This tutorial explores using TMY data as inputs to different plane of array diffuse irradiance models.
This tutorial has been tested against the following package versions:
It should work with other Python and Pandas versions. It requires pvlib > 0.2.0 and IPython > 3.0.
Authors:
See the tmy_to_power tutorial for more detailed explanations for the initial setup
In [1]:
# built-in python modules
import os
import inspect
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# seaborn makes your plots look better
try:
import seaborn as sns
sns.set(rc={"figure.figsize": (12, 6)})
except ImportError:
print('We suggest you install seaborn using conda or pip and rerun this cell')
# finally, we import the pvlib library
import pvlib
In [2]:
# Find the absolute file path to your pvlib installation
pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(pvlib)))
# absolute path to a data file
datapath = os.path.join(pvlib_abspath, 'data', '703165TY.csv')
# read tmy data with year values coerced to a single year
tmy_data, meta = pvlib.tmy.readtmy3(datapath, coerce_year=2015)
tmy_data.index.name = 'Time'
# TMY data seems to be given as hourly data with time stamp at the end
# shift the index 30 Minutes back for calculation of sun positions
tmy_data = tmy_data.shift(freq='-30Min')
In [3]:
tmy_data.GHI.plot()
plt.ylabel('Irradiance (W/m**2)')
Out[3]:
In [4]:
tmy_data.DHI.plot()
plt.ylabel('Irradiance (W/m**2)')
Out[4]:
In [5]:
surface_tilt = 30
surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention
albedo = 0.2
# create pvlib Location object based on meta data
sand_point = pvlib.location.Location(meta['latitude'], meta['longitude'], tz='US/Alaska',
altitude=meta['altitude'], name=meta['Name'].replace('"',''))
print(sand_point)
In [6]:
solpos = pvlib.solarposition.get_solarposition(tmy_data.index, sand_point)
solpos.plot()
Out[6]:
In [7]:
# the extraradiation function returns a simple numpy array
# instead of a nice pandas series. We will change this
# in a future version
dni_extra = pvlib.irradiance.extraradiation(tmy_data.index)
dni_extra = pd.Series(dni_extra, index=tmy_data.index)
dni_extra.plot()
plt.ylabel('Extra terrestrial radiation (W/m**2)')
Out[7]:
In [8]:
airmass = pvlib.atmosphere.relativeairmass(solpos['apparent_zenith'])
airmass.plot()
plt.ylabel('Airmass')
Out[8]:
Make an empty pandas DataFrame for the results.
In [9]:
diffuse_irrad = pd.DataFrame(index=tmy_data.index)
In [10]:
models = ['Perez', 'Hay-Davies', 'Isotropic', 'King', 'Klucher', 'Reindl']
In [11]:
diffuse_irrad['Perez'] = pvlib.irradiance.perez(surface_tilt,
surface_azimuth,
dhi=tmy_data.DHI,
dni=tmy_data.DNI,
dni_extra=dni_extra,
solar_zenith=solpos.apparent_zenith,
solar_azimuth=solpos.azimuth,
airmass=airmass)
In [12]:
diffuse_irrad['Hay-Davies'] = pvlib.irradiance.haydavies(surface_tilt,
surface_azimuth,
dhi=tmy_data.DHI,
dni=tmy_data.DNI,
dni_extra=dni_extra,
solar_zenith=solpos.apparent_zenith,
solar_azimuth=solpos.azimuth)
In [13]:
diffuse_irrad['Isotropic'] = pvlib.irradiance.isotropic(surface_tilt,
dhi=tmy_data.DHI)
In [14]:
diffuse_irrad['King'] = pvlib.irradiance.king(surface_tilt,
dhi=tmy_data.DHI,
ghi=tmy_data.GHI,
solar_zenith=solpos.apparent_zenith)
In [15]:
diffuse_irrad['Klucher'] = pvlib.irradiance.klucher(surface_tilt, surface_azimuth,
dhi=tmy_data.DHI,
ghi=tmy_data.GHI,
solar_zenith=solpos.apparent_zenith,
solar_azimuth=solpos.azimuth)
In [16]:
diffuse_irrad['Reindl'] = pvlib.irradiance.reindl(surface_tilt,
surface_azimuth,
dhi=tmy_data.DHI,
dni=tmy_data.DNI,
ghi=tmy_data.GHI,
dni_extra=dni_extra,
solar_zenith=solpos.apparent_zenith,
solar_azimuth=solpos.azimuth)
Calculate yearly, monthly, daily sums.
In [17]:
yearly = diffuse_irrad.resample('A', how='sum').dropna().squeeze() / 1000.0 # kWh
monthly = diffuse_irrad.resample('M', how='sum', kind='period') / 1000.0
daily = diffuse_irrad.resample('D', how='sum') / 1000.0
In [18]:
ax = diffuse_irrad.plot(title='In-plane diffuse irradiance', alpha=.75, lw=1)
ax.set_ylim(0, 800)
ylabel = ax.set_ylabel('Diffuse Irradiance [W]')
plt.legend()
Out[18]:
In [19]:
diffuse_irrad.describe()
Out[19]:
In [20]:
diffuse_irrad.dropna().plot(kind='density')
Out[20]:
Daily
In [21]:
ax_daily = daily.tz_convert('UTC').plot(title='Daily diffuse irradiation')
ylabel = ax_daily.set_ylabel('Irradiation [kWh]')
Monthly
In [22]:
ax_monthly = monthly.plot(title='Monthly average diffuse irradiation', kind='bar')
ylabel = ax_monthly.set_ylabel('Irradiation [kWh]')
Yearly
In [23]:
yearly.plot(kind='barh')
Out[23]:
Compute the mean deviation from measured for each model and display as a function of the model
In [24]:
mean_yearly = yearly.mean()
yearly_mean_deviation = (yearly - mean_yearly) / yearly * 100.0
yearly_mean_deviation.plot(kind='bar')
Out[24]:
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