def test_density_hub(self):
# Test modelchain with density_model='barometric'
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine))
# Test modelchain with density_model='ideal_gas'
test_mc_2 = mc.ModelChain(wt.WindTurbine(**self.test_turbine),
density_model="ideal_gas")
# Test modelchain with density_model='interpolation_extrapolation'
test_mc_3 = mc.ModelChain(
wt.WindTurbine(**self.test_turbine),
density_model="interpolation_extrapolation",
)
# Parameters for tests
temperature_2m = np.array([[267], [268]])
temperature_10m = np.array([[267], [266]])
pressure_0m = np.array([[101125], [101000]])
weather_df = pd.DataFrame(
np.hstack((temperature_2m, temperature_10m, pressure_0m)),
index=[0, 1],
columns=[
np.array(["temperature", "temperature", "pressure"]),
np.array([2, 10, 0]),
],
)
# temperature_10m is closer to hub height than temperature_2m
rho_exp = pd.Series(data=[1.30591, 1.30919])
assert_series_equal(test_mc.density_hub(weather_df), rho_exp)
rho_exp = pd.Series(data=[1.30595575725, 1.30923554056])
assert_series_equal(test_mc_2.density_hub(weather_df), rho_exp)
# change heights of temperatures so that old temperature_2m is now used
weather_df.columns = [
np.array(["temperature", "temperature", "pressure"]),
np.array([10, 200, 0]),
]
rho_exp = pd.Series(data=[1.30591, 1.29940])
assert_series_equal(test_mc.density_hub(weather_df), rho_exp)
rho_exp = pd.Series(data=[1.30595575725, 1.29944375221])
assert_series_equal(test_mc_2.density_hub(weather_df), rho_exp)
# temperature at hub height
weather_df.columns = [
np.array(["temperature", "temperature", "pressure"]),
np.array([100, 10, 0]),
]
rho_exp = pd.Series(data=[1.30305, 1.29657])
assert_series_equal(test_mc.density_hub(weather_df), rho_exp)
# density interpolation
density_10m = np.array([[1.30591], [1.29940]])
density_150m = np.array([[1.30305], [1.29657]])
weather_df = pd.DataFrame(
np.hstack((density_10m, density_150m)),
index=[0, 1],
columns=[np.array(["density", "density"]),
np.array([10, 150])],
)
rho_exp = pd.Series(data=[1.304071, 1.297581])
assert_series_equal(test_mc_3.density_hub(weather_df), rho_exp)
def test_wind_speed_hub(self):
# Test modelchain with wind_speed_model='logarithmic'
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine))
# Test modelchain with wind_speed_model='hellman'
test_mc_2 = mc.ModelChain(wt.WindTurbine(**self.test_turbine),
wind_speed_model="hellman")
# Test modelchain with wind_speed_model='interpolation_extrapolation'
test_mc_3 = mc.ModelChain(
wt.WindTurbine(**self.test_turbine),
wind_speed_model="interpolation_extrapolation",
)
# Test modelchain with
# wind_speed_model='log_interpolation_extrapolation'
test_mc_4 = mc.ModelChain(
wt.WindTurbine(**self.test_turbine),
wind_speed_model="log_interpolation_extrapolation",
)
# Parameters for tests
wind_speed_8m = np.array([[4.0], [5.0]])
wind_speed_10m = np.array([[5.0], [6.5]])
roughness_length = np.array([[0.15], [0.15]])
weather_df = pd.DataFrame(
np.hstack((wind_speed_8m, wind_speed_10m, roughness_length)),
index=[0, 1],
columns=[
np.array(["wind_speed", "wind_speed", "roughness_length"]),
np.array([8, 10, 0]),
],
)
# wind_speed_10m is closer to hub height than wind_speed_8m
v_wind_exp = pd.Series(data=[7.74137, 10.06377])
assert_series_equal(test_mc.wind_speed_hub(weather_df), v_wind_exp)
v_wind_exp = pd.Series(data=[7.12462, 9.26201])
assert_series_equal(test_mc_2.wind_speed_hub(weather_df), v_wind_exp)
v_wind_exp = pd.Series(data=[50.0, 74.0])
assert_series_equal(test_mc_3.wind_speed_hub(weather_df), v_wind_exp)
v_wind_exp = pd.Series(data=[15.3188511585, 21.9782767378])
assert_series_equal(test_mc_4.wind_speed_hub(weather_df), v_wind_exp)
# wind_speed is given at hub height
weather_df.columns = [
np.array(["wind_speed", "wind_speed", "roughness_length"]),
np.array([10, 100, 0]),
]
v_wind_exp = pd.Series(data=[5.0, 6.5], name=100)
assert_series_equal(test_mc.wind_speed_hub(weather_df), v_wind_exp)
def test_heigths_as_string(self):
"""Test run_model if data heights are of type string."""
test_turbine = {
"hub_height": 100,
"rotor_diameter": 80,
"turbine_type": "E-126/4200",
}
# Convert data heights to str
string_weather = self.weather_df.copy()
string_weather.columns = pd.MultiIndex.from_arrays([
string_weather.columns.get_level_values(0),
string_weather.columns.get_level_values(1).astype(str),
])
# Heights in the original DataFrame are of type np.int64
assert isinstance(
self.weather_df.columns.get_level_values(1)[0], np.int64)
assert isinstance(string_weather.columns.get_level_values(1)[0], str)
test_modelchain = {
"power_output_model": "power_curve",
"density_corr": True,
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(string_weather)
def test_wrong_density_correction_type(self):
"""Raise TypeErrors due to wrong type of `density_correction`"""
with pytest.raises(TypeError):
test_modelchain = {'power_output_model': 'power_curve',
'density_correction': 'wrong_type'}
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
def test_wrong_spelling_wind_speed_model(self):
"""Raise ValueErrors due to wrong spelling of wind_speed_model"""
with pytest.raises(ValueError):
test_modelchain = {'wind_speed_model': 'wrong_spelling',
'power_output_model': 'power_coefficient_curve',
'density_correction': False}
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
def test_temperature_hub(self):
# Test modelchain with temperature_model='linear_gradient'
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine))
# Test modelchain with temperature_model='interpolation_extrapolation'
test_mc_2 = mc.ModelChain(
wt.WindTurbine(**self.test_turbine),
temperature_model="interpolation_extrapolation",
)
# Parameters for tests
temperature_2m = np.array([[267], [268]])
temperature_10m = np.array([[267], [266]])
weather_df = pd.DataFrame(
np.hstack((temperature_2m, temperature_10m)),
index=[0, 1],
columns=[
np.array(["temperature", "temperature"]),
np.array([2, 10]),
],
)
# temperature_10m is closer to hub height than temperature_2m
temp_exp = pd.Series(data=[266.415, 265.415], name=10)
assert_series_equal(test_mc.temperature_hub(weather_df), temp_exp)
temp_exp = pd.Series(data=[267.0, 243.5])
assert_series_equal(test_mc_2.temperature_hub(weather_df), temp_exp)
# change heights of temperatures so that old temperature_2m is now used
weather_df.columns = [
np.array(["temperature", "temperature"]),
np.array([10, 200]),
]
temp_exp = pd.Series(data=[266.415, 267.415], name=10)
assert_series_equal(test_mc.temperature_hub(weather_df), temp_exp)
temp_exp = pd.Series(data=[267.0, 267.052632])
assert_series_equal(test_mc_2.temperature_hub(weather_df), temp_exp)
# temperature at hub height
weather_df.columns = [
np.array(["temperature", "temperature"]),
np.array([100, 10]),
]
temp_exp = pd.Series(data=[267, 268], name=100)
assert_series_equal(test_mc.temperature_hub(weather_df), temp_exp)
def test_wrong_spelling_power_output_model(self):
"""Raise ValueErrors due to wrong spelling of power_output_model"""
with pytest.raises(ValueError):
test_modelchain = {
"wind_speed_model": "hellman",
"power_output_model": "wrong_spelling",
"density_correction": False,
}
test_mc = mc.ModelChain(wt.WindTurbine(**self.test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
def test_with_default_parameter(self):
"""Test with default parameters of modelchain (power curve)"""
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'turbine_type': 'E-126/4200'}
power_output_exp = pd.Series(data=[1637405.4840444783,
3154438.3894902095],
name='feedin_power_plant')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine))
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_missing_p_values(self):
"""Raise TypeErrors due to missing p-values"""
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'turbine_type': 'E-126/4205'}
msg = "Power curve values of"
with pytest.raises(TypeError, match=msg):
test_modelchain = {'power_output_model': 'power_curve',
'density_corr': True}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
def test_with_density_corrected_power_curve_and_hellman(self):
"""Test with density corrected power curve and hellman"""
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'turbine_type': 'E-126/4200'}
test_modelchain = {'wind_speed_model': 'hellman',
'power_output_model': 'power_curve',
'density_correction': True}
power_output_exp = pd.Series(data=[1366958.544547462,
2823402.837201821],
name='feedin_power_plant')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_with_power_coefficient_curve_and_hellman(self):
"""Test with power coefficient curve and hellman"""
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'turbine_type': 'E-126/4200'}
power_output_exp = pd.Series(data=[534137.5112701517,
1103611.1736067757],
name='feedin_power_plant')
test_modelchain = {'wind_speed_model': 'hellman',
'power_output_model': 'power_coefficient_curve',
'density_correction': False}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_missing_p_values(self):
"""Raise TypeErrors due to missing p-values"""
test_turbine = {
"hub_height": 100,
"rotor_diameter": 80,
"turbine_type": "E-126/4205",
}
msg = "Power curve values of"
with pytest.raises(TypeError, match=msg):
test_modelchain = {
"power_output_model": "power_curve",
"density_corr": True,
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
def test_modelchain_with_power_curve_as_dict(self):
"""Test power curves as dict"""
my_turbine = {'nominal_power': 3e6, 'hub_height': 105,
'rotor_diameter': 70,
'power_curve': {
'value': [p * 1000 for p in [
0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],
'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]},
'power_coefficient_curve': {
'value': [0.0, 0.43, 0.45, 0.35, 0.12, 0.03],
'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]}}
power_output_exp = pd.Series(data=[919055.54840,
1541786.60559],
name='feedin_power_plant')
test_mc = mc.ModelChain(wt.WindTurbine(**my_turbine))
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_with_power_coefficient_curve_and_hellman(self):
"""Test with power coefficient curve and hellman"""
test_turbine = {
"hub_height": 100,
"rotor_diameter": 80,
"turbine_type": "E-126/4200",
}
power_output_exp = pd.Series(
data=[534137.5112701517, 1103611.1736067757],
name="feedin_power_plant",
)
test_modelchain = {
"wind_speed_model": "hellman",
"power_output_model": "power_coefficient_curve",
"density_correction": False,
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_weather_with_nan_values(self, recwarn):
"""Test warning if weather data contain nan values."""
test_turbine = {
"hub_height": 100,
"rotor_diameter": 80,
"turbine_type": "E-126/4200",
}
nan_weather = self.weather_df.copy()
nan_weather.loc[1, ("temperature", 10)] = np.nan
test_modelchain = {
"power_output_model": "power_curve",
"density_corr": True,
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
msg = "'temperature', 10"
with pytest.warns(WindpowerlibUserWarning, match=msg):
test_mc.run_model(nan_weather)
test_mc.run_model(self.weather_df)
assert len(recwarn) == 0
def test_with_density_corrected_power_curve_and_hellman(self):
"""Test with density corrected power curve and hellman"""
test_turbine = {
"hub_height": 100,
"rotor_diameter": 80,
"turbine_type": "E-126/4200",
}
test_modelchain = {
"wind_speed_model": "hellman",
"power_output_model": "power_curve",
"density_correction": True,
}
power_output_exp = pd.Series(
data=[1366958.544547462, 2823402.837201821],
name="feedin_power_plant",
)
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_modelchain_with_power_curve_as_dict(self):
"""Test power curves as dict"""
my_turbine = {
"nominal_power": 3e6,
"hub_height": 105,
"rotor_diameter": 70,
"power_curve": {
"value":
[p * 1000 for p in [0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],
"wind_speed": [0.0, 3.0, 5.0, 10.0, 15.0, 25.0],
},
"power_coefficient_curve": {
"value": [0.0, 0.43, 0.45, 0.35, 0.12, 0.03],
"wind_speed": [0.0, 3.0, 5.0, 10.0, 15.0, 25.0],
},
}
power_output_exp = pd.Series(data=[919055.54840, 1541786.60559],
name="feedin_power_plant")
test_mc = mc.ModelChain(wt.WindTurbine(**my_turbine))
test_mc.run_model(self.weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
def test_run_model(self):
temperature_2m = np.array([[267], [268]])
temperature_10m = np.array([[267], [266]])
pressure_0m = np.array([[101125], [101000]])
wind_speed_8m = np.array([[4.0], [5.0]])
wind_speed_10m = np.array([[5.0], [6.5]])
roughness_length = np.array([[0.15], [0.15]])
weather_df = pd.DataFrame(np.hstack(
(temperature_2m, temperature_10m, pressure_0m, wind_speed_8m,
wind_speed_10m, roughness_length)),
index=[0, 1],
columns=[
np.array([
'temperature', 'temperature',
'pressure', 'wind_speed',
'wind_speed', 'roughness_length'
]),
np.array([2, 10, 0, 8, 10, 0])
])
test_turbine = {
'hub_height': 100,
'rotor_diameter': 80,
'turbine_name': 'ENERCON E 126 7500',
'fetch_curve': 'power_curve'
}
# Test with default parameters of modelchain (power curve)
power_output_exp = pd.Series(data=[1731887.39768, 3820152.27489],
name='feedin_wind_turbine')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine))
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Test with density corrected power curve and hellman
test_modelchain = {
'wind_speed_model': 'hellman',
'power_output_model': 'power_curve',
'density_correction': True
}
power_output_exp = pd.Series(data=[1433937.37959, 3285183.55084],
name='feedin_wind_turbine')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Test with power coefficient curve and hellman
power_output_exp = pd.Series(data=[559060.36156, 1251143.98621],
name='feedin_wind_turbine')
test_turbine['fetch_curve'] = 'power_coefficient_curve'
test_modelchain = {
'wind_speed_model': 'hellman',
'power_output_model': 'power_coefficient_curve',
'density_correction': False
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Ideal gas equation and density corrected power coefficient curve
power_output_exp = pd.Series(data=[569117.952419, 1302746.06501],
name='feedin_wind_turbine')
test_modelchain = {
'wind_speed_model': 'hellman',
'density_model': 'ideal_gas',
'power_output_model': 'power_coefficient_curve',
'density_correction': True
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Raise ValueErrors due to wrong spelling of parameters
with pytest.raises(ValueError):
test_modelchain['power_output_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['density_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['temperature_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['wind_speed_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
# Raise TypeErrors due to wrong type of `density_correction`
with pytest.raises(TypeError):
test_modelchain = {
'power_output_model': 'power_curve',
'density_correction': 'wrong_type'
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(TypeError):
test_modelchain = {
'power_output_model': 'power_coefficient_curve',
'density_correction': 'wrong_type'
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
# Raise TypeErrors due to missing cp- or p-values
with pytest.raises(TypeError):
test_turbine = {
'hub_height': 100,
'rotor_diameter': 80,
'turbine_name': 'ENERCON E 126 7500',
'fetch_curve': 'power_curve'
}
test_modelchain = {
'power_output_model': 'power_coefficient_curve',
'density_correction': True
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(TypeError):
test_turbine = {
'hub_height': 100,
'rotor_diameter': 80,
'turbine_name': 'ENERCON E 126 7500',
'fetch_curve': 'power_coefficient_curve'
}
test_modelchain = {
'power_output_model': 'power_curve',
'density_corr': True
}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
def test_run_model(self):
temperature_2m = np.array([[267], [268]])
temperature_10m = np.array([[267], [266]])
pressure_0m = np.array([[101125], [101000]])
wind_speed_8m = np.array([[4.0], [5.0]])
wind_speed_10m = np.array([[5.0], [6.5]])
roughness_length = np.array([[0.15], [0.15]])
weather_df = pd.DataFrame(np.hstack((temperature_2m,
temperature_10m,
pressure_0m,
wind_speed_8m,
wind_speed_10m,
roughness_length)),
index=[0, 1],
columns=[np.array(['temperature',
'temperature',
'pressure',
'wind_speed',
'wind_speed',
'roughness_length']),
np.array([2, 10, 0, 8, 10, 0])])
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'name': 'E-126/4200',
'fetch_curve': 'power_curve'}
# Test with default parameters of modelchain (power curve)
power_output_exp = pd.Series(data=[1637405.4840444783,
3154438.3894902095],
name='feedin_power_plant')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine))
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Test with density corrected power curve and hellman
test_modelchain = {'wind_speed_model': 'hellman',
'power_output_model': 'power_curve',
'density_correction': True}
power_output_exp = pd.Series(data=[1366958.544547462,
2823402.837201821],
name='feedin_power_plant')
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Test with power coefficient curve and hellman
power_output_exp = pd.Series(data=[534137.5112701517,
1103611.1736067757],
name='feedin_power_plant')
test_turbine['fetch_curve'] = 'power_coefficient_curve'
test_modelchain = {'wind_speed_model': 'hellman',
'power_output_model': 'power_coefficient_curve',
'density_correction': False}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
assert_series_equal(test_mc.power_output, power_output_exp)
# Raise ValueErrors due to wrong spelling of parameters
with pytest.raises(ValueError):
test_modelchain['power_output_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['density_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['temperature_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(ValueError):
test_modelchain['wind_speed_model'] = 'wrong_spelling'
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
# Raise TypeErrors due to wrong type of `density_correction`
with pytest.raises(TypeError):
test_modelchain = {'power_output_model': 'power_curve',
'density_correction': 'wrong_type'}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
# Raise TypeErrors due to missing cp- or p-values
with pytest.raises(TypeError):
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'name': 'E-126/4200',
'fetch_curve': 'power_curve'}
test_modelchain = {'power_output_model': 'power_coefficient_curve',
'density_correction': True}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
with pytest.raises(TypeError):
test_turbine = {'hub_height': 100,
'rotor_diameter': 80,
'name': 'E-126/4200',
'fetch_curve': 'power_coefficient_curve'}
test_modelchain = {'power_output_model': 'power_curve',
'density_corr': True}
test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
**test_modelchain)
test_mc.run_model(weather_df)
