# TMY data and diffuse irradiance models¶

This tutorial explores using TMY data as inputs to different plane of array diffuse irradiance models.

This tutorial requires pvlib > 0.6.0.

Authors:

• Rob Andrews (@Calama-Consulting), Heliolytics, June 2014
• Will Holmgren (@wholmgren), University of Arizona, July 2015, March 2016, August 2018

## Setup¶

See the tmy_to_power tutorial for more detailed explanations for the initial setup

In [1]:
# built-in python modules
import os
import inspect

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

# 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.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')['2015']

In [3]:
tmy_data.GHI.plot()

In [4]:
tmy_data.DHI.plot()

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
altitude=meta['altitude'], name=meta['Name'].replace('"',''))
print(sand_point)

Location:
name: SAND POINT
latitude: 55.317
longitude: -160.517
altitude: 7.0

In [6]:
solpos = pvlib.solarposition.get_solarposition(tmy_data.index, sand_point.latitude, sand_point.longitude)

solpos.plot();

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 = pd.Series(dni_extra, index=tmy_data.index)

dni_extra.plot()

In [8]:
airmass = pvlib.atmosphere.get_relative_airmass(solpos['apparent_zenith'])

airmass.plot()
plt.ylabel('Airmass');


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']


### Perez¶

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)


### HayDavies¶

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)


### Isotropic¶

In [13]:
diffuse_irrad['Isotropic'] = pvlib.irradiance.isotropic(surface_tilt,
dhi=tmy_data.DHI)


### King Diffuse model¶

In [14]:
diffuse_irrad['King'] = pvlib.irradiance.king(surface_tilt,
dhi=tmy_data.DHI,
ghi=tmy_data.GHI,
solar_zenith=solpos.apparent_zenith)


### Klucher Model¶

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)


### Reindl¶

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').sum().dropna().squeeze() / 1000.0  # kWh
monthly = diffuse_irrad.resample('M', kind='period').sum() / 1000.0


## Plot Results¶

In [18]:
ax = diffuse_irrad.plot(title='In-plane diffuse irradiance', alpha=.75, lw=1)
ax.set_ylim(0, 800)
plt.legend();

In [19]:
diffuse_irrad.describe()

Out[19]:
Perez Hay-Davies Isotropic King Klucher Reindl
count 8760.000000 8760.000000 8760.000000 8760.000000 8760.000000 4578.000000
mean 55.398143 52.457636 49.094681 53.067042 53.610788 100.774025
std 80.439197 75.436304 72.134083 76.340191 78.795999 78.355679
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 39.151044
50% 3.732051 4.114161 3.732051 4.009573 3.732051 87.163898
75% 95.389001 91.217201 81.172105 91.749845 90.879727 145.523600
max 580.694617 533.102519 523.420126 538.809780 544.508380 535.970735
In [20]:
diffuse_irrad.dropna().plot(kind='density');


Daily

In [21]:
ax_daily = daily.tz_convert('UTC').plot(title='Daily diffuse irradiation')


Monthly

In [22]:
ax_monthly = monthly.plot(title='Monthly average diffuse irradiation', kind='bar')


Yearly

In [23]:
yearly.plot(kind='barh');


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');

In [ ]: