (version 3.3.1) was used as the
evaluation tool. GHI values were collected from 04.05.2017 to
10.05.2017. The recorded GHI values were given as input to
the HOMER Pro, to assess the energy potential within this
time span (total 149 hours). Energy estimated by HOMER Pro
was ultimately used to find out the energy potential kWh) per
square meter of area. In addition to that, PV of 1kWp of
15.6% efficiency and temperature coefficient of power as -
0.45%/0
C [11] were taken as reference input to HOMER Pro.
HOMER Pro uses equation (2) to find out the array power
output and ultimately the energy yield. Derating factor of
73.1% was found from grid connected systems in U.S.A [12].
However, default derating factor of 80% in HOMER Pro was
used which does not take temperature derating into
consideration. Temperature derating was modelled using the
temperature coefficient of power. PV cell temperatures were
simulated by HOMER Pro using the average temperature
conditions [13] ; derived from the NASA atmospheric science
data center. Estimated energy yield was then linearly
extrapolated to one year. However, to capture the seasonal
variation of the GHI, further processing was done. In addition,
ground reflectance Rb of 0.2, and 0º and 30º tilt of panels were
used for the simulation. Remaining parameters are calculated
internally be HOMER Pro.
With a view to finding out the yearly energy density,
measured values of GHI from the solar meteorology group at
the university of Oldenburg (location: Longitude 8.180
E and
Latitude 53.150
N) was collected for the same time span as us.
The values measured by the university were compared with
our measurement. Due to good correlation (correlation
coefficient of 0.94), the university data set of 2016 was used to
estimate hourly series of 8760 values of GHI at the used
meteorological tower. Based on the estimated hourly series of
GHI at the meteorological tower, energy yield could be
estimated for a whole year. This could give an indication on
the energy potential for solar PV application.
B. Evaluation method for wind energy yield
In order to verify the performance of the wind measurement
station and discard erroneous data, the time series recorded by
the data logger have been reviewed. This review is based on
checking data integrity and plausibility, extreme outlier values
and anomalies, completeness, range and continuous constant
value test. The provided data set has been checked and
reviewed based on the mentioned criteria using windPRO, a
professional wind data evaluation tool.
The windPRO software was used to generate the wind
characteristics such as wind rose and Weibull parameters
(scale factor A and shape factor k) for twelve direction sectors
which allows comparing the wind speed at different sectors
and heights. Extrapolation of Weibull parameters from one
height to another was done by using the equations (7) and (8)
for twelve sectors (which is a common practice of wind farms
planning in wind industry). The long-term scaling method is
used to correlate the one-week measured data from the tower
at 18m height to five-year wind data from another tower at
university of Oldenburg (32m height on top of Wechloy
campus library). The Distribution Scaling Method equation
(18) was used to generate the long-term Weibull parameters
for measurement at the Wechloy tower by knowing the short-
term Weibull parameters of the measurement at the wechloy
meteorological tower and long-term and short-term Weibull
parameters (reference data) of university of Oldenburg data.
First, short-term Wechloy meteorological tower measurements
(10-min averages) was obtained for one week. Then long-term
reference of university of Oldenburg (5 years, 30-min
averages) was converted to 10-min-resolution by replicating

4. Paper Identification Number 287 4
the same values 3 times. Afterwards, the overlapping
measurement period of the reference data was used as a short-
term reference measurement, which leads finally to obtaining
the long-term data sets.
Looking at the measured data at 18m height, the maximum
wind speed is 3.9 ms-1
. This wind data is not high enough to
calculate and compare the energy yield of different methods
using a turbine power curve method since the typical cut-in
wind speed for the most wind turbines is in the range 3 to 4
m/s. Thus this methodology was excluded and the power
coefficient method equation (4) was used instead.
Different methods were used to calculate the energy yield for
one week using the short-term measured data. The histogram
method equation (10) was used considering different bin-
width of 0.25, 0.5 and 1 m/s. The energy yield by Weibull
method considered first the mean values of Weibull
parameters, second sectorized values of the parameters. The
same procedures were used to estimate the energy yield for
one year using direct extrapolated one-week data and
correlated data.
IV. RESULTS AN DISCUSSION
A. Solar Energy Assessment
Collected data was checked by HOMER Pro to assess the
completeness of data. No unusual values were obtained. GHI
values for the time span were plotted with minimum and
maximum GHI (see Fig. 1). Maximum mean irradiation was
found to be 894.8 Wm-2
, whereas maximum value of GHI was
recorded at 1129.3 Wm-2
. The variation of GHI over the
averaging period could be observed from the maximum and
minimum values.
Figure 1: Mean, maximum and minimum GHI.
The values measured by the solar meteorology group of
University of Oldenburg were comparable to what we
observed. A linear fitting was performed to find out the
relationship (see Fig. 2).
Figure 2: Comparison of different data sets.
It is noticeable from Fig. 2 that the fitting is good at lower
values of GHI compared to higher GHI. The linear relation
between the two measurements is as below:
( )_ 0.8089 _ 0.0016GHI wechloy GHI uol= × −
Where, GHI_uol>0
This equation was used later to estimate the hourly values of
GHI at the Wechloy campus. The correlation coefficient was
found 0.94. As a first step of analysis, the collected values of
GHI were given as input to the HOMER Pro. All the other
parameters were used from the standard HOMER Pro library.
Subsequently, the area of the PV cell was calculated to find
out the energy density. In addition, linear extrapolation was
done for the whole year, the findings are as given in Table II.
TABLE II. WEEKLY SOLAR ENERGY YIELD
ESTIMATION
Aspect Weekly energy
potential (kWh/m2
)
Yearly energy density
(linear extrapolation
(kWh/m2
)
Value 2.2 132.9
Uncertainty ±0.01 ±0.58
It can be argued here that linear extrapolation would not be an
accurate approximation around the year due to the seasonal
variation.
In order to include the seasonal variability, yearly time
series of GHI was obtained for the meteorological tower under
investigation with the help of year-round measured data
(2016) at the university of Oldenburg and the equation (2).
However, it was noticed that there was some missing data
(from 30 June to 15 July, 2016 and from 16 September, 2016
to 5 October, 2016). In order to make them usable, all the
values in these time series were kept constant to the available
nearby day.
An hourly time series of GHI was built which was then
integrated with the HOMER Pro model to find out the yearly
energy yield from 1kWp solar PV. The findings are listed in
Table III.

5. Paper Identification Number 287 5
TABLE III. YEARLY SOLAR ENERGY YIELD ESTIMATION
PV slope PV area (m2
) Energy density
(kWh/m2
)
Uncertainty
00
6.4 90.8 ±0.08
300
7.4 114.2 ±0.06
The uncertainties mentioned in Table III are only from the
propagated uncertainties through measurement. It seems that
the energy density is higher when the panels are kept as sloped
rather than flat since more panels could be installed within
same area and the panels are better aligned with the sun
position. If the energy density values in Table III are
compared with values mentioned in Table II, it was observed
that the energy density throughout the year is less by
approximately 14-32%. Hence it can be concluded safely, that
for assessment of energy potential, it is important to find
measured data of solar irradiance for at least a year to observe
the seasonal variation.
B. Wind Energy Assessment
The collected data was analyzed quantitatively and
qualitatively. No missing data or duplications were found.
Figure 3: Wind rose at 18m height for the collected data
During the analysis, the wind rose was drawn (Fig. 3) which
shows the mean wind speed in the individual wind direction
sectors. The highest mean wind speeds are observed in the
sector East-North-East, while the Eastern wind is the smallest
in contrast; the South-South-West sector shows a clear
dominance of the wind directions. The energy estimation of a
wind turbine or a wind farm is in general based on the
sectorized wind data, with usually 12 sectors [9]. The
correlation between reference site and planning site must be
performed sector by sector: The distribution factors obtained
for one individual wind direction sector of the wind rose at the
reference site on top of Wechloy campus library are
transferred by the correlation to the corresponding sector of
the planning site to achieve its individual distribution factors.
Table IV shows the Weibull parameters for 5 years correlated
data from one-week Weibull parameters of the measurement.
TABLE IV: WECHLOY METEOROLOGIVAL TOWER WEIBULL
PARAMETERS FOR LONG-TERM (5 YEARS) CORRELATED AT 18M
HEIGHT
Sector
A
parameter
k
parameter
frequency
Mean
wind
speed
0-N 1.45 4.06 9.57 1.31
1-NNE 1.09 2.98 15.46 0.98
2-ENE 2.45 4.29 10.12 2.23
3-E 0.00 0.00 0.33 0.00
4-ESE 0.00 0.00 0.33 0.00
5-SSE 1.30 2.52 6.45 1.16
6-S 0.00 0.00 13.35 0.00
7-SSW 5.35 4.31 23.92 4.87
8-WSW 2.45 2.32 8.12 2.17
9-W 2.33 2.10 3.34 2.06
10-WNW 1.71 2.94 3.34 1.52
11-NNW 1.57 1.94 5.67 1.39
Mean 1.64 2.29 100.00 2.13
The energy yield for 1 week is estimated based on the 1 week
measurement from 18 m height cup anemometer. The
histogram method with three different bin-width (0.25, 0.5 and
1 m/s) shows different weekly energy yields as shown in
Table V. The 0.5 and 1 m/s bin-widths are overestimating the
energy yield; this is due to the small range of the wind speed
(0 to 3.9 m/s). On the other hand, the 0.25 m/s bin-width
shows a good agreement to Weibull methods which is due to
the small range of the measured wind speeds (0 to 3.9 m/s).
TABLE V: ENERGY YIELD (kWh/m2
) FOR 1 WEEK MEASUREMENTS
AT 18M HEIGHT USING DIFFERENT METHODS
Histogram
(Bin=1)
Histogram
(Bin=0.5)
Histogram
(Bin=0.25)
Weibull
0.34 ± 0.03 0.22 ± 0.02 0.18 ± 0.02 0.16 ± 0.02
For the yearly energy yield, Weibull fitting for every
individual sector was used to compare the energy yields for
one year using correlated mean values of Weibull parameters;
correlated sector-wise of Weibull parameters and direct
extrapolation of the weekly energy yield. The results show
huge differences between the three methods as shown in Table
V. Since the wind is fluctuating diurnally, synoptically and
seasonally, using the extrapolation will lead to unrealistic
energy yield. The mean (weighted averaged) Weibull
parameters correlation method which disregards the weighing
(frequency) of the sectorized Weibull parameters will also
lead inaccurate estimation of the energy yield. Thus, the
sector-wise Weibull method is comparatively the accurate
method to the energy yield estimation compared to the other
methods. The energy yield for one-year correlated data is
shown in Table VI.
TABLE VI: ENERGY YIELD (kWh/m2
) FOR 1 YEAR CORRELATED
DATA AT 18M HEIGHT USING DIFFERENT METHODS
Extrapolated
(1 week to 1 year)
Correlated
(mean values)
Correlated (sector-wise)
9.08 ± 0.82 19.50 ± 1.75 80.62 ± 7.26
Higher annual energy yield was observed in case of solar
compared to the wind energy potential. However, the use of
only one week of data and only one height makes the

6. Paper Identification Number 287 6
comparison difficult. It is also noticeable that the uncertainty
in the wind is higher compared to solar. The uncertainty in the
wind speed measurement was taken into consideration and it
was assumed that the uncertainty in the estimated wind power
would be three times higher.
In order to estimate the energy yield by a real wind turbine,
energy yield was also calculated for a 2-kW wind turbine with
3 m/s cut-in wind speed and 3.6 m rotor diameter
(Manufacturer: ReDriven, retrieved from RETScreen) [14]
using a turbine power curve method. The turbine power curve
has 1 m/s bin-width. Hence, the estimated energy by the
histogram was also based on the same bin-width. The annual
energy yield was found to be 30.38 (±2.73) kWh/m2
. The
annual energy yield for a real wind turbine was found
approximately 62% lower than the estimated wind energy
potential discussed before. It can be concluded that the
location of the turbine is not ideal, and the turbulence caused
by the surrounding structures has a larger effect on the turbine
power production.
V. CONCLUSION
To summarize, annual solar energy density (kWh/m2
) at the
Wechloy campus was found to be in the range of 90.8-114.2
kWh/m2
/year whereas for wind the energy density was found
80.6 kWh/m2
/year at 18m height. The annual energy yield for
a small 2 kW turbine was found to be 30.38 kWh/m2
.
Although the solar energy density is higher, the evaluation
data was taken for only one week which posed limitations in
estimating for the whole year. In addition, the position of the
meteorological tower was not ideal. Surrounding trees which
surpassed the height of the tower at the northern and western
directions affected the measurements. This limitation makes it
difficult to compare the results from different approaches
taken during the analysis. However, the methodology could
be used to provide an idea of the annual energy yield density.
REFERENCES
[1] NREL. Glossary of Solar Radiation Resource Terms
n.d. http://rredc.nrel.gov/solar/glossary/gloss_g.html
(accessed June 8, 2017).
[2] PPRE stuff. Performance of Renewable Energy
Systems. Suse2017. 2017.
[3] Instruction Manual CM11 CM14. 2000.
[4] Salim MS, Najim JM, Salih, Mohammed S. Practical
Evaluation of Solar Irradiance Effect on PV
Performance. Energy Sci Technol 2013;6:36–40.
doi:10.3968/j.est.1923847920130602.2671.
[5] Photovoltaic Softwares.com. PROFESSIONAL
PHOTOVOLTAIC SOFTWARES To download n.d.
http://photovoltaic-software.com/professional.php
(accessed July 8, 2017).
[6] World Meteorological Organization. Guide to
Meteorological Instruments and Methods of
Observation. 2014.
[7] Homer LLC. How HOMER calculates the PV array
power output n.d.
http://usersupport.homerenergy.com/customer/en/port
al/articles/2186875-how-homer-calculates-the-pv-
array-power-output (accessed June 8, 2017).
[8] Homer LLC. How HOMER calculates the radiation
incident on the PV array 2015.
http://usersupport.homerenergy.com/customer/en/port
al/articles/2186872-how-homer-calculates-the-
radiation-incident-on-the-pv-array (accessed June 8,
2017).
[9] Gasch R. WInd Power Plants-Fundamentals, Design,
Construction and Operation. Springer-Verilog; 2012.
[10] C.G. J, A. M. Height variation of wind speed and wind
distributions statistics. Geophys Res Lett 3 1976. doi::
doi: 10.1029/GL003i005p00261. issn: 0094-8276.
[11] Yingli Green Energy Holding Company Limited.
YGE 60 Cell 40mm SERIES 2014.
http://www.yinglisolar.com/assets/uploads/products/d
ownloads/YGE_60_Cell_Series_EN.pdf (accessed
July 8, 2017).
[12] Marion B, Adelste J, Boyle K, Hayden H, Hammond
B, Fletcher T, et al. Performance Parameters for Grid-
Connected PV Systems. 31 st IEEE Photovoltaics
Spec. Conf. Exhib., 2005.
[13] Homer LLC. How HOMER Calculates the PV Cell
Temperature 2017.
http://www.homerenergy.com/support/docs/3.9/how_h
omer_calculates_the_pv_cell_temperature.html
(accessed July 8, 2017).
[14] Natural Resources Canada. RETScreen 2017.
https://www.nrcan.gc.ca/energy/software-tools/7465
(accessed July 8, 2017).