def test_pvengine_float_inputs_perez_transparency_spacing_fast(params):
"""Test that module transparency and spacing are having the
expected effect to calculated PV back side irradiance"""
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# --- with 0 transparency and spacing
# Create models
irr_params = {'module_transparency': 0.,
'module_spacing_ratio': 0.}
irradiance_model = HybridPerezOrdered(**irr_params)
pvarray = OrderedPVArray.init_from_dict(params)
eng = PVEngine(pvarray, irradiance_model=irradiance_model)
# Fit engine
eng.fit(timestamps, DNI, DHI,
params['solar_zenith'],
params['solar_azimuth'],
params['surface_tilt'],
params['surface_azimuth'],
params['rho_ground'])
# Run timestep
def fn_report(pvarray): return (pvarray.ts_pvrows[1]
.back.get_param_weighted('qinc'))
no_spacing_transparency_back_qinc = \
eng.run_fast_mode(fn_build_report=fn_report, pvrow_index=1)
# --- with non-0 transparency and spacing
# Create models
irr_params = {'module_transparency': 0.1,
'module_spacing_ratio': 0.1}
irradiance_model = HybridPerezOrdered(**irr_params)
pvarray = OrderedPVArray.init_from_dict(params)
eng = PVEngine(pvarray, irradiance_model=irradiance_model)
# Fit engine
eng.fit(timestamps, DNI, DHI,
params['solar_zenith'],
params['solar_azimuth'],
params['surface_tilt'],
params['surface_azimuth'],
params['rho_ground'])
# Run timestep
w_spacing_transparency_back_qinc = \
eng.run_fast_mode(fn_build_report=fn_report, pvrow_index=1)
# Checks
expected_back_qinc = 134.7143531 # higher than when params are 0
np.testing.assert_almost_equal(
w_spacing_transparency_back_qinc, expected_back_qinc)
assert no_spacing_transparency_back_qinc < w_spacing_transparency_back_qinc
def test_run_fast_mode_back_shading(params):
"""Test that PV engine works for timeseries fast mode and float inputs,
and when there's large direct shading on the back surface.
Value is very close to loop-style fast mode"""
params.update({
'gcr': 0.6,
'surface_azimuth': 270,
'surface_tilt': 120,
'solar_zenith': 70.
})
# Prepare some engine inputs
irradiance_model = HybridPerezOrdered()
pvarray = OrderedPVArray.init_from_dict(
params, param_names=irradiance_model.params)
fast_mode_pvrow_index = 1
fast_mode_segment_index = 0
# Create engine object
eng = PVEngine(pvarray,
irradiance_model=irradiance_model,
fast_mode_pvrow_index=fast_mode_pvrow_index,
fast_mode_segment_index=fast_mode_segment_index)
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# Expected values
expected_qinc = 683.537153
# Fit engine
eng.fit(timestamps, DNI, DHI, params['solar_zenith'],
params['solar_azimuth'], params['surface_tilt'],
params['surface_azimuth'], params['rho_ground'])
def fn_report(pvarray):
return (pvarray.ts_pvrows[1].back.get_param_weighted('qinc'))
# By providing segment index
qinc = eng.run_fast_mode(fn_build_report=fn_report)
# Check results
np.testing.assert_allclose(qinc, expected_qinc)
# Without providing segment index
eng.fast_mode_segment_index = None
qinc = eng.run_fast_mode(fn_build_report=fn_report)
# Check results
np.testing.assert_allclose(qinc, expected_qinc)
def test_run_fast_mode_segments(params):
"""Test that PV engine works for timeseries fast mode and float inputs.
Value is very close to loop-like fast mode"""
# Discretize middle PV row's back side
params.update({'cut': {1: {'back': 5}}})
# Prepare some engine inputs
irradiance_model = HybridPerezOrdered()
pvarray = OrderedPVArray.init_from_dict(
params, param_names=irradiance_model.params)
fast_mode_pvrow_index = 1
fast_mode_segment_index = 2
# Create engine object
eng = PVEngine(pvarray,
irradiance_model=irradiance_model,
fast_mode_pvrow_index=fast_mode_pvrow_index,
fast_mode_segment_index=fast_mode_segment_index)
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# Fit engine
eng.fit(timestamps, DNI, DHI, params['solar_zenith'],
params['solar_azimuth'], params['surface_tilt'],
params['surface_azimuth'], params['rho_ground'])
# Checks
np.testing.assert_almost_equal(eng.irradiance.direct['front_illum_pvrow'],
DNI)
# Define report function to grab irradiance from PV row segment
def fn_report(pvarray):
return (pvarray.ts_pvrows[1].back.list_segments[2].get_param_weighted(
'qinc'))
# Expected value for middle segment
qinc_expected = 116.572594
# Run fast mode for specific segment
qinc_segment = eng.run_fast_mode(fn_build_report=fn_report)
# Check results
np.testing.assert_allclose(qinc_segment, qinc_expected)
# Without providing segment index: the value should be the same as above
eng.fast_mode_segment_index = None
qinc_segment = eng.run_fast_mode(fn_build_report=fn_report)
# Check results
np.testing.assert_allclose(qinc_segment, qinc_expected)
def test_run_fast_and_full_modes_sequentially(params, fn_report_example):
"""Make sure that can run fast and full modes one after the other
without making the engine crash"""
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# Prepare some engine inputs
pvarray = OrderedPVArray.init_from_dict(params)
fast_mode_pvrow_index = 1
fast_mode_segment_index = 0
# Create engine object
eng = PVEngine(pvarray,
fast_mode_pvrow_index=fast_mode_pvrow_index,
fast_mode_segment_index=fast_mode_segment_index)
# Fit engine
eng.fit(timestamps, DNI, DHI, params['solar_zenith'],
params['solar_azimuth'], params['surface_tilt'],
params['surface_azimuth'], params['rho_ground'])
# Run fast mode
def fn_report(pvarray):
return (pvarray.ts_pvrows[1].back.get_param_weighted('qinc'))
qinc_fast = eng.run_fast_mode(fn_build_report=fn_report)
# Run full mode
report = eng.run_full_mode(fn_build_report=fn_report_example)
np.testing.assert_allclose(qinc_fast, 119.095285)
np.testing.assert_allclose(report['qinc_back'], 116.49050349491)
def test_fast_mode_8760(params, df_inputs_clearsky_8760):
"""Test fast mode with 1 PV row to make sure that is consistent
after the vectorization update: should get exact same values
"""
# Get MET data
df_inputs = df_inputs_clearsky_8760
timestamps = df_inputs.index
dni = df_inputs.dni.values
dhi = df_inputs.dhi.values
solar_zenith = df_inputs.solar_zenith.values
solar_azimuth = df_inputs.solar_azimuth.values
surface_tilt = df_inputs.surface_tilt.values
surface_azimuth = df_inputs.surface_azimuth.values
# Run simulation for only 1 PV row
params.update({'n_pvrows': 1})
# Run engine
pvarray = OrderedPVArray.init_from_dict(params)
eng = PVEngine(pvarray)
eng.fit(timestamps, dni, dhi, solar_zenith, solar_azimuth, surface_tilt,
surface_azimuth, params['rho_ground'])
qinc = eng.run_fast_mode(
fn_build_report=lambda pvarray:
(pvarray.ts_pvrows[0].back.get_param_weighted('qinc')),
pvrow_index=0)
# Check than annual energy on back is consistent
np.testing.assert_allclose(np.nansum(qinc) / 1e3, 342.848005)
def test_run_fast_mode_isotropic(params):
"""Test that PV engine works for timeseries fast mode and float inputs,
and using the isotropic irradiance model"""
# Prepare some engine inputs
irradiance_model = IsotropicOrdered()
pvarray = OrderedPVArray.init_from_dict(
params, param_names=irradiance_model.params)
fast_mode_pvrow_index = 1
fast_mode_segment_index = 0
# Create engine object
eng = PVEngine(pvarray, irradiance_model=irradiance_model,
fast_mode_pvrow_index=fast_mode_pvrow_index,
fast_mode_segment_index=fast_mode_segment_index)
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# Fit engine
eng.fit(timestamps, DNI, DHI,
params['solar_zenith'], params['solar_azimuth'],
params['surface_tilt'], params['surface_azimuth'],
params['rho_ground'])
# Checks
np.testing.assert_almost_equal(eng.irradiance.direct['front_illum_pvrow'],
DNI)
# Expected value
qinc_expected = 122.73453
# Run fast mode
qinc = eng.run_fast_mode(
fn_build_report=lambda pvarray: (pvarray.ts_pvrows[1]
.back.get_param_weighted('qinc')))
# Check results
np.testing.assert_allclose(qinc, qinc_expected)
# Without providing segment index
eng.fast_mode_segment_index = None
qinc = eng.run_fast_mode(
fn_build_report=lambda pvarray: (pvarray.ts_pvrows[1]
.back.get_param_weighted('qinc')))
# Check results
np.testing.assert_allclose(qinc, qinc_expected)
def test_run_fast_mode_compare_to_loop_like(params):
"""Test that PV engine works for timeseries fast mode and float inputs.
Check the left pv row back side (no obstruction, since rotation < 0)
This should be exactly the same calculated value as in loop-like fast mode
because ground is not infinite for back of left pv row"""
# Prepare some engine inputs
irradiance_model = HybridPerezOrdered()
pvarray = OrderedPVArray.init_from_dict(
params, param_names=irradiance_model.params)
fast_mode_pvrow_index = 1
# Irradiance inputs
timestamps = dt.datetime(2019, 6, 11, 11)
DNI = 1000.
DHI = 100.
# Create engine object
eng = PVEngine(pvarray,
irradiance_model=irradiance_model,
fast_mode_pvrow_index=fast_mode_pvrow_index)
# Fit engine
eng.fit(timestamps, DNI, DHI, params['solar_zenith'],
params['solar_azimuth'], params['surface_tilt'],
params['surface_azimuth'], params['rho_ground'])
# Run baseline calculation
pvrow_idx = 0
time_idx = 0
pvarray_loop = _fast_mode_with_loop(eng.pvarray, eng.irradiance,
eng.vf_calculator, pvrow_idx, time_idx)
# Expected should be: 138.10421248631152
qinc_expected = pvarray_loop.pvrows[0].back.get_param_weighted('qinc')
# Run timeseries calculation
qinc = eng.run_fast_mode(
fn_build_report=lambda pvarray:
(pvarray.ts_pvrows[0].back.get_param_weighted('qinc')),
pvrow_index=0)
# Check results: value taken from loop-like fast mode
np.testing.assert_allclose(qinc, qinc_expected)
def pvfactors_engine_run(data, pvarray_parameters, parallel=0, mode='full'):
"""My wrapper function to launch the pvfactors engine in parallel. It is mostly for Windows use.
In Linux you can directly call run_parallel_engine. It uses MyReportBuilder to generate the output.
Args:
data (pandas DataFrame): The data to fit the model.
pvarray_parameters (dict): The pvfactors dict describing the simulation.
parallel (int, optional): Number of threads to launch. Defaults to 0 (just calls PVEngine.run_all_timesteps)
mode (str): full or fast depending on the type of back irraadiances. See pvfactors doc.
Returns:
pandas DataFrame: The results of the simulation, as desired in MyReportBuilder.
"""
n, row = _get_cut(pvarray_parameters['cut'])
rb = Report(n, row)
if parallel > 1:
report = run_parallel_engine(rb,
pvarray_parameters,
data.index,
data.dni,
data.dhi,
data.zenith,
data.azimuth,
data.surface_tilt,
data.surface_azimuth,
data.albedo,
n_processes=parallel)
else:
pvarray = OrderedPVArray.init_from_dict(pvarray_parameters)
engine = PVEngine(pvarray)
engine.fit(data.index, data.dni, data.dhi, data.zenith, data.azimuth,
data.surface_tilt, data.surface_azimuth, data.albedo,
data.ghi)
if mode == 'full': report = engine.run_full_mode(rb.build)
else:
report = engine.run_fast_mode(rb.build,
pvrow_index=0,
segment_index=0)
df_report = pd.DataFrame(report, index=data.index).fillna(0)
return df_report
