def apply_decomposition_model(weather_df, model, location):
"""
Uses the specified decomposition model to calculate DNI and DHI.
Parameters
----------
weather_df : :pandas:`DataFrame`
Weather DataFrame containing all variables needed to apply the
decomposition model. See model functions for more information.
model : :obj:`str`
Decomposition model to use. Choose from 'reindl', 'erbs' or 'disc'.
location : :pvlib:`Location`
Returns
-------
:pandas:`DataFrame`
DataFrame with DNI and DHI.
"""
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
if model == 'reindl':
solar_position = location.get_solarposition(
weather_df.index, pressure=weather_df['pressure'].mean(),
temperature=weather_df['temp_air'].mean())
df = reindl(weather_df.ghi, weather_df.i0_h, solar_position.elevation)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'erbs':
df = irradiance.erbs(weather_df.ghi, solar_position.zenith,
weather_df.index)
df['dni_corrected'] = irradiance.dni(
weather_df['ghi'], df['dhi'], solar_position.zenith,
clearsky_dni=location.get_clearsky(
weather_df.index, solar_position=solar_position).dni,
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
elif model == 'disc':
df = irradiance.disc(weather_df.ghi, solar_position.zenith,
weather_df.index, weather_df.pressure.mean())
df['dhi'] = weather_df.ghi - df.dni * pvlib.tools.cosd(
solar_position.zenith)
df['gni'] = df.dni + df.dhi
return df
def test_dni():
ghi = pd.Series([90, 100, 100, 100, 100])
dhi = pd.Series([100, 90, 50, 50, 50])
zenith = pd.Series([80, 100, 85, 70, 85])
clearsky_dni = pd.Series([50, 50, 200, 50, 300])
dni = irradiance.dni(ghi, dhi, zenith,
clearsky_dni=clearsky_dni, clearsky_tolerance=2)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 400,
146.190220008, 573.685662283]))
dni = irradiance.dni(ghi, dhi, zenith)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 573.685662283,
146.190220008, 573.685662283]))
def test_dni():
ghi = pd.Series([90, 100, 100, 100, 100])
dhi = pd.Series([100, 90, 50, 50, 50])
zenith = pd.Series([80, 100, 85, 70, 85])
clearsky_dni = pd.Series([50, 50, 200, 50, 300])
dni = irradiance.dni(ghi, dhi, zenith,
clearsky_dni=clearsky_dni, clearsky_tolerance=2)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 400,
146.190220008, 573.685662283]))
dni = irradiance.dni(ghi, dhi, zenith)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 573.685662283,
146.190220008, 573.685662283]))
def calculate_dni_pvlib(weather_df, corrected=True):
"""
Calculates DNI from open_FRED GHI and DHI using the pvlib.
:param weather_df:
:param corrected:
:return:
"""
# calculate DNI
times = weather_df.index
location = read_htw_data.setup_pvlib_location_object()
solarposition = location.get_solarposition(
times, pressure=None, temperature=weather_df['temp_air'])
if corrected:
# calculate corrected DNI
clearsky = location.get_clearsky(times, solar_position=solarposition)
dni = irradiance.dni(weather_df['ghi'], weather_df['dhi'],
zenith=solarposition['zenith'],
clearsky_dni=clearsky['dni'],
clearsky_tolerance=1.1,
zenith_threshold_for_zero_dni=88.0,
zenith_threshold_for_clearsky_limit=80.0)
else:
dni = (weather_df['ghi'] - weather_df['dhi']) / np.cos(
np.radians(solarposition['zenith']))
# setup df with calculated and Fred DNI
dni.name = 'dni'
df = weather_df['dni'].to_frame().join(
dni.to_frame(), lsuffix='_fred', rsuffix='_pvlib')
dni.set_index('time', inplace=True)
return df
def reindl (lat,lon, times, ghi, extra_i, zenith):
"""
this function calculates dhi, dni and the clearness index kt
from ghi, extraterrestial_irradiance and the solar zenith propsed by Merra2
[1]
Parameters
-----------
ghi: numeric pd.Series or sequence
global horizontal irradiance [W/m^2]
zenith: numeric pd.Series or sequence
real solar zenith angle (not apparent) in [°]
extra_i: numeric pd.Series or sequence
extraterrestial irradiance [W/m^2] == top-of-the-atmosphere irradiance (TOA) == SWTDN (Merra-2)
Returns
-------
data : OrderedDict or DataFrame
Contains the following keys/columns:
* ``dni``: the modeled direct normal irradiance in W/m^2.
* ``dhi``: the modeled diffuse horizontal irradiance in
W/m^2.
* ``kt``: Ratio of global to extraterrestrial irradiance
on a horizontal plane.
References
-----------
[1] Reindl et al. (1990): Diffuse fraction correlations
"""
i0_h = extra_i * tools.cosd(zenith)
kt = ghi / i0_h
kt = np.maximum(kt, 0)
kt.fillna(0, inplace=True)
# for kt outside the boundaries, set diffuse fraction to zero
df = 0.0
# for 0<kt<=0.3 set diffuse fraction
df = np.where((kt > 0) & (kt <= 0.3), 1.02 - 0.254 * kt + 0.0123 * tools.cosd(zenith), df)
# for 0.3<kt<0.78 and df>=0.1, set diffuse fraction
df = np.where((kt > 0.3) & (kt <= 0.78) & (1.4 - 1.794 * kt + 0.177 * tools.cosd(zenith) >= 0.1),
1.4 - 1.794 * kt + 0.177 * tools.cosd(zenith), df)
# for kt > 0.78 and df>=0.1
df = np.where((kt > 0.78) & (0.486 * kt + 0.182 * tools.cosd(zenith) >= 0.1),
0.486 * kt + 0.182 * tools.cosd(zenith), df)
# remove extreme values
df = np.where((df < 0.9) & (kt < 0.2), 0, df)
df = np.where((df > 0.8) & (kt > 0.6), 0, df)
df = np.where((df > 1), 0, df)
df = np.where(((ghi - extra_i) >= 0), 0, df)
dhi = df * ghi
dni = irradiance.dni(ghi, dhi, zenith,
clearsky_dni=Location(lat, lon).get_clearsky(times).dni,
zenith_threshold_for_zero_dni=88.0,
clearsky_tolerance=1.1,
zenith_threshold_for_clearsky_limit=64)
data = OrderedDict()
data['dni'] = dni
data['dhi'] = dhi
data['kt'] = kt
if isinstance(dni, pd.Series):
data = pd.DataFrame(data)
return data
