def test_faiman_ndarray():
temps = np.array([0, 10, 5])
irrads = np.array([0, 500, 0])
winds = np.array([10, 5, 0])
result = temperature.faiman(irrads, temps, wind_speed=winds)
expected = np.array([0.0, 18.446, 5.0])
assert_allclose(expected, result, 3)
def test_faiman_series():
times = pd.date_range(start="2015-01-01", end="2015-01-02", freq="12H")
temps = pd.Series([0, 10, 5], index=times)
irrads = pd.Series([0, 500, 0], index=times)
winds = pd.Series([10, 5, 0], index=times)
result = temperature.faiman(irrads, temps, wind_speed=winds)
expected = pd.Series([0.0, 18.446, 5.0], index=times)
assert_series_equal(expected, result)
def test_faiman_default():
result = temperature.faiman(900, 20, 5)
assert_allclose(result, 35.203, 0.001)
def test_faiman_kwargs():
result = temperature.faiman(900, 20, wind_speed=5.0, u0=22.0, u1=6.)
assert_allclose(result, 37.308, 0.001)
pvrow_height,
pvrow_width,
albedo,
n_pvrows=3,
index_observed_pvrow=1)
# turn into pandas DataFrame
irrad = pd.concat(irrad, axis=1)
# using bifaciality factor and pvfactors results, create effective irradiance
bifaciality = 0.75
effective_irrad_bifi = irrad['total_abs_front'] + (irrad['total_abs_back'] *
bifaciality)
# get cell temperature using the Faiman model
temp_cell = temperature.faiman(effective_irrad_bifi, temp_air=25, wind_speed=1)
# using the pvwatts_dc model and parameters detailed above,
# set pdc0 and return DC power for both bifacial and monofacial
pdc0 = 1
gamma_pdc = -0.0043
pdc_bifi = pvsystem.pvwatts_dc(effective_irrad_bifi,
temp_cell,
pdc0,
gamma_pdc=gamma_pdc).fillna(0)
pdc_bifi.plot(title='Bifacial Simulation on June Solstice', ylabel='DC Power')
# %%
# For illustration, perform monofacial simulation using pvfactors front-side
# irradiance (AOI-corrected), and plot along with bifacial results.
