def test_luminance_in_timeseries_calc(df_perez_luminance,
mock_array_timeseries_calculate):
"""
Test that the calculation of luminance -- first step in using the vf model
with Perez -- is functional
"""
df_inputs_clearday = pd.read_csv(FILE_PATH)
df_inputs_clearday = df_inputs_clearday.set_index('datetime', drop=True)
df_inputs_clearday.index = (pd.DatetimeIndex(
df_inputs_clearday.index).tz_localize('UTC').tz_convert(
'Etc/GMT+7').tz_localize(None))
# Break up inputs
(timestamps, surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
dni, dhi) = breakup_df_inputs(df_inputs_clearday)
_, df_outputs = calculate_radiosities_serially_perez(
(None, timestamps, solar_zenith, solar_azimuth, surface_tilt,
surface_azimuth, dni, dhi))
col_order = df_outputs.columns
tol = 1e-8
np.testing.assert_allclose(df_outputs.values,
df_perez_luminance[col_order].values,
atol=0,
rtol=tol)
def test_serial_calculation(pvarray_parameters_serial_calc,
df_inputs_serial_calculation):
"""
Make sure that the calculations using the Perez model stay consistent for
all the modeled surfaces. Also testing that there is no unexpected NaN.
"""
# Break up inputs
(timestamps, surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
dni, dhi) = breakup_df_inputs(df_inputs_serial_calculation)
# Run calculation in 1 process only
df_registries, _ = calculate_radiosities_serially_perez(
(pvarray_parameters_serial_calc, timestamps, solar_zenith,
solar_azimuth, surface_tilt, surface_azimuth, dni, dhi))
# Format df_registries to get outputs
df_outputs = get_average_pvrow_outputs(df_registries,
include_shading=False)
# Did the outputs remain consistent?
test_results = values_are_consistent(df_outputs)
for result in test_results:
assert result['passed'], ("test failed for %s" %
result['irradiance_term'])
def test_back_surface_luminance():
"""
The model didn't calculate cases when the sun would hit the back surface
because the perez model would return 0 circumsolar (not calculated for
back surface). Fix was implemented, and this should check for it.
"""
pvarray_parameters = {
'surface_azimuth': 90,
'surface_tilt': 0.0,
'gcr': 0.3,
'n_pvrows': 3,
'Pvrow_height': 1.5,
'pvrow_width': 1.0,
'rho_back_pvrow': 0.03,
'rho_front_pvrow': 0.01,
'rho_ground': 0.2,
'solar_azimuth': 90.0,
'solar_zenith': 20.0
}
input_filename = 'file_test_back_surface_luminance.csv'
df_inputs = pd.read_csv(os.path.join(TEST_DATA, input_filename),
index_col=0)
df_inputs.index = pd.DatetimeIndex(df_inputs.index).tz_localize(
'UTC').tz_convert('US/Arizona')
# Break up inputs
(timestamps, tracker_theta, surface_azimuth,
solar_zenith, solar_azimuth, dni, dhi) = breakup_df_inputs(df_inputs)
args = (pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
tracker_theta, surface_azimuth, dni, dhi)
df_registries, _ = calculate_radiosities_serially_perez(args)
df_outputs = get_average_pvrow_outputs(df_registries)
vf_ipoa_front = df_outputs.loc[:, IDX_SLICE[1, 'front', 'qinc']]
vf_ipoa_back = df_outputs.loc[:, IDX_SLICE[1, 'back', 'qinc']]
assert isinstance(vf_ipoa_front[0], float)
assert isinstance(vf_ipoa_back[0], float)
def test_serial_circumsolar_shading_calculation():
"""
Calculate and save results from front surface circumsolar shading on
pvrows. Test that it functions with the given data.
"""
# Choose a PV array configuration and pass the arguments necessary for
# the calculation to be triggered:
# eg 'calculate_front_circ_horizon_shading'
arguments = {
'array_azimuth': 90.0,
'array_tilt': 20.0,
'cut': [(1, 5, 'front')],
'gcr': 0.3,
'n_pvrows': 2,
'pvrow_height': 1.5,
'pvrow_width': 1.,
'rho_ground': 0.2,
'rho_pvrow_back': 0.03,
'rho_pvrow_front': 0.01,
'solar_azimuth': 90.0,
'solar_zenith': 30.0,
'circumsolar_angle': 50.,
'horizon_band_angle': 6.5,
'calculate_front_circ_horizon_shading': True,
'circumsolar_model': 'gaussian'
}
# Load inputs for the serial calculation
test_file = os.path.join(
TEST_DATA, 'file_test_serial_circumsolar_shading_calculation.csv')
df_inputs = pd.read_csv(test_file, index_col=0)
df_inputs.index = pd.DatetimeIndex(df_inputs.index)
(timestamps, array_tilt, array_azimuth,
solar_zenith, solar_azimuth, dni, dhi) = breakup_df_inputs(df_inputs)
# Run the calculation for functional testing
df_registries, df_inputs_perez = (
calculate_radiosities_serially_perez((arguments, timestamps, array_tilt,
array_azimuth, solar_zenith,
solar_azimuth, dni, dhi))
)
def test_negativevf_and_flatcasenoon():
pvarray_parameters = {
'surface_azimuth': 90,
'tracker_theta': 0.0,
'gcr': 0.3,
'n_pvrows': 3,
'pvrow_height': 1.5,
'pvrow_width': 1.0,
'rho_back_pvrow': 0.03,
'rho_front_pvrow': 0.01,
'rho_ground': 0.2,
'solar_azimuth': 90.0,
'solar_zenith': 20.0
}
input_filename = 'file_test_negativevf_and_flatcasenoon.csv'
df_inputs = pd.read_csv(os.path.join(TEST_DATA, input_filename),
index_col=0)
df_inputs.index = pd.DatetimeIndex(df_inputs.index).tz_localize(
'UTC').tz_convert('US/Arizona')
# Break up inputs
(timestamps, tracker_theta, surface_azimuth,
solar_zenith, solar_azimuth, dni, dhi) = breakup_df_inputs(df_inputs)
args = (pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
tracker_theta, surface_azimuth, dni, dhi)
df_registries, _ = calculate_radiosities_serially_perez(args)
df_outputs = get_average_pvrow_outputs(df_registries)
vf_ipoa_front = df_outputs.loc[:, IDX_SLICE[1, 'front', 'qinc']]
vf_ipoa_back = df_outputs.loc[:, IDX_SLICE[1, 'back', 'qinc']]
# The model should calculate for all daytime points now since we fixed
# the solar noon case (almost flat but not really), and we allowed
# negative vf values early and late in the day
expected_n_calculated_values = 13
assert np.sum(vf_ipoa_front.notnull()) == expected_n_calculated_values
assert np.sum(vf_ipoa_back.notnull()) == expected_n_calculated_values
def test_serial_calculation_with_skips(
pvarray_parameters_serial_calc,
df_inputs_serial_calculation_with_skips):
"""
Make sure that the calculations using the Perez model stay consistent for
all the modeled surfaces. Also testing that there is no unexpected NaN.
"""
# Break up inputs
(timestamps, surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
dni, dhi) = breakup_df_inputs(df_inputs_serial_calculation_with_skips)
# Run calculation in 1 process only
df_registries, _ = calculate_radiosities_serially_perez(
(pvarray_parameters_serial_calc, timestamps, solar_zenith,
solar_azimuth, surface_tilt, surface_azimuth, dni, dhi))
list_nan_idx = df_registries.index[df_registries.set_index(
'timestamps').count(axis=1) == 0]
# There should be one line with only nan values
assert len(list_nan_idx) == 1
def test_save_all_outputs_calculate_perez():
"""
Make sure that the serial and parallel calculations are able to save all
the requested data on discretized segments (instead of averaging them by
default). Check the consistency of the results.
"""
# Load timeseries input data
df_inputs_clearday = pd.read_csv(FILE_PATH)
df_inputs_clearday = df_inputs_clearday.set_index('datetime', drop=True)
df_inputs_clearday.index = (pd.DatetimeIndex(
df_inputs_clearday.index).tz_localize('UTC').tz_convert(
'Etc/GMT+7').tz_localize(None))
idx_subset = 10
# PV array parameters for test
arguments = {
'n_pvrows': 3,
'pvrow_height': 1.5,
'pvrow_width': 1.,
'gcr': 0.4,
'rho_ground': 0.8,
'rho_back_pvrow': 0.03,
'rho_front_pvrow': 0.01,
'cut': [(1, 3, 'front')]
}
# Break up inputs
(timestamps, surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
dni, dhi) = breakup_df_inputs(df_inputs_clearday.iloc[:idx_subset])
args = (arguments, timestamps, solar_zenith, solar_azimuth, surface_tilt,
surface_azimuth, dni, dhi)
# Run the serial calculation
df_registries_serial, _ = (calculate_radiosities_serially_perez(args))
df_registries_parallel, _ = (calculate_radiosities_parallel_perez(*args))
# Format the outputs
df_outputs_segments_serial = get_pvrow_segment_outputs(
df_registries_serial, values=['qinc'], include_shading=False)
df_outputs_segments_parallel = get_pvrow_segment_outputs(
df_registries_parallel, values=['qinc'], include_shading=False)
# Load files with expected outputs
expected_ipoa_dict_qinc = np.array(
[[842.54617681, 842.5566707, 842.43690951],
[839.30179691, 839.30652961, 839.30906023],
[839.17118956, 839.17513098, 839.17725568],
[842.24679271, 842.26194393, 842.15463231]])
# Perform the comparisons
rtol = 1e-6
atol = 0
np.testing.assert_allclose(expected_ipoa_dict_qinc,
df_outputs_segments_serial.values,
atol=atol,
rtol=rtol)
np.testing.assert_allclose(expected_ipoa_dict_qinc,
df_outputs_segments_parallel.values,
atol=atol,
rtol=rtol)
def pvfactors_timeseries(solar_azimuth,
solar_zenith,
surface_azimuth,
surface_tilt,
timestamps,
dni,
dhi,
gcr,
pvrow_height,
pvrow_width,
albedo,
n_pvrows=3,
index_observed_pvrow=1,
rho_front_pvrow=0.03,
rho_back_pvrow=0.05,
horizon_band_angle=15.,
run_parallel_calculations=True,
n_workers_for_parallel_calcs=None):
"""
Calculate front and back surface plane-of-array irradiance on
a fixed tilt or single-axis tracker PV array configuration, and using
the open-source "pvfactors" package.
Please refer to pvfactors online documentation for more details:
https://sunpower.github.io/pvfactors/
Parameters
----------
solar_azimuth: numeric
Sun's azimuth angles using pvlib's azimuth convention (deg)
solar_zenith: numeric
Sun's zenith angles (deg)
surface_azimuth: numeric
Azimuth angle of the front surface of the PV modules, using pvlib's
convention (deg)
surface_tilt: numeric
Tilt angle of the PV modules, going from 0 to 180 (deg)
timestamps: datetime or DatetimeIndex
List of simulation timestamps
dni: numeric
Direct normal irradiance (W/m2)
dhi: numeric
Diffuse horizontal irradiance (W/m2)
gcr: float
Ground coverage ratio of the pv array
pvrow_height: float
Height of the pv rows, measured at their center (m)
pvrow_width: float
Width of the pv rows in the considered 2D plane (m)
albedo: float
Ground albedo
n_pvrows: int, default 3
Number of PV rows to consider in the PV array
index_observed_pvrow: int, default 1
Index of the PV row whose incident irradiance will be returned. Indices
of PV rows go from 0 to n_pvrows-1.
rho_front_pvrow: float, default 0.03
Front surface reflectivity of PV rows
rho_back_pvrow: float, default 0.05
Back surface reflectivity of PV rows
horizon_band_angle: float, default 15
Elevation angle of the sky dome's diffuse horizon band (deg)
run_parallel_calculations: bool, default True
pvfactors is capable of using multiprocessing. Use this flag to decide
to run calculations in parallel (recommended) or not.
n_workers_for_parallel_calcs: int, default None
Number of workers to use in the case of parallel calculations. The
default value of 'None' will lead to using a value equal to the number
of CPU's on the machine running the model.
Returns
-------
front_poa_irradiance: numeric
Calculated incident irradiance on the front surface of the PV modules
(W/m2)
back_poa_irradiance: numeric
Calculated incident irradiance on the back surface of the PV modules
(W/m2)
df_registries: pandas DataFrame
DataFrame containing detailed outputs of the simulation; for
instance the shapely geometries, the irradiance components incident on
all surfaces of the PV array (for all timestamps), etc.
In the pvfactors documentation, this is refered to as the "surface
registry".
References
----------
.. [1] Anoma, Marc Abou, et al. "View Factor Model and Validation for
Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE
Photovoltaic Specialist Conference. 2017.
"""
# Convert pandas Series inputs to numpy arrays
if isinstance(solar_azimuth, pd.Series):
solar_azimuth = solar_azimuth.values
if isinstance(solar_zenith, pd.Series):
solar_zenith = solar_zenith.values
if isinstance(surface_azimuth, pd.Series):
surface_azimuth = surface_azimuth.values
if isinstance(surface_tilt, pd.Series):
surface_tilt = surface_tilt.values
if isinstance(dni, pd.Series):
dni = dni.values
if isinstance(dhi, pd.Series):
dhi = dhi.values
# Import pvfactors functions for timeseries calculations.
from pvfactors.timeseries import (calculate_radiosities_parallel_perez,
calculate_radiosities_serially_perez,
get_average_pvrow_outputs)
idx_slice = pd.IndexSlice
# Build up pv array configuration parameters
pvarray_parameters = {
'n_pvrows': n_pvrows,
'pvrow_height': pvrow_height,
'pvrow_width': pvrow_width,
'gcr': gcr,
'rho_ground': albedo,
'rho_front_pvrow': rho_front_pvrow,
'rho_back_pvrow': rho_back_pvrow,
'horizon_band_angle': horizon_band_angle
}
# Run pvfactors calculations: either in parallel or serially
if run_parallel_calculations:
df_registries, df_custom_perez = calculate_radiosities_parallel_perez(
pvarray_parameters,
timestamps,
solar_zenith,
solar_azimuth,
surface_tilt,
surface_azimuth,
dni,
dhi,
n_processes=n_workers_for_parallel_calcs)
else:
inputs = (pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, dni, dhi)
df_registries, df_custom_perez = calculate_radiosities_serially_perez(
inputs)
# Get the average surface outputs
df_outputs = get_average_pvrow_outputs(df_registries,
values=['qinc'],
include_shading=True)
# Select the calculated outputs from the pvrow to observe
ipoa_front = df_outputs.loc[:, idx_slice[index_observed_pvrow, 'front',
'qinc']]
ipoa_back = df_outputs.loc[:, idx_slice[index_observed_pvrow, 'back',
'qinc']]
# Set timestamps as index of df_registries for consistency of outputs
df_registries = df_registries.set_index('timestamps')
return ipoa_front, ipoa_back, df_registries
def pvfactors_timeseries(
solar_azimuth, solar_zenith, surface_azimuth, surface_tilt,
timestamps, dni, dhi, gcr, pvrow_height, pvrow_width, albedo,
n_pvrows=3, index_observed_pvrow=1,
rho_front_pvrow=0.03, rho_back_pvrow=0.05,
horizon_band_angle=15.,
run_parallel_calculations=True, n_workers_for_parallel_calcs=None):
"""
Calculate front and back surface plane-of-array irradiance on
a fixed tilt or single-axis tracker PV array configuration, and using
the open-source "pvfactors" package.
Please refer to pvfactors online documentation for more details:
https://sunpower.github.io/pvfactors/
Inputs
------
solar_azimuth: numeric
Sun's azimuth angles using pvlib's azimuth convention (deg)
solar_zenith: numeric
Sun's zenith angles (deg)
surface_azimuth: numeric
Azimuth angle of the front surface of the PV modules, using pvlib's
convention (deg)
surface_tilt: numeric
Tilt angle of the PV modules, going from 0 to 180 (deg)
timestamps: datetime or DatetimeIndex
List of simulation timestamps
dni: numeric
Direct normal irradiance (W/m2)
dhi: numeric
Diffuse horizontal irradiance (W/m2)
gcr: float
Ground coverage ratio of the pv array
pvrow_height: float
Height of the pv rows, measured at their center (m)
pvrow_width: float
Width of the pv rows in the considered 2D plane (m)
albedo: float
Ground albedo
n_pvrows: int, default 3
Number of PV rows to consider in the PV array
index_observed_pvrow: int, default 1
Index of the PV row whose incident irradiance will be returned. Indices
of PV rows go from 0 to n_pvrows-1.
rho_front_pvrow: float, default 0.03
Front surface reflectivity of PV rows
rho_back_pvrow: float, default 0.05
Back surface reflectivity of PV rows
horizon_band_angle: float, default 15
Elevation angle of the sky dome's diffuse horizon band (deg)
run_parallel_calculations: bool, default True
pvfactors is capable of using multiprocessing. Use this flag to decide
to run calculations in parallel (recommended) or not.
n_workers_for_parallel_calcs: int, default None
Number of workers to use in the case of parallel calculations. The
default value of 'None' will lead to using a value equal to the number
of CPU's on the machine running the model.
Returns
-------
front_poa_irradiance: numeric
Calculated incident irradiance on the front surface of the PV modules
(W/m2)
back_poa_irradiance: numeric
Calculated incident irradiance on the back surface of the PV modules
(W/m2)
df_registries: pandas DataFrame
DataFrame containing detailed outputs of the simulation; for
instance the shapely geometries, the irradiance components incident on
all surfaces of the PV array (for all timestamps), etc.
In the pvfactors documentation, this is refered to as the "surface
registry".
References
----------
.. [1] Anoma, Marc Abou, et al. "View Factor Model and Validation for
Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE
Photovoltaic Specialist Conference. 2017.
"""
# Convert pandas Series inputs to numpy arrays
if isinstance(solar_azimuth, pd.Series):
solar_azimuth = solar_azimuth.values
if isinstance(solar_zenith, pd.Series):
solar_zenith = solar_zenith.values
if isinstance(surface_azimuth, pd.Series):
surface_azimuth = surface_azimuth.values
if isinstance(surface_tilt, pd.Series):
surface_tilt = surface_tilt.values
if isinstance(dni, pd.Series):
dni = dni.values
if isinstance(dhi, pd.Series):
dhi = dhi.values
# Import pvfactors functions for timeseries calculations.
from pvfactors.timeseries import (calculate_radiosities_parallel_perez,
calculate_radiosities_serially_perez,
get_average_pvrow_outputs)
idx_slice = pd.IndexSlice
# Build up pv array configuration parameters
pvarray_parameters = {
'n_pvrows': n_pvrows,
'pvrow_height': pvrow_height,
'pvrow_width': pvrow_width,
'gcr': gcr,
'rho_ground': albedo,
'rho_front_pvrow': rho_front_pvrow,
'rho_back_pvrow': rho_back_pvrow,
'horizon_band_angle': horizon_band_angle
}
# Run pvfactors calculations: either in parallel or serially
if run_parallel_calculations:
df_registries, df_custom_perez = calculate_radiosities_parallel_perez(
pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, dni, dhi,
n_processes=n_workers_for_parallel_calcs)
else:
inputs = (pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, dni, dhi)
df_registries, df_custom_perez = calculate_radiosities_serially_perez(
inputs)
# Get the average surface outputs
df_outputs = get_average_pvrow_outputs(df_registries,
values=['qinc'],
include_shading=True)
# Select the calculated outputs from the pvrow to observe
ipoa_front = df_outputs.loc[:, idx_slice[index_observed_pvrow,
'front', 'qinc']]
ipoa_back = df_outputs.loc[:, idx_slice[index_observed_pvrow,
'back', 'qinc']]
# Set timestamps as index of df_registries for consistency of outputs
df_registries = df_registries.set_index('timestamps')
return ipoa_front, ipoa_back, df_registries
def test_save_all_outputs_calculate_perez():
"""
Make sure that the serial and parallel calculations are able to save all
the requested data on discretized segments (instead of averaging them by
default). Check the consistency of the results.
"""
# Load timeseries input data
df_inputs_clearday = pd.read_csv(FILE_PATH)
df_inputs_clearday = df_inputs_clearday.set_index('datetime', drop=True)
df_inputs_clearday.index = (pd.DatetimeIndex(
df_inputs_clearday.index).tz_localize('UTC').tz_convert(
'Etc/GMT+7').tz_localize(None))
idx_subset = 10
# Adjustment in angles needed: need to keep azimuth constant and change
# tilt angle only
df_inputs_clearday.loc[(df_inputs_clearday.solar_azimuth <= 180.),
'array_azimuth'] = (
df_inputs_clearday.loc[:, 'array_azimuth'][-1])
df_inputs_clearday.loc[(df_inputs_clearday.solar_azimuth <= 180.),
'array_tilt'] *= (-1)
# PV array parameters for test
arguments = {
'n_pvrows': 3,
'pvrow_height': 1.5,
'pvrow_width': 1.,
'gcr': 0.4,
'rho_ground': 0.8,
'rho_back_pvrow': 0.03,
'rho_front_pvrow': 0.01,
'cut': [(1, 3, 'front')]
}
# Break up inputs
(timestamps, array_tilt, array_azimuth, solar_zenith, solar_azimuth, dni,
dhi) = breakup_df_inputs(df_inputs_clearday.iloc[:idx_subset])
args = (arguments, timestamps, solar_zenith, solar_azimuth, array_tilt,
array_azimuth, dni, dhi)
# Run the serial calculation
df_registries_serial, _ = (calculate_radiosities_serially_perez(args))
df_registries_parallel, _ = (calculate_radiosities_parallel_perez(*args))
# Format the outputs
df_outputs_segments_serial = get_pvrow_segment_outputs(
df_registries_serial, values=['qinc'], include_shading=False)
df_outputs_segments_parallel = get_pvrow_segment_outputs(
df_registries_parallel, values=['qinc'], include_shading=False)
# Load files with expected outputs
expected_ipoa_dict_qinc = np.array(
[[842.43691838, 842.54795737, 842.52912932],
[839.30539601, 839.30285394, 839.29810984],
[839.17118976, 839.17513111, 839.17725576],
[842.24681064, 842.26195526, 842.15463995]])
# Perform the comparisons
rtol = 1e-7
atol = 0
assert np.allclose(expected_ipoa_dict_qinc,
df_outputs_segments_serial.values,
atol=atol,
rtol=rtol)
assert np.allclose(expected_ipoa_dict_qinc,
df_outputs_segments_parallel.values,
atol=atol,
rtol=rtol)
