def test_infer_dc_model(system, cec_dc_snl_ac_system, pvsyst_dc_snl_ac_system,
pvwatts_dc_pvwatts_ac_system, location, dc_model,
weather, mocker):
dc_systems = {'sapm': system,
'cec': cec_dc_snl_ac_system,
'desoto': cec_dc_snl_ac_system,
'pvsyst': pvsyst_dc_snl_ac_system,
'singlediode': cec_dc_snl_ac_system,
'pvwatts_dc': pvwatts_dc_pvwatts_ac_system}
dc_model_function = {'sapm': 'sapm',
'cec': 'calcparams_cec',
'desoto': 'calcparams_desoto',
'pvsyst': 'calcparams_pvsyst',
'singlediode': 'calcparams_desoto',
'pvwatts_dc': 'pvwatts_dc'}
system = dc_systems[dc_model]
# remove Adjust from model parameters for desoto, singlediode
if dc_model in ['desoto', 'singlediode']:
system.module_parameters.pop('Adjust')
m = mocker.spy(system, dc_model_function[dc_model])
mc = ModelChain(system, location,
aoi_model='no_loss', spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.dc, (pd.Series, pd.DataFrame))
def test_aoi_model_no_loss(system, location, weather):
mc = ModelChain(system, location, dc_model='sapm',
aoi_model='no_loss', spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert mc.aoi_modifier == 1.0
assert not mc.ac.empty
assert mc.ac[0] > 150 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_ac_model_user_func(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'acdc')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, ac_model=acdc,
aoi_model='no_loss', spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert_series_equal(mc.ac, mc.dc)
assert not mc.ac.empty
def test_dc_model_user_func(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'poadc')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model=poadc,
aoi_model='no_loss', spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert not mc.ac.empty
def test_aoi_models(system, location, aoi_model, method, weather, mocker):
mc = ModelChain(system, location, dc_model='sapm',
aoi_model=aoi_model, spectral_model='no_loss')
m = mocker.spy(system, method)
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, pd.Series)
assert not mc.ac.empty
assert mc.ac[0] > 150 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_losses_models_no_loss(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(pvsystem, 'pvwatts_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model='pvwatts',
aoi_model='no_loss', spectral_model='no_loss',
losses_model='no_loss')
assert mc.losses_model == mc.no_extra_losses
mc.run_model(weather.index, weather=weather)
assert m.call_count == 0
assert mc.losses == 1
def test_aoi_model_user_func(system, location, weather, mocker):
m = mocker.spy(sys.modules[__name__], 'constant_aoi_loss')
mc = ModelChain(system, location, dc_model='sapm',
aoi_model=constant_aoi_loss, spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert mc.aoi_modifier == 0.9
assert not mc.ac.empty
assert mc.ac[0] > 140 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_losses_models_ext_def(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'constant_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model='pvwatts',
aoi_model='no_loss', spectral_model='no_loss',
losses_model=constant_losses)
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert mc.losses == 0.9
assert not mc.ac.empty
def test_run_model_tracker(system, location, weather, mocker):
system = SingleAxisTracker(module_parameters=system.module_parameters,
inverter_parameters=system.inverter_parameters)
mocker.spy(system, 'singleaxis')
mc = ModelChain(system, location)
mc.run_model(weather.index, weather=weather)
assert system.singleaxis.call_count == 1
assert (mc.tracking.columns == ['tracker_theta', 'aoi', 'surface_azimuth',
'surface_tilt']).all()
assert mc.ac[0] > 0
assert np.isnan(mc.ac[1])
def test_aoi_model_no_loss(system, location, weather):
mc = ModelChain(system,
location,
dc_model='sapm',
aoi_model='no_loss',
spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert mc.aoi_modifier == 1.0
assert not mc.ac.empty
assert mc.ac[0] > 150 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_run_model_tracker(system, location, weather, mocker):
system = SingleAxisTracker(module_parameters=system.module_parameters,
inverter_parameters=system.inverter_parameters)
mocker.spy(system, 'singleaxis')
mc = ModelChain(system, location)
mc.run_model(weather.index, weather=weather)
assert system.singleaxis.call_count == 1
assert (mc.tracking.columns == ['tracker_theta', 'aoi', 'surface_azimuth',
'surface_tilt']).all()
assert mc.ac[0] > 0
assert np.isnan(mc.ac[1])
def test_run_model_times(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
irradiance = pd.DataFrame({
'dni': 900,
'ghi': 600,
'dhi': 150
},
index=times)
with pytest.warns(pvlibDeprecationWarning):
mc.run_model(irradiance, times=times)
def test_losses_models_ext_def(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'constant_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model='pvwatts',
aoi_model='no_loss', spectral_model='no_loss',
losses_model=constant_losses)
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert mc.losses == 0.9
assert not mc.ac.empty
def test_run_model_with_weather(system, location, weather, mocker):
mc = ModelChain(system, location)
m = mocker.spy(system, 'sapm_celltemp')
weather['wind_speed'] = 5
weather['temp_air'] = 10
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
# assert_called_once_with cannot be used with series, so need to use
# assert_series_equal on call_args
assert_series_equal(m.call_args[0][1], weather['wind_speed']) # wind
assert_series_equal(m.call_args[0][2], weather['temp_air']) # temp
assert not mc.ac.empty
def test_weather_irradiance_input(system, location):
"""Test will raise a warning and should be removed in future versions."""
mc = ModelChain(system, location)
times = pd.date_range('2012-06-01 12:00:00', periods=2, freq='H')
i = pd.DataFrame({'dni': [2, 3], 'dhi': [4, 6], 'ghi': [9, 5]}, index=times)
w = pd.DataFrame({'wind_speed': [11, 5], 'temp_air': [30, 32]}, index=times)
mc.run_model(times, irradiance=i, weather=w)
assert_series_equal(mc.weather['dni'],
pd.Series([2, 3], index=times, name='dni'))
assert_series_equal(mc.weather['wind_speed'],
pd.Series([11, 5], index=times, name='wind_speed'))
def test_run_model_with_weather(system, location, weather, mocker):
mc = ModelChain(system, location)
m = mocker.spy(system, 'sapm_celltemp')
weather['wind_speed'] = 5
weather['temp_air'] = 10
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
# assert_called_once_with cannot be used with series, so need to use
# assert_series_equal on call_args
assert_series_equal(m.call_args[0][1], weather['wind_speed']) # wind
assert_series_equal(m.call_args[0][2], weather['temp_air']) # temp
assert not mc.ac.empty
def download_forecasts(request):
modules_per_string = request.session['modules_per_string']
strings_per_inverter = request.session['strings_per_inverter']
module = request.session['module']
inverter = request.session['inverter']
latitude = request.session['latitude']
longitude = request.session['longitude']
tz = request.session['timezone']
sandia_modules = retrieve_sam('SandiaMod')
cec_inverters = retrieve_sam('SandiaInverter')
# Parametros de la granja solar
surface_tilt = 30
surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention
albedo = 0.2
# Rango de tiempo
start = pd.Timestamp(date.today(), tz=tz)
# Pronostico a 3 días en adelante
end = start + pd.Timedelta(days=3)
module = pd.Series(sandia_modules[module])
inverter = cec_inverters[inverter]
# model a big tracker for more fun
system = SingleAxisTracker(module_parameters=module,
inverter_parameters=inverter,
modules_per_string=modules_per_string,
strings_per_inverter=strings_per_inverter)
# fx is a common abbreviation for forecast
fx_model = GFS()
fx_data = fx_model.get_processed_data(latitude, longitude, start, end)
# use a ModelChain object to calculate modeling intermediates
mc = ModelChain(system, fx_model.location)
# extract relevant data for model chain
mc.run_model(fx_data.index, weather=fx_data)
AC = mc.ac.fillna(0)
response = HttpResponse(content_type='text/csv')
response[
'Content-Disposition'] = 'attachment; filename=AC_5days_forecasts.csv'
AC.to_csv(path_or_buf=response,
sep=';',
float_format='%.2f',
index=True,
decimal=",")
return response
def test_weather_irradiance_input(system, location):
"""Test will raise a warning and should be removed in future versions."""
mc = ModelChain(system, location)
times = pd.date_range('2012-06-01 12:00:00', periods=2, freq='H')
i = pd.DataFrame({'dni': [2, 3], 'dhi': [4, 6], 'ghi': [9, 5]}, index=times)
w = pd.DataFrame({'wind_speed': [11, 5], 'temp_air': [30, 32]}, index=times)
mc.run_model(times, irradiance=i, weather=w)
assert_series_equal(mc.weather['dni'],
pd.Series([2, 3], index=times, name='dni'))
assert_series_equal(mc.weather['wind_speed'],
pd.Series([11, 5], index=times, name='wind_speed'))
def test_infer_dc_model(system, cec_dc_snl_ac_system, pvsyst_dc_snl_ac_system,
pvwatts_dc_pvwatts_ac_system, location, dc_model,
weather, mocker):
dc_systems = {
'sapm': system,
'cec': cec_dc_snl_ac_system,
'desoto': cec_dc_snl_ac_system,
'pvsyst': pvsyst_dc_snl_ac_system,
'singlediode': cec_dc_snl_ac_system,
'pvwatts_dc': pvwatts_dc_pvwatts_ac_system
}
dc_model_function = {
'sapm': 'sapm',
'cec': 'calcparams_cec',
'desoto': 'calcparams_desoto',
'pvsyst': 'calcparams_pvsyst',
'singlediode': 'calcparams_desoto',
'pvwatts_dc': 'pvwatts_dc'
}
temp_model_function = {
'sapm': 'sapm',
'cec': 'sapm',
'desoto': 'sapm',
'pvsyst': 'pvsyst',
'singlediode': 'sapm',
'pvwatts_dc': 'sapm'
}
temp_model_params = {
'sapm': {
'a': -3.40641,
'b': -0.0842075,
'deltaT': 3
},
'pvsyst': {
'u_c': 29.0,
'u_v': 0
}
}
system = dc_systems[dc_model]
system.temperature_model_parameters = temp_model_params[
temp_model_function[dc_model]]
# remove Adjust from model parameters for desoto, singlediode
if dc_model in ['desoto', 'singlediode']:
system.module_parameters.pop('Adjust')
m = mocker.spy(system, dc_model_function[dc_model])
mc = ModelChain(system,
location,
aoi_model='no_loss',
spectral_model='no_loss',
temperature_model=temp_model_function[dc_model])
mc.run_model(weather)
assert m.call_count == 1
assert isinstance(mc.dc, (pd.Series, pd.DataFrame))
def test_dc_model_user_func(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'poadc')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system,
location,
dc_model=poadc,
aoi_model='no_loss',
spectral_model='no_loss')
mc.run_model(weather)
assert m.call_count == 1
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert not mc.ac.empty
def test_ac_model_user_func(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(sys.modules[__name__], 'acdc')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system,
location,
ac_model=acdc,
aoi_model='no_loss',
spectral_model='no_loss')
mc.run_model(weather)
assert m.call_count == 1
assert_series_equal(mc.ac, mc.dc)
assert not mc.ac.empty
def test_infer_dc_model(system, cec_dc_snl_ac_system,
pvwatts_dc_pvwatts_ac_system, location, dc_model,
mocker):
dc_systems = {'sapm': system, 'singlediode': cec_dc_snl_ac_system,
'pvwatts_dc': pvwatts_dc_pvwatts_ac_system}
system = dc_systems[dc_model]
m = mocker.spy(system, dc_model)
mc = ModelChain(system, location,
aoi_model='no_loss', spectral_model='no_loss')
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
mc.run_model(times)
assert m.call_count == 1
assert isinstance(mc.dc, (pd.Series, pd.DataFrame))
def test_aoi_models(system, location, aoi_model, weather, mocker):
mc = ModelChain(system,
location,
dc_model='sapm',
aoi_model=aoi_model,
spectral_model='no_loss')
m = mocker.spy(system, 'get_iam')
mc.run_model(weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, pd.Series)
assert not mc.ac.empty
assert mc.ac[0] > 150 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_losses_models_pvwatts(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
age = 1
pvwatts_dc_pvwatts_ac_system.losses_parameters = dict(age=age)
m = mocker.spy(pvsystem, 'pvwatts_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model='pvwatts',
aoi_model='no_loss', spectral_model='no_loss',
losses_model='pvwatts')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
m.assert_called_with(age=age)
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert not mc.ac.empty
def test_run_model_with_weather_fuentes_temp(sapm_dc_snl_ac_system, location,
weather, mocker):
weather['wind_speed'] = 5
weather['temp_air'] = 10
sapm_dc_snl_ac_system.temperature_model_parameters = {'noct_installed': 45}
mc = ModelChain(sapm_dc_snl_ac_system, location)
mc.temperature_model = 'fuentes'
m_fuentes = mocker.spy(sapm_dc_snl_ac_system, 'fuentes_celltemp')
mc.run_model(weather)
assert m_fuentes.call_count == 1
assert_series_equal(m_fuentes.call_args[0][1], weather['temp_air'])
assert_series_equal(m_fuentes.call_args[0][2], weather['wind_speed'])
assert not mc.ac.empty
def test_losses_models_no_loss(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
m = mocker.spy(pvsystem, 'pvwatts_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system,
location,
dc_model='pvwatts',
aoi_model='no_loss',
spectral_model='no_loss',
losses_model='no_loss')
assert mc.losses_model == mc.no_extra_losses
mc.run_model(weather)
assert m.call_count == 0
assert mc.losses == 1
def test_losses_models_pvwatts(pvwatts_dc_pvwatts_ac_system, location, weather,
mocker):
age = 1
pvwatts_dc_pvwatts_ac_system.losses_parameters = dict(age=age)
m = mocker.spy(pvsystem, 'pvwatts_losses')
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, dc_model='pvwatts',
aoi_model='no_loss', spectral_model='no_loss',
losses_model='pvwatts')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
m.assert_called_with(age=age)
assert isinstance(mc.ac, (pd.Series, pd.DataFrame))
assert not mc.ac.empty
def test_aoi_model_user_func(system, location, weather, mocker):
m = mocker.spy(sys.modules[__name__], 'constant_aoi_loss')
mc = ModelChain(system,
location,
dc_model='sapm',
aoi_model=constant_aoi_loss,
spectral_model='no_loss')
mc.run_model(weather)
assert m.call_count == 1
assert mc.aoi_modifier == 0.9
assert not mc.ac.empty
assert mc.ac[0] > 140 and mc.ac[0] < 200
assert mc.ac[1] < 1
def test_run_model_with_weather_sapm_temp(sapm_dc_snl_ac_system, location,
weather, mocker):
# test with sapm cell temperature model
weather['wind_speed'] = 5
weather['temp_air'] = 10
mc = ModelChain(sapm_dc_snl_ac_system, location)
mc.temperature_model = 'sapm'
m_sapm = mocker.spy(sapm_dc_snl_ac_system, 'sapm_celltemp')
mc.run_model(weather)
assert m_sapm.call_count == 1
# assert_called_once_with cannot be used with series, so need to use
# assert_series_equal on call_args
assert_series_equal(m_sapm.call_args[0][1], weather['temp_air']) # temp
assert_series_equal(m_sapm.call_args[0][2], weather['wind_speed']) # wind
assert not mc.ac.empty
def test_ac_models(system, cec_dc_adr_ac_system, pvwatts_dc_pvwatts_ac_system,
location, ac_model, weather, mocker):
ac_systems = {'snlinverter': system, 'adrinverter': cec_dc_adr_ac_system,
'pvwatts': pvwatts_dc_pvwatts_ac_system}
system = ac_systems[ac_model]
mc = ModelChain(system, location, ac_model=ac_model,
aoi_model='no_loss', spectral_model='no_loss')
if ac_model == 'pvwatts':
ac_model += '_ac'
m = mocker.spy(system, ac_model)
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.ac, pd.Series)
assert not mc.ac.empty
assert mc.ac[1] < 1
def test_run_model_with_weather_faiman_temp(sapm_dc_snl_ac_system, location,
weather, mocker):
# test with faiman cell temperature model
weather['wind_speed'] = 5
weather['temp_air'] = 10
sapm_dc_snl_ac_system.temperature_model_parameters = {
'u0': 25.0, 'u1': 6.84
}
mc = ModelChain(sapm_dc_snl_ac_system, location)
mc.temperature_model = 'faiman'
m_faiman = mocker.spy(sapm_dc_snl_ac_system, 'faiman_celltemp')
mc.run_model(weather)
assert m_faiman.call_count == 1
assert_series_equal(m_faiman.call_args[0][1], weather['temp_air'])
assert_series_equal(m_faiman.call_args[0][2], weather['wind_speed'])
assert not mc.ac.empty
def test_run_model_with_weather_pvsyst_temp(sapm_dc_snl_ac_system, location,
weather, mocker):
# test with pvsyst cell temperature model
weather['wind_speed'] = 5
weather['temp_air'] = 10
sapm_dc_snl_ac_system.racking_model = 'freestanding'
sapm_dc_snl_ac_system.temperature_model_parameters = \
temperature._temperature_model_params('pvsyst', 'freestanding')
mc = ModelChain(sapm_dc_snl_ac_system, location)
mc.temperature_model = 'pvsyst'
m_pvsyst = mocker.spy(sapm_dc_snl_ac_system, 'pvsyst_celltemp')
mc.run_model(weather)
assert m_pvsyst.call_count == 1
assert_series_equal(m_pvsyst.call_args[0][1], weather['temp_air'])
assert_series_equal(m_pvsyst.call_args[0][2], weather['wind_speed'])
assert not mc.ac.empty
def test_spectral_models(system, location, spectral_model, weather):
# add pw to weather dataframe
weather['precipitable_water'] = [0.3, 0.5]
mc = ModelChain(system, location, dc_model='sapm',
aoi_model='no_loss', spectral_model=spectral_model)
spectral_modifier = mc.run_model(weather).spectral_modifier
assert isinstance(spectral_modifier, (pd.Series, float, int))
def test_ac_models(system, cec_dc_adr_ac_system, pvwatts_dc_pvwatts_ac_system,
location, ac_model, weather, mocker):
ac_systems = {'snlinverter': system, 'adrinverter': cec_dc_adr_ac_system,
'pvwatts': pvwatts_dc_pvwatts_ac_system}
system = ac_systems[ac_model]
mc = ModelChain(system, location, ac_model=ac_model,
aoi_model='no_loss', spectral_model='no_loss')
if ac_model == 'pvwatts':
ac_model += '_ac'
m = mocker.spy(system, ac_model)
mc.run_model(weather)
assert m.call_count == 1
assert isinstance(mc.ac, pd.Series)
assert not mc.ac.empty
assert mc.ac[1] < 1
def test_spectral_models(system, location, spectral_model, expected):
mc = ModelChain(system, location, dc_model='sapm',
aoi_model='no_loss', spectral_model=spectral_model)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array(expected), index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_aoi_models(system, location, aoi_model, expected):
mc = ModelChain(system, location, dc_model='sapm',
aoi_model=aoi_model, spectral_model='no_loss')
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array(expected), index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_run_model(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array([183.522449305, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_infer_dc_model(system, cec_dc_snl_ac_system,
pvwatts_dc_pvwatts_ac_system, location, dc_model,
weather, mocker):
dc_systems = {
'sapm': system,
'singlediode': cec_dc_snl_ac_system,
'pvwatts_dc': pvwatts_dc_pvwatts_ac_system
}
system = dc_systems[dc_model]
m = mocker.spy(system, dc_model)
mc = ModelChain(system,
location,
aoi_model='no_loss',
spectral_model='no_loss')
mc.run_model(weather.index, weather=weather)
assert m.call_count == 1
assert isinstance(mc.dc, (pd.Series, pd.DataFrame))
def test_run_model(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array([ 1.82033564e+02, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_run_model_with_weather(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
weather = pd.DataFrame({'wind_speed': 5, 'temp_air': 10}, index=times)
ac = mc.run_model(times, weather=weather).ac
expected = pd.Series(np.array([201.691634921, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_run_model():
system, location = mc_setup()
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array([ 1.82033564e+02, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected)
def test_run_model_with_weather(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
weather = pd.DataFrame({'wind_speed':5, 'temp_air':10}, index=times)
ac = mc.run_model(times, weather=weather).ac
expected = pd.Series(np.array([ 1.99952400e+02, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_run_model_with_weather():
system, location = mc_setup()
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
weather = pd.DataFrame({'wind_speed':5, 'temp_air':10}, index=times)
ac = mc.run_model(times, weather=weather).ac
expected = pd.Series(np.array([ 1.99952400e+02, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected)
def test_run_model_perez(system, location):
mc = ModelChain(system, location, transposition_model='perez')
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
irradiance = pd.DataFrame({'dni': 900, 'ghi': 600, 'dhi': 150},
index=times)
ac = mc.run_model(irradiance).ac
expected = pd.Series(np.array([187.94295642, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected)
def test_run_model_with_irradiance(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
irradiance = pd.DataFrame({'dni': 900, 'ghi': 600, 'dhi': 150},
index=times)
ac = mc.run_model(irradiance).ac
expected = pd.Series(np.array([187.80746494643176, -0.02]),
index=times)
assert_series_equal(ac, expected)
def test_spectral_models(system, location, spectral_model):
times = pd.date_range('20160101 1200-0700', periods=3, freq='6H')
weather = pd.DataFrame(data=[0.3, 0.5, 1.0],
index=times,
columns=['precipitable_water'])
mc = ModelChain(system, location, dc_model='sapm',
aoi_model='no_loss', spectral_model=spectral_model)
spectral_modifier = mc.run_model(times=times,
weather=weather).spectral_modifier
assert isinstance(spectral_modifier, (pd.Series, float, int))
def test_run_model_with_irradiance(system, location):
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
irradiance = pd.DataFrame({'dni':900, 'ghi':600, 'dhi':150},
index=times)
ac = mc.run_model(times, irradiance=irradiance).ac
expected = pd.Series(np.array([ 1.90054749e+02, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected)
def test_spectral_models(system, location, spectral_model):
times = pd.date_range('20160101 1200-0700', periods=3, freq='6H')
weather = pd.DataFrame(data=[0.3, 0.5, 1.0],
index=times,
columns=['precipitable_water'])
mc = ModelChain(system, location, dc_model='sapm',
aoi_model='no_loss', spectral_model=spectral_model)
spectral_modifier = mc.run_model(times=times,
weather=weather).spectral_modifier
assert isinstance(spectral_modifier, (pd.Series, float, int))
def test_run_model_perez(system, location):
mc = ModelChain(system, location, transposition_model='perez')
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
irradiance = pd.DataFrame({'dni':900, 'ghi':600, 'dhi':150},
index=times)
ac = mc.run_model(times, irradiance=irradiance).ac
expected = pd.Series(np.array([ 190.194545796, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected)
def test_ac_models(system, cec_dc_adr_ac_system, pvwatts_dc_pvwatts_ac_system,
location, ac_model, expected):
ac_systems = {'snlinverter': system, 'adrinverter': cec_dc_adr_ac_system,
'pvwatts': pvwatts_dc_pvwatts_ac_system,
acdc: pvwatts_dc_pvwatts_ac_system}
system = ac_systems[ac_model]
mc = ModelChain(system, location, ac_model=ac_model,
aoi_model='no_loss', spectral_model='no_loss')
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array(expected), index=times)
assert_series_equal(ac, expected, check_less_precise=2)
def test_run_model_tracker(system, location):
system = SingleAxisTracker(module_parameters=system.module_parameters,
inverter_parameters=system.inverter_parameters)
mc = ModelChain(system, location)
times = pd.date_range('20160101 1200-0700', periods=2, freq='6H')
ac = mc.run_model(times).ac
expected = pd.Series(np.array([ 121.421719, -2.00000000e-02]),
index=times)
assert_series_equal(ac, expected, check_less_precise=2)
expected = pd.DataFrame(np.
array([[ 54.82513187, 90. , 11.0039221 , 11.0039221 ],
[ nan, 0. , 0. , nan]]),
columns=['aoi', 'surface_azimuth', 'surface_tilt', 'tracker_theta'],
index=times)
assert_frame_equal(mc.tracking, expected, check_less_precise=2)
}
# own location parameter
wittenberg = {
'altitude': 34,
'name': 'Wittenberg',
'latitude': my_weather.latitude,
'longitude': my_weather.longitude,
}
# the following has been implemented in the pvlib ModelChain in the
# complete_irradiance method (pvlib version > v0.4.5)
if w.get('dni') is None:
w['dni'] = (w.ghi - w.dhi) / cosd(
Location(**wittenberg).get_solarposition(times).zenith)
# pvlib's ModelChain
mc = ModelChain(PVSystem(**yingli210),
Location(**wittenberg),
orientation_strategy='south_at_latitude_tilt')
mc.run_model(times, weather=w)
if plt:
mc.dc.p_mp.fillna(0).plot()
plt.show()
else:
logging.warning("No plots shown. Install matplotlib to see the plots.")
logging.info('Done!')
