Firstly, a longer period of measured data (more data) can be used to determine the coefficients of the angle-specific model. The more the data, the more features of the meteorological conditions of the site can be determined. The features determined in the short period cannot represent as the only features of years. Therefore, more data will provide more information about the site's meteorological conditions, which will lead to a better prediction.
Secondly, a smaller step size of the cosine sun incident angle intervals can also be considered for future work. A smaller step size can show the system's characteristics in more detail as the system's characteristics are different (although the different is small) when the sun's position has a minor movement. Thereby, a smaller step size improves the accuracy of the angle- specific Ross coefficient model.
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Lai, K. Y. & Lim, B.H., 2020. Comparative Study for Time-specific Ross Coefficient and Overall Ross Coefficient for Estimation of Photovoltaic Module Temperature. In: 2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies (CSUDET) pp.251-256.
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[Accessed 14 April 2022].
Niclas, n.d. Standard Test Conditions (STC): definition and problems. [Online]
Available at: <https://sinovoltaics.com/learning-center/quality/standard-test- conditions-stc-definition-and-problems/> [Accessed 20 August 2021].
Nykmap, DQ, n.d. Spherical coordinates. [Online] Available at:
<https://mathinsight.org/spherical_coordinates> [Accessed 25 August 2021].
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https://doi.org/10.1016/j.egypro.2014.12.433
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[Accessed 30 August 2021].
Ross, R. (1976). Interface design considerations for terrestrial solar cell modules. In: Photovoltaic Specialists Conference, 12th, Baton Rouge, La., November 15-18, 1976, Conference Record. (A78-10902 01-44) New York, Institute of Electrical and Electronics Engineers, Inc. pp. 801-806
Ross, R.G. and M.I. Smokler, 1986. Flat-plate solar 29 array project final report-Vol. VI, Engineering Engineering sciences and reliability. In: JPL Publication, 86-31 NASA, Springfield, VA.
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<https://saltspringrealestateagent.com/does-the-home-get-sunlight/ >
[Accessed 14 April 2022].
Skoplaki, E., Boudouvis, A. G., and Palyvos, J. A. Ã. (2008). A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting. Solar Energy Materials & Solar Cells, [e-journal] 92 (11), pp. 1393- 1402. https://doi.org/10.1016/j.solmat.2008.05.016
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APPENDICES
Appendix A: Coding for Calculating Sun’s Position and Cosine Sun Incident Angle
import math as m import pandas as pd import datetime
from datetime import datetime, timedelta
def datetime_range(start, end, delta): # use to calculate the time current = start
while current < end:
yield current current += delta
def create_date (): # use to calculate the date
global table_Azimuth global table_Altitude global table_cosdelta
date =pd.date_range(start="2019-01-01",end="2019-12-31") table_Azimuth=pd.DataFrame(index=range(365))
table_Azimuth['date'] = date
table_Altitude=pd.DataFrame(index=range(365)) table_Altitude['date'] = date
table_cosdelta=pd.DataFrame(index=range(365)) table_cosdelta['date'] = date
def calculation():
N = 1 d=[]
e=[]
z=[]
for i in range (365):
latitude = 3.0408 longtitude = 101.7943 GMT= 120
D = 0
beta = m.radians (10) facing_angle = m.radians (0)
# claculate the declination angle
sindelta =0.39795 * m.cos (m.radians(0.98563*(N-173))) delta = m.asin(sindelta)
delta = m.degrees (delta)
# calculate Longtitude Correction
LC = (GMT- longtitude)/15
#Calculate x
x = 360*(N-1)/365.242
#Calculate Equation of Time
x = m.radians(x)
EOT = 0.258* m.cos(x) - 7.416* m.sin(x) - 3.648* m.cos(2*x) - 9.228 * m.sin(2*x)
# claculate solar time ts = LCT + EOT/60 - LC - D
#calculate hour angle w = 15 * (ts - 12)
# calculate altitude angle
delta = m.radians(delta) latitude = m.radians(latitude) w = m.radians(w)
a = m.asin(m.sin(delta)* m.sin(latitude)+ m.cos(delta)*m.cos(w)*m.cos(latitude)) a = m.degrees(a)
#Calculate Azimuth angle a_radians = m.radians(a)
cosA= (m.sin(delta)* m.cos(latitude) - m.cos(delta) * m.cos(w)* m.sin(latitude)) / m.cos(a_radians)
b = -1* m.cos(delta) * m. sin(w)/ m.cos(a_radians)
if cosA > 0:
A = m.asin(b)
A = "{:.2f}".format(A) A = float (A)
else:
A = 180 - m.degrees (m.asin(b)) A = "{:.2f}".format(A)
A = float (A)
if a < 0: # no sun when less a less than 0 a =float ("NaN")
A = float ("NaN") a = "{:.2f}".format(a) a = float (a)
A_radians = m.radians(A)
if a == "NaN" and A =="NaN":
cos_delta = float ("NaN") else:
cos_delta= m.cos(beta) *m.sin (a_radians)+ m.cos(a_radians)*m.sin(beta)*m.cos (facing_angle- A_radians)
cos_delta= "{:.2f}".format(cos_delta)
N += 1
d.append(cos_delta) e.append(a)
z.append(A) table_cosdelta[j] = d table_Altitude[j] = e
table_Azimuth[j] = z
global LCT global j global time global g
create_date()
time = [dt.strftime('%H:%M ') for dt in
datetime_range(datetime(2021, 1, 1, 0), datetime(2021,1,1,23,59), timedelta(minutes=1))]
for j in time:
LCT = j
(hour,minute)= LCT.split(":")
LCT = int (hour) + int (minute)/60 # convert time to decimal calculation()
table_Altitude.to_csv(r'Altitude2019.csv', index=None, header=True) table_Azimuth.to_csv(r'Azimuth2019.csv', index=None, header=True) table_cosdelta.to_csv(r'cosdelta2019.csv', index=None, header=True)
Appendix B: Coding for Retrieving and Processing the Data from 21st January 2020 to 2nd February 2020 and Compiling All the Interested Data
import math as m import pandas as pd import datetime
from datetime import datetime, timedelta
def datetime_range(start, end, delta): # use to calculate the time current = start
while current < end:
yield current current += delta
time = [dt.strftime('%H:%M ') for dt in
datetime_range(datetime(2021, 1, 1, 7), datetime(2021,1,1,20), timedelta(minutes=1))]
a = pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\UTAR 2020 Weather Station Data compiled.xlsm', sheet_name = 'Raw data')
b = pd.read_csv(r'C:\Users\Asus\Desktop\Y4S3\Project\Coding\cosdelta.csv') c= pd.read_csv(r'C:\Users\Asus\Desktop\Y4S3\Project\Coding\altitude.csv')
# the needed data for the date 20200121
df =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 1.xlsx')
# start from 1705 row to 13400 and skip 15 row after retreived one data, 2:7:4 --> to obtain the data and the module temperature
temperature1 = df.iloc[1000:12700:15,2:7:4] # 47 mins time shift temperature1 = temperature1.reset_index()
temperature1 = temperature1.iloc[0:,1:]
solarirradiance = a. iloc[29213:29993, 12] # 28800 (20*60*24) +420 -7 = 29213 solarirradiance = solarirradiance.reset_index()
solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[29213:29993, 7] # 20-7 = 13 ; 13 *60 = 780; 780+29213 = 29993 windspeed = windspeed.reset_index()
windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[29213:29993,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[20, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[20, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature1['ambient temperature'] = ambienttemperature temperature1['solar irradiance'] = solarirradiance
temperature1['wind speed'] = windspeed temperature1['cosinetheta'] = consinetheta temperature1 ['Solar Altitude Angle'] = alpha temperature1 ['Time'] = time
temperature1 = temperature1.loc[temperature1["Solar Altitude Angle"] > 0]
temperature1 = temperature1.reset_index() temperature1 = temperature1.iloc[0:,1:]
df2 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 2.xlsx')
temperature2 = df2.iloc[909:12609:15,2:7:4]
temperature2 = temperature2.reset_index() temperature2 = temperature2.iloc[0:,1:]
solarirradiance = a. iloc[30653:31433, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[30653:31433, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[30653:31433,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[21, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[21, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature2['ambient temperature'] = ambienttemperature temperature2['solar irradiance'] = solarirradiance
temperature2['wind speed'] = windspeed temperature2['cosinetheta'] = consinetheta temperature2 ['Solar Altitude Angle'] = alpha temperature2 ['Time'] = time
temperature2 = temperature2.loc[temperature2["Solar Altitude Angle"] > 0]
df3 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 3.xlsx')
temperature3 = df3.iloc[960:12660:15,2:7:4]
temperature3 = temperature3.reset_index() temperature3 = temperature3.iloc[0:,1:]
solarirradiance = a. iloc[32093:32873, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[32093:32873, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[32093:32873,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[22, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[22, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature3['ambient temperature'] = ambienttemperature temperature3['solar irradiance'] = solarirradiance
temperature3['wind speed'] = windspeed temperature3['cosinetheta'] = consinetheta temperature3 ['Solar Altitude Angle'] = alpha temperature3 ['Time'] = time
temperature3 = temperature3.loc[temperature3["Solar Altitude Angle"] > 0]
df4 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 4.xlsx')
temperature4 = df4.iloc[1040:12740:15,2:7:4]
temperature4 = temperature4.reset_index() temperature4 = temperature4.iloc[0:,1:]
solarirradiance = a. iloc[33533:34313, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[33533:34313, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[33533:34313,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[23, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[23, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature4['ambient temperature'] = ambienttemperature temperature4['solar irradiance'] = solarirradiance
temperature4['wind speed'] = windspeed temperature4['cosinetheta'] = consinetheta temperature4 ['Solar Altitude Angle'] = alpha temperature4 ['Time'] = time
temperature4 = temperature4.loc[temperature4["Solar Altitude Angle"] > 0]
df5 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 5.xlsx')
temperature5 = df5.iloc[1033:12733:15,2:7:4]
temperature5 = temperature5.reset_index() temperature5 = temperature5.iloc[0:,1:]
solarirradiance = a. iloc[34973:35753, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[34973:35753, 7]
windspeed = windspeed.reset_index()
windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[34973:35753,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[24, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[24, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature5['ambient temperature'] = ambienttemperature temperature5['solar irradiance'] = solarirradiance
temperature5 ['wind speed'] = windspeed temperature5 ['cosinetheta'] = consinetheta temperature5 ['Solar Altitude Angle'] = alpha temperature5 ['Time'] = time
temperature5 = temperature5.loc[temperature5["Solar Altitude Angle"] > 0]
df6 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 6.xlsx')
temperature6 = df6.iloc[1015:12715:15,2:7:4]
temperature6 = temperature6.reset_index() temperature6 = temperature6.iloc[0:,1:]
solarirradiance = a. iloc[36413:37193, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[36413:37193, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[36413:37193,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[25, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[25, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature6['ambient temperature'] = ambienttemperature temperature6['solar irradiance'] = solarirradiance
temperature6['wind speed'] = windspeed temperature6['cosinetheta'] = consinetheta temperature6 ['Solar Altitude Angle'] = alpha temperature6 ['Time'] = time
temperature6 = temperature6.loc[temperature6["Solar Altitude Angle"] > 0]
df7 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 7.xlsx')
temperature7 = df7.iloc[1004:12704:15,2:7:4]
temperature7 = temperature7.reset_index() temperature7 = temperature7.iloc[0:,1:]
solarirradiance = a. iloc[37853:38633, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[37853:38633, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[37853:38633,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[26, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[26, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature7['ambient temperature'] = ambienttemperature temperature7['solar irradiance'] = solarirradiance
temperature7['wind speed'] = windspeed temperature7['cosinetheta'] = consinetheta temperature7 ['Solar Altitude Angle'] = alpha temperature7 ['Time'] = time
temperature7 = temperature7.loc[temperature7["Solar Altitude Angle"] > 0]
df8 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 8.xlsx')
temperature8 = df8.iloc[1050:12750:15,2:7:4]
temperature8 = temperature8.reset_index() temperature8 = temperature8.iloc[0:,1:]
solarirradiance = a. iloc[39293:40073, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[39293:40073, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[39293:40073,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[27, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[27, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature8['ambient temperature'] = ambienttemperature temperature8['solar irradiance'] = solarirradiance
temperature8['wind speed'] = windspeed temperature8['cosinetheta'] = consinetheta temperature8 ['Solar Altitude Angle'] = alpha temperature8 ['Time'] = time
temperature8 = temperature8.loc[temperature8["Solar Altitude Angle"] > 0]
df9 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020012 9.xlsx')
temperature9 = df9.iloc[1070:12770:15,2:7:4]
temperature9 = temperature9.reset_index() temperature9 = temperature9.iloc[0:,1:]
solarirradiance = a. iloc[40733:41513, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[40733:41513, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[40733:41513,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[28, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[28, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature9['ambient temperature'] = ambienttemperature temperature9['solar irradiance'] = solarirradiance
temperature9['wind speed'] = windspeed temperature9['cosinetheta'] = consinetheta temperature9 ['Solar Altitude Angle'] = alpha temperature9 ['Time'] = time
temperature9 = temperature9.loc[temperature9["Solar Altitude Angle"] > 0]
df10 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020013 0.xlsx')
temperature10 = df10.iloc[1118:12818:15,2:7:4]
temperature10 = temperature10.reset_index() temperature10 = temperature10.iloc[0:,1:]
solarirradiance = a. iloc[42173:42953, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[42173:42953, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[42173:42953,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[29, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[29, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature10['ambient temperature'] = ambienttemperature temperature10['solar irradiance'] = solarirradiance
temperature10['wind speed'] = windspeed temperature10['cosinetheta'] = consinetheta temperature10 ['Solar Altitude Angle'] = alpha temperature10 ['Time'] = time
temperature10 = temperature10.loc[temperature10["Solar Altitude Angle"] > 0]
df11 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020013 1.xlsx')
temperature11 = df11.iloc[1182:12882:15,2:7:4]
temperature11 = temperature11.reset_index() temperature11 = temperature11.iloc[0:,1:]
solarirradiance = a. iloc[43613:44393, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[43613:44393, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[43613:44393,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[30, 421:1201]
consinetheta = consinetheta.reset_index()
consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[30, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature11['ambient temperature'] = ambienttemperature temperature11['solar irradiance'] = solarirradiance
temperature11['wind speed'] = windspeed temperature11['cosinetheta'] = consinetheta temperature11 ['Solar Altitude Angle'] = alpha temperature11 ['Time'] = time
temperature11 = temperature11.loc[temperature11["Solar Altitude Angle"] > 0]
df12 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020020 1.xlsx')
temperature12 = df12.iloc[648:12348:15,2:7:4]
temperature12 = temperature12.reset_index() temperature12 = temperature12.iloc[0:,1:]
solarirradiance = a. iloc[45053:45833, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[45053:45833, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[45053:45833,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[31, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[31, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature12['ambient temperature'] = ambienttemperature temperature12['solar irradiance'] = solarirradiance
temperature12['wind speed'] = windspeed temperature12['cosinetheta'] = consinetheta temperature12 ['Solar Altitude Angle'] = alpha temperature12 ['Time'] = time
temperature12 = temperature12.loc[temperature12["Solar Altitude Angle"] > 0]
df13 =
pd.read_excel(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200203\735\2020020 2.xlsx')
temperature13 = df13.iloc[1017:12717:15,2:7:4]
temperature13 = temperature13.reset_index() temperature13 = temperature13.iloc[0:,1:]
solarirradiance = a. iloc[46493:47273, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[46493:47273, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[46493:47273,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[32, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[32, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature13['ambient temperature'] = ambienttemperature temperature13['solar irradiance'] = solarirradiance
temperature13['wind speed'] = windspeed temperature13['cosinetheta'] = consinetheta temperature13 ['Solar Altitude Angle'] = alpha temperature13 ['Time'] = time
temperature13 = temperature13.loc[temperature13["Solar Altitude Angle"] > 0]
df14 =
pd.read_csv(r'C:\Users\Asus\Desktop\Y4S3\Project\Data\Arduino\20200224\735\LOGGER0 3.csv')
temperature14 = df14.iloc[1121:12821:15,2:7:4]
temperature14 = temperature14.reset_index() temperature14 = temperature14.iloc[0:,1:]
solarirradiance = a. iloc[62328:63113, 12]
solarirradiance = solarirradiance.reset_index() solarirradiance =solarirradiance.iloc[0:,1:]
windspeed= a.iloc[62328:63113, 7]
windspeed = windspeed.reset_index() windspeed = windspeed.iloc[0:,1:]
ambienttemperature = a.iloc[62328:63113,3]
ambienttemperature = ambienttemperature.reset_index() ambienttemperature = ambienttemperature.iloc[0:,1:]
consinetheta = b.iloc[43, 421:1201]
consinetheta = consinetheta.reset_index() consinetheta = consinetheta.iloc[0:,1:]
alpha = c.iloc[43, 421:1201]
alpha = alpha.reset_index() alpha = alpha.iloc[0:,1:]
temperature14['ambient temperature'] = ambienttemperature temperature14['solar irradiance'] = solarirradiance
temperature14['wind speed'] = windspeed temperature14['cosinetheta'] = consinetheta temperature14 ['Solar Altitude Angle'] = alpha temperature14 ['Time'] = time
temperature14 = temperature14.loc[temperature14["Solar Altitude Angle"] > 0]
mergeddata=
temperature1.append(temperature2).append(temperature3).append(temperature4).append(te mperature5).append(temperature6).append(temperature7).append(temperature8).append(tem perature9).append(temperature10).append(temperature11).append(temperature12).append(te mperature13).append(temperature14)
mergeddata = mergeddata.rename(columns = {'Temp 2': 'Module Temperature'})
mergeddata.to_csv(r'compiled data 4.csv', index=None, header=True)
Appendix C: Coding for Conventional Ross Coefficient Model
import math as m import pandas as pd
df =pd.read_csv(r'C:\Users\Asus\Desktop\Modified Data\2019\compiled data (2019).csv') rosscoefficient = pd.DataFrame()
rosscoefficient = df.iloc[0:,:4]
rosscoefficient['Different in Temperature']= rosscoefficient.iloc[0:,1]- rosscoefficient.iloc[0:,2]
rosscoefficient.to_csv(r'rosscoefficient(2019).csv', index=None, header=True)
Appendix D: Coding for Sandia Model
import math as m import pandas as pd
import matplotlib.pyplot as plt from scipy import stats import seaborn as sns
df = pd.read_csv(r'C:\Users\Asus\Desktop\Y4S3\Project\Coding\merged data.csv') temp= pd.DataFrame()
temp = df.iloc[0:,:5]
sandia = temp
for i in range (len(temp)):
if temp.iloc[i,1] == 0 or temp.iloc[i,1]== float("NaN"):
sandia = sandia.drop(i)
sandia['temperature different'] = (sandia.iloc[0:,4]-sandia.iloc[0:,3]) sandia= sandia.reset_index()
temp = sandia
for i in range (len(temp)):
if temp.iloc[i,6] < 0:
sandia = sandia.drop(i)
sandia= sandia.drop('index', axis =1)
sandia['ln(tmod-tamb/ solar irradiance)'] = (sandia.iloc[0:,4]-sandia.iloc[0:,3]) /sandia.iloc[0:,1]
sandia['ln(tmod-tamb/ solar irradiance)']=sandia['ln(tmod-tamb/ solar irradiance)'].apply(lambda x: float(m.log(x)))
sandia.to_csv(r'sandia.csv', index=None, header=True)
Appendix E: Coding for Faiman Model
import math as m import pandas as pd
import matplotlib.pyplot as plt from scipy import stats import seaborn as sns
df = pd.read_csv(r'C:\Users\Asus\Desktop\Y4S3\Project\Coding\compiled data 4.csv')
faiman = df.iloc[0:,:5]
faiman['Different in Temperature']= faiman.iloc[0:,1]- faiman.iloc[0:,2]
faiman['y-axis (G/ tmod-tamb)'] =faiman.iloc[0:,3] /faiman.iloc[0:,5]
# remove outlier '''
faiman = faiman.loc[faiman['y-axis (G/ tmod-tamb)'] < 3000]
faiman = faiman.loc[faiman['y-axis (G/ tmod-tamb)'] > -3000]
'''
faiman.to_csv(r'faiman(range 3000).csv', index=None, header=True)
Appendix F: Coding for Time-specific Ross Coefficient Model
import math as m import pandas as pd
from datetime import datetime, timedelta import matplotlib.pyplot as plt
from scipy import stats import seaborn as sns
df = pd.read_csv(r'C:\Users\Asus\Desktop\Modified Data\2019\compiled data (2019).csv')
timespecific = df.iloc[0:,:4]
timespecific['Different in Temperature']= timespecific.iloc[0:,1]- timespecific.iloc[0:,2]
timespecific['Time']= df.iloc[0:,6]
hour1 = timespecific.loc[timespecific['Time'] <="08:00"]
# From the compiled data retrieved all the data that collected after 8AM and assign it to temp temp = timespecific.loc[timespecific['Time'] > "08:00"]
# From temp retrived the data occur before 9AM hour2 = temp.loc[temp['Time'] <= "09:00"]
temp = timespecific.loc[timespecific['Time'] > "09:00"]
hour3= temp.loc[temp['Time'] <= "10:00"]
temp = timespecific.loc[timespecific['Time'] > "10:00"]
hour4 = temp.loc[temp['Time'] <= "11:00"]
temp = timespecific.loc[timespecific['Time'] > "11:00"]
hour5 = temp.loc[temp['Time'] <= "12:00"]
temp = timespecific.loc[timespecific['Time'] > "12:00"]
hour6 = temp.loc[temp['Time'] <= "13:00"]
temp = timespecific.loc[timespecific['Time'] > "13:00"]
hour7 = temp.loc[temp['Time'] <= "14:00"]
temp = timespecific.loc[timespecific['Time'] > "14:00"]
hour8 = temp.loc[temp['Time'] <= "15:00"]
temp = timespecific.loc[timespecific['Time'] > "15:00"]
hour9 = temp.loc[temp['Time'] <= "16:00"]
temp = timespecific.loc[timespecific['Time'] > "16:00"]
hour10 = temp.loc[temp['Time'] <= "17:00"]
temp = timespecific.loc[timespecific['Time'] > "17:00"]
hour11 = temp.loc[temp['Time'] <= "18:00"]
temp = timespecific.loc[timespecific['Time'] > "18:00"]
hour12 = temp.loc[temp['Time'] <= "19:00"]
hour13 = timespecific.loc[timespecific['Time'] > "19:00"]
hour1.to_csv(r'hour1(try3).csv', index=None, header=True) hour2.to_csv(r'hour2(try3).csv', index=None, header=True) hour3.to_csv(r'hour3(try3).csv', index=None, header=True) hour4.to_csv(r'hour4(try3).csv', index=None, header=True) hour5.to_csv(r'hour5(try3).csv', index=None, header=True) hour6.to_csv(r'hour6(try3).csv', index=None, header=True)