def test_cloud_cover_to_ghi_linear():
amodel = GFS()
ghi_clear = 1000
offset = 25
out = amodel.cloud_cover_to_ghi_linear(0, ghi_clear, offset=offset)
assert_allclose(out, 1000)
out = amodel.cloud_cover_to_ghi_linear(100, ghi_clear, offset=offset)
assert_allclose(out, 250)
def test_cloud_cover_to_ghi_linear():
amodel = GFS()
ghi_clear = 1000
offset = 25
out = amodel.cloud_cover_to_ghi_linear(0, ghi_clear, offset=offset)
assert_allclose(out, 1000)
out = amodel.cloud_cover_to_ghi_linear(100, ghi_clear, offset=offset)
assert_allclose(out, 250)
def test_cloud_cover_to_ghi_linear():
with pytest.warns(pvlibDeprecationWarning):
amodel = GFS()
ghi_clear = 1000
offset = 25
out = amodel.cloud_cover_to_ghi_linear(0, ghi_clear, offset=offset)
assert_allclose(out, 1000)
out = amodel.cloud_cover_to_ghi_linear(100, ghi_clear, offset=offset)
assert_allclose(out, 250)
def _make_pv_forecast(self, forecast) -> pd.DataFrame:
"""Compile the forecast required for PV generation prediction
Uses pvlib to generate solar irradiance predictions.
Arguments:
forecast {pandas.DataFrame} -- DarkSky originated forecast
"""
# Annoyingly, the PV & wind libraries want temperature named differently
pv_forecast = forecast.rename(columns={
'temperature': 'air_temp',
'windSpeed': 'wind_speed',
})
# Use PV lib to get insolation based on the cloud cover reported here
model = GFS()
# Next up, we get hourly solar irradiance using interpolated cloud cover
# We can get this from the clearsky GHI...
if tables in sys.modules:
# We can use Ineichen clear sky model (uses pytables for turbidity)
clearsky = self.pv_location.get_clearsky(pv_forecast.index)
else:
# We can't, so use 'Simplified Solis'
clearsky = self.pv_location.get_clearsky(pv_forecast.index,
model='simplified_solis')
# ... and by knowledge of where the sun is
solpos = self.pv_location.get_solarposition(pv_forecast.index)
ghi = model.cloud_cover_to_ghi_linear(pv_forecast['cloudCover'] * 100,
clearsky['ghi'])
dni = disc(ghi, solpos['zenith'], pv_forecast.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solpos['zenith']))
# Whump it all together and we have our forecast!
pv_forecast['dni'] = dni
pv_forecast['dhi'] = dhi
pv_forecast['ghi'] = ghi
return pv_forecast
