This is the demonstration how to use NEDO data process utility



In [1]:

    
%load_ext autoreload
%autoreload 2
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os

from pypvcell.solarcell import SQCell,MJCell,TransparentCell
from pypvcell.illumination import Illumination
from pypvcell.spectrum import Spectrum

from pypvcell.metpv_reader import NEDOLocation

from pvlib.location import Location
from pvlib.tracking import SingleAxisTracker
from pvlib.irradiance import total_irrad,aoi_projection



In [2]:

    
nedo_solar_file='hm51106year.csv'

Load the data into a `NEDOLocation` object



In [3]:

    
ngo_loc=NEDOLocation(nedo_solar_file)
df=ngo_loc.main_df



In [4]:

    
ngo_loc.main_df.head()









    Out[4]:







  
    
      
      measure_type
      month
      day
      wind_speed
      1
      2
      3
      4
      5
      6
      ...
      21
      22
      23
      24
      max
      min
      integral
      avg
      day_num
      year
    
  
  
    
      2016-01-01
      1
      1
      1
      17.9
      0
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      175
      NaN
      926.0
      NaN
      1
      2016
    
    
      2016-01-01
      2
      1
      1
      17.9
      0
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      118
      NaN
      500.0
      NaN
      1
      2016
    
    
      2016-01-01
      3
      1
      1
      17.9
      0
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      77
      NaN
      426.0
      NaN
      1
      2016
    
    
      2016-01-01
      4
      1
      1
      17.9
      0
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      10
      NaN
      52.0
      NaN
      1
      2016
    
    
      2016-01-01
      5
      1
      1
      17.9
      31
      21
      17
      12
      11
      17
      ...
      51
      41
      49
      49
      104
      11.0
      NaN
      48.0
      1
      2016
    
  

5 rows × 34 columns

main_df adds the column names into the raw data and convert it to a pandas.DataFrame object

Convert the NEDO data into a more useful form

Add column names into the raw data does not make the dataframe easier to use. nedo_data_reader can "unstack" the data, i.e., making each row an entry of particular time.



In [5]:

    
%%time
ngo_df=ngo_loc.extract_unstack_hour_data(norm=False)









    



CPU times: user 11.5 s, sys: 174 ms, total: 11.7 s
Wall time: 11.7 s



In [6]:

    
ngo_df.head()









    Out[6]:







  
    
      
      GHI
      DHI
      dHI
      sun_time
      temperature
      wind_dir
      wind_speed
      rain
      snow
      avg_time
    
    
      avg_time
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2016-01-01 00:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      31.0
      10.0
      47.0
      0.0
      0.0
      2016-01-01 00:30:00+09:00
    
    
      2016-01-01 01:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      21.0
      10.0
      37.0
      0.0
      0.0
      2016-01-01 01:30:00+09:00
    
    
      2016-01-01 02:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      17.0
      10.0
      20.0
      0.0
      0.0
      2016-01-01 02:30:00+09:00
    
    
      2016-01-01 03:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      12.0
      3.0
      19.0
      0.0
      0.0
      2016-01-01 03:30:00+09:00
    
    
      2016-01-01 04:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      11.0
      14.0
      24.0
      0.0
      0.0
      2016-01-01 04:30:00+09:00



In [7]:

    
ngo_df.to_csv("ngo_df.csv")

Calculate the overall insolation



In [8]:

    
ngo_df[['GHI','DHI','dHI']].sum()









    Out[8]:





GHI    504188.0
DHI    263011.0
dHI    241177.0
dtype: float64

Extrat DNI

Daily METPV-11 data records the solar insolation on a horizontal plane. We can use get_DNI() to recalculate the DNI data.



In [9]:

    
ngo_dni=ngo_loc.get_DNI()



In [10]:

    
ngo_dni.head()









    Out[10]:





avg_time
2016-01-01 00:30:00+09:00   -0.0
2016-01-01 01:30:00+09:00   -0.0
2016-01-01 02:30:00+09:00   -0.0
2016-01-01 03:30:00+09:00   -0.0
2016-01-01 04:30:00+09:00   -0.0
dtype: float64



In [11]:

    
plt.plot(ngo_dni)
plt.ylim([0,1000])









    Out[11]:





(0, 1000)

Calculate DNI on a tilted surface



In [12]:

    
ngo_tilt_irr=ngo_loc.tilt_irr(include_solar_pos=True)



In [13]:

    
ngo_tilt_irr.head()









    Out[13]:







  
    
      
      GHI
      DHI
      dHI
      sun_time
      temperature
      wind_dir
      wind_speed
      rain
      snow
      avg_time
      ...
      poa_sky_diffuse
      poa_ground_diffuse
      aoi
      DNI
      apparent_elevation
      apparent_zenith
      azimuth
      elevation
      equation_of_time
      zenith
    
    
      avg_time
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2016-01-01 00:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      31.0
      10.0
      47.0
      0.0
      0.0
      2016-01-01 00:30:00+09:00
      ...
      0.0
      0.0
      153.413612
      -0.0
      -72.544908
      162.544908
      45.990508
      -72.544908
      -2.901565
      162.544908
    
    
      2016-01-01 01:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      21.0
      10.0
      37.0
      0.0
      0.0
      2016-01-01 01:30:00+09:00
      ...
      0.0
      0.0
      143.902222
      -0.0
      -62.146860
      152.146860
      70.266810
      -62.146860
      -2.921438
      152.146860
    
    
      2016-01-01 02:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      17.0
      10.0
      20.0
      0.0
      0.0
      2016-01-01 02:30:00+09:00
      ...
      0.0
      0.0
      131.815509
      -0.0
      -50.338026
      140.338026
      83.274763
      -50.338026
      -2.941303
      140.338026
    
    
      2016-01-01 03:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      12.0
      3.0
      19.0
      0.0
      0.0
      2016-01-01 03:30:00+09:00
      ...
      0.0
      0.0
      118.688199
      -0.0
      -38.225568
      128.225568
      92.549976
      -38.225568
      -2.961160
      128.225568
    
    
      2016-01-01 04:30:00+09:00
      0.0
      0.0
      0.0
      0.0
      11.0
      14.0
      24.0
      0.0
      0.0
      2016-01-01 04:30:00+09:00
      ...
      0.0
      0.0
      105.108603
      -0.0
      -26.182925
      116.182925
      100.521619
      -26.182925
      -2.981010
      116.182925
    
  

5 rows × 23 columns



In [14]:

    
ngo_tilt_irr.columns









    Out[14]:





Index(['GHI', 'DHI', 'dHI', 'sun_time', 'temperature', 'wind_dir',
       'wind_speed', 'rain', 'snow', 'avg_time', 'poa_global', 'poa_direct',
       'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse', 'aoi', 'DNI',
       'apparent_elevation', 'apparent_zenith', 'azimuth', 'elevation',
       'equation_of_time', 'zenith'],
      dtype='object')



In [15]:

    
plt.plot(ngo_tilt_irr['poa_direct'],alpha=0.5,label='incidence on tilt surface')
plt.plot(ngo_dni,alpha=0.5,label='DNI')
plt.ylim([0,1000])
plt.legend()









    Out[15]:





<matplotlib.legend.Legend at 0x11aafdda0>

Visualize the sun irradiances in angular plot



In [16]:

    
from matplotlib.colors import LogNorm
filtered_df=ngo_tilt_irr.loc[(ngo_tilt_irr['poa_direct']>1) & (ngo_tilt_irr['poa_direct']<500),
                             ["azimuth","zenith",'poa_direct']]
ax = plt.subplot(111, projection='polar')
ax.plot(filtered_df['azimuth'].values*np.pi/180-np.pi/2, 
        filtered_df['zenith'].values-ngo_loc.latitude,'.')
plt.show()



In [17]:

    
import matplotlib as mpl
filtered_df=ngo_tilt_irr.loc[(ngo_tilt_irr['poa_direct']>1) & (ngo_tilt_irr['poa_direct']<500),
                             ["azimuth","zenith",'poa_direct']]
ax = plt.subplot(111, projection='polar')
colormap = plt.get_cmap('hsv')
norm = mpl.colors.Normalize(1, 400)
cax=ax.scatter(filtered_df['azimuth'].values*np.pi/180-np.pi/2, filtered_df['zenith'].values-ngo_loc.latitude,
           c=filtered_df['poa_direct'].values,s=200,norm=norm,alpha=0.5)
plt.colorbar(cax)
plt.savefig("nagoya_angular.png",dpi=600)
plt.show()

Analyze angle of incidence



In [18]:

    
ngo_tilt_irr.columns









    Out[18]:





Index(['GHI', 'DHI', 'dHI', 'sun_time', 'temperature', 'wind_dir',
       'wind_speed', 'rain', 'snow', 'avg_time', 'poa_global', 'poa_direct',
       'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse', 'aoi', 'DNI',
       'apparent_elevation', 'apparent_zenith', 'azimuth', 'elevation',
       'equation_of_time', 'zenith'],
      dtype='object')



In [19]:

    
plt.hist(ngo_tilt_irr['aoi'],weights=ngo_tilt_irr['poa_direct'],bins=100)
plt.show()



In [ ]:

	measure_type	month	day	wind_speed	1	2	3	4	5	6	...	21	22	23	24	max	min	integral	avg	day_num	year
2016-01-01	1	1	1	17.9	0	0	0	0	0	0	...	0	0	0	0	175	NaN	926.0	NaN	1	2016
2016-01-01	2	1	1	17.9	0	0	0	0	0	0	...	0	0	0	0	118	NaN	500.0	NaN	1	2016
2016-01-01	3	1	1	17.9	0	0	0	0	0	0	...	0	0	0	0	77	NaN	426.0	NaN	1	2016
2016-01-01	4	1	1	17.9	0	0	0	0	0	0	...	0	0	0	0	10	NaN	52.0	NaN	1	2016
2016-01-01	5	1	1	17.9	31	21	17	12	11	17	...	51	41	49	49	104	11.0	NaN	48.0	1	2016

	GHI	DHI	dHI	sun_time	temperature	wind_dir	wind_speed	rain	snow	avg_time
avg_time
2016-01-01 00:30:00+09:00	0.0	0.0	0.0	0.0	31.0	10.0	47.0	0.0	0.0	2016-01-01 00:30:00+09:00
2016-01-01 01:30:00+09:00	0.0	0.0	0.0	0.0	21.0	10.0	37.0	0.0	0.0	2016-01-01 01:30:00+09:00
2016-01-01 02:30:00+09:00	0.0	0.0	0.0	0.0	17.0	10.0	20.0	0.0	0.0	2016-01-01 02:30:00+09:00
2016-01-01 03:30:00+09:00	0.0	0.0	0.0	0.0	12.0	3.0	19.0	0.0	0.0	2016-01-01 03:30:00+09:00
2016-01-01 04:30:00+09:00	0.0	0.0	0.0	0.0	11.0	14.0	24.0	0.0	0.0	2016-01-01 04:30:00+09:00