def test_normalize_with_expected_power_energy_option(pv_15, expected_15, irradiance_15):
norm, insol = normalize_with_expected_power(
pv_15, expected_15, irradiance_15, pv_input='energy')
expected_norm = pd.Series(
{Timestamp('2020-01-01 12:00:00', freq='15T'): np.nan,
Timestamp('2020-01-01 12:15:00', freq='15T'): 5.714285714285714,
Timestamp('2020-01-01 12:30:00', freq='15T'): 4.705882352941177,
Timestamp('2020-01-01 12:45:00', freq='15T'): 3.5918367346938775,
Timestamp('2020-01-01 13:00:00', freq='15T'): 4.097560975609756}
)
expected_norm.name = 'energy_Wh'
expected_norm.index.freq = '15T'
expected_insol = pd.Series(
{Timestamp('2020-01-01 12:00:00', freq='15T'): np.nan,
Timestamp('2020-01-01 12:15:00', freq='15T'): 231.25,
Timestamp('2020-01-01 12:30:00', freq='15T'): 225.0,
Timestamp('2020-01-01 12:45:00', freq='15T'): 240.625,
Timestamp('2020-01-01 13:00:00', freq='15T'): 233.125}
)
expected_insol.name = 'energy_Wh'
expected_insol.index.freq = '15T'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalized_with_expected_power_low_freq_expected(pv_15, expected_30, irradiance_30):
norm, insol = normalize_with_expected_power(
pv_15, expected_30, irradiance_30)
expected_norm = pd.Series(
{Timestamp('2020-01-01 12:15:00', freq='15T'): 1.09375,
Timestamp('2020-01-01 12:30:00', freq='15T'): 1.1458333333333335,
Timestamp('2020-01-01 12:45:00', freq='15T'): 1.0000000000000002,
Timestamp('2020-01-01 13:00:00', freq='15T'): 0.9772727272727274}
)
expected_norm.name = 'energy_Wh'
expected_norm.index.freq = '15T'
expected_insol = pd.Series(
{Timestamp('2020-01-01 12:15:00', freq='15T'): 246.875,
Timestamp('2020-01-01 12:30:00', freq='15T'): 240.625,
Timestamp('2020-01-01 12:45:00', freq='15T'): 233.75,
Timestamp('2020-01-01 13:00:00', freq='15T'): 226.25}
)
expected_insol.name = 'energy_Wh'
expected_insol.index.freq = '15T'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def _clearsky_preprocess(self):
'''
Perform clear-sky-based normalization, filtering, and aggregation.
If optional parameter self.power_expected is passed in,
normalize_with_expected_power will be used instead of pvwatts.
'''
try:
if self.poa_global_clearsky is None:
self._calc_clearsky_poa(model='isotropic')
except AttributeError:
raise AttributeError(
"No poa_global_clearsky. 'set_clearsky' must be run " +
"prior to 'clearsky_analysis'")
if self.temperature_cell_clearsky is None:
if self.temperature_ambient_clearsky is None:
self._calc_clearsky_tamb()
self.temperature_cell_clearsky = self._calc_cell_temperature(
self.poa_global_clearsky, self.temperature_ambient_clearsky, 0)
# Note example notebook uses windspeed=0 in the clearskybranch
if self.power_expected is None:
cs_normalized, cs_insolation = self._pvwatts_norm(
self.poa_global_clearsky, self.temperature_cell_clearsky)
else: # self.power_expected passed in by user
cs_normalized, cs_insolation = normalization.normalize_with_expected_power(
self.pv_energy,
self.power_expected,
self.poa_global_clearsky,
pv_input='energy')
self._filter(cs_normalized, 'clearsky')
cs_aggregated, cs_aggregated_insolation = self._aggregate(
cs_normalized[self.clearsky_filter],
cs_insolation[self.clearsky_filter])
self.clearsky_aggregated_performance = cs_aggregated
self.clearsky_aggregated_insolation = cs_aggregated_insolation
def _sensor_preprocess(self):
'''
Perform sensor-based normalization, filtering, and aggregation.
If optional parameter self.power_expected is passed in,
normalize_with_expected_power will be used instead of pvwatts.
'''
if self.poa_global is None:
raise ValueError(
'poa_global must be available to perform _sensor_preprocess')
if self.power_expected is None:
# Thermal details required if power_expected is not manually set.
if self.temperature_cell is None and self.temperature_ambient is None:
raise ValueError(
'either cell or ambient temperature must be available '
'to perform _sensor_preprocess')
if self.temperature_cell is None:
self.temperature_cell = self._calc_cell_temperature(
self.poa_global, self.temperature_ambient, self.windspeed)
energy_normalized, insolation = self._pvwatts_norm(
self.poa_global, self.temperature_cell)
else: # self.power_expected passed in by user
energy_normalized, insolation = normalization.normalize_with_expected_power(
self.pv_energy,
self.power_expected,
self.poa_global,
pv_input='energy')
self._filter(energy_normalized, 'sensor')
aggregated, aggregated_insolation = self._aggregate(
energy_normalized[self.sensor_filter],
insolation[self.sensor_filter])
self.sensor_aggregated_performance = aggregated
self.sensor_aggregated_insolation = aggregated_insolation
def test_normalize_with_expected_power_uniform_frequency(
pv_15, expected_15, irradiance_15):
norm, insol = normalize_with_expected_power(pv_15, expected_15,
irradiance_15)
expected_norm = pv_15.iloc[1:] / expected_15.iloc[1:]
expected_norm.name = 'energy_Wh'
expected_insol = irradiance_15.iloc[1:] * 0.25
expected_insol.name = 'energy_Wh'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalize_with_expected_power_energy_option(pv_15, expected_15,
irradiance_15):
norm, insol = normalize_with_expected_power(pv_15,
expected_15,
irradiance_15,
pv_input='energy')
expected_norm = pv_15 / (0.25 * expected_15.iloc[1:])
expected_norm.name = 'energy_Wh'
expected_insol = irradiance_15.iloc[1:] * 0.25
expected_insol = expected_insol.reindex(expected_norm.index)
expected_insol.name = 'energy_Wh'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalized_with_expected_power_low_freq_expected(
pv_15, expected_30, irradiance_30):
norm, insol = normalize_with_expected_power(pv_15, expected_30,
irradiance_30)
expected_15 = expected_30.reindex(pv_15.index).interpolate()
expected_energy = expected_15.iloc[1:] * 0.25
expected_norm = pv_15.iloc[1:] * 0.25 / expected_energy
expected_norm.name = 'energy_Wh'
irradiance_15 = irradiance_30.reindex(pv_15.index).interpolate()
expected_insol = irradiance_15.iloc[1:] * 0.25
expected_insol.name = 'energy_Wh'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalize_with_expected_power_low_freq_pv(pv_30, expected_15,
irradiance_15):
norm, insol = normalize_with_expected_power(pv_30, expected_15,
irradiance_15)
pv_energy = pv_30.iloc[1:] * 0.5
expected_energy = expected_15.iloc[1:] * 0.25
# aggregate to 30 min level
expected_energy = expected_energy.rolling(2).sum()
expected_energy = expected_energy.reindex(pv_energy.index)
expected_norm = pv_energy / expected_energy
expected_norm.name = 'energy_Wh'
expected_insol = irradiance_15.iloc[1:] * 0.25
# aggregate to 30 min level
expected_insol = expected_insol.rolling(2).sum()
expected_insol = expected_insol.reindex(pv_energy.index)
expected_insol.name = 'energy_Wh'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalize_with_expected_power_low_freq_pv(pv_30, expected_15, irradiance_15):
norm, insol = normalize_with_expected_power(
pv_30, expected_15, irradiance_15)
expected_norm = pd.Series(
{Timestamp('2020-01-01 12:30:00', freq='30T'): 0.9302325581395349,
Timestamp('2020-01-01 13:00:00', freq='30T'): 1.1333333333333333}
)
expected_norm.name = 'energy_Wh'
expected_norm.index.freq = '30T'
expected_insol = pd.Series(
{Timestamp('2020-01-01 12:30:00', freq='30T'): 456.25,
Timestamp('2020-01-01 13:00:00', freq='30T'): 473.75}
)
expected_insol.name = 'energy_Wh'
expected_insol.index.freq = '30T'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
def test_normalize_with_expected_power_uniform_frequency(pv_15, expected_15, irradiance_15):
norm, insol = normalize_with_expected_power(
pv_15, expected_15, irradiance_15)
expected_norm = pd.Series(
{Timestamp('2020-01-01 12:15:00', freq='15T'): 1.0,
Timestamp('2020-01-01 12:30:00', freq='15T'): 1.0784313725490198,
Timestamp('2020-01-01 12:45:00', freq='15T'): 1.0612244897959184,
Timestamp('2020-01-01 13:00:00', freq='15T'): 1.0487804878048783}
)
expected_norm.name = 'energy_Wh'
expected_norm.index.freq = '15T'
expected_insol = pd.Series(
{Timestamp('2020-01-01 12:15:00', freq='15T'): 231.25,
Timestamp('2020-01-01 12:30:00', freq='15T'): 225.0,
Timestamp('2020-01-01 12:45:00', freq='15T'): 240.625,
Timestamp('2020-01-01 13:00:00', freq='15T'): 233.125}
)
expected_insol.name = 'energy_Wh'
expected_insol.index.freq = '15T'
pd.testing.assert_series_equal(norm, expected_norm)
pd.testing.assert_series_equal(insol, expected_insol)
