def test_daytime_zero_at_end_of_day(clearsky_january):
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/5/2020 16:00':'1/6/2020 00:00'] = 0
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
# test a period of zeros starting earlier in the day
ghi.loc['1/5/2020 12:00':'1/6/2020 00:00'] = 0
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
def test_daytime(clearsky_january):
_assert_daytime_no_shoulder(
clearsky_january['ghi'],
daytime.power_or_irradiance(clearsky_january['ghi']))
# punch a mid-day hole
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/10/2020 12:00':'1/10/2020 14:00'] = 0
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
def test_daytime_with_clipping(clearsky_january):
ghi = clearsky_january['ghi'].copy()
ghi.loc[ghi >= 500] = 500
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
# Include a period where data goes to zero during clipping and
# returns to normal after the clipping is done
ghi.loc[ghi['1/3/2020'].between_time('12:30', '15:30').index] = 0
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
def test_daytime_missing_data(clearsky_january):
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/5/2020 16:00':'1/6/2020 11:30'] = np.nan
# test with NaNs
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
# test with completely missing data
ghi.dropna(inplace=True)
_assert_daytime_no_shoulder(
ghi,
daytime.power_or_irradiance(ghi,
freq=pd.infer_freq(
clearsky_january.index)))
def test_daytime_split_day():
location = Location(35, -150)
clearsky = location.get_clearsky(
pd.date_range(start='1/1/2020', end='1/10/2020', freq='15T'), # no tz
model='simplified_solis')
_assert_daytime_no_shoulder(clearsky['ghi'],
daytime.power_or_irradiance(clearsky['ghi']))
def test_daytime_variable(clearsky_january):
ghi = clearsky_january['ghi'].copy()
np.random.seed(1337)
ghi.loc['1/10/2020'] *= np.random.rand(len(ghi['1/10/2020']))
ghi.loc['1/11/2020'] *= np.random.rand(len(ghi['1/11/2020']))
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
def test_daytime_daylight_savings(albuquerque):
spring = pd.date_range(start='2/10/2020',
end='4/10/2020',
freq='15T',
tz='America/Denver')
clearsky_spring = albuquerque.get_clearsky(spring,
model='simplified_solis')
_assert_daytime_no_shoulder(
clearsky_spring['ghi'],
daytime.power_or_irradiance(clearsky_spring['ghi']))
fall = pd.date_range(start='10/1/2020',
end='12/1/2020',
freq='15T',
tz='America/Denver')
clearsky_fall = albuquerque.get_clearsky(fall, model='simplified_solis')
_assert_daytime_no_shoulder(
clearsky_fall['ghi'],
daytime.power_or_irradiance(clearsky_fall['ghi']))
def test_daytime_outliers(clearsky_january):
outliers = pd.Series(False, index=clearsky_january.index)
outliers.loc['1/5/2020 13:00'] = True
outliers.loc['1/5/2020 14:00'] = True
outliers.loc['1/10/2020 02:00'] = True
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/5/2020 1300'] = 999
ghi.loc['1/5/2020 14:00'] = -999
ghi.loc['1/10/2020 02:00'] = -999
_assert_daytime_no_shoulder(
clearsky_january['ghi'],
daytime.power_or_irradiance(clearsky_january['ghi'],
outliers=outliers))
def test_daytime_overcast(clearsky_january):
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/3/2020':'1/5/2020'] *= 0.5
ghi.loc['1/7/2020':'1/8/2020'] *= 0.6
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
def test_daytime_zero_at_start_of_day(clearsky_january):
ghi = clearsky_january['ghi'].copy()
ghi.loc['1/5/2020 00:00':'1/5/2020 09:00'] = 0
_assert_daytime_no_shoulder(clearsky_january['ghi'],
daytime.power_or_irradiance(ghi))
data['ac_power__752'].plot()
plt.xlabel("Date")
plt.ylabel("AC Power (kW)")
plt.tight_layout()
plt.show()
# %%
# It is critical to set all negative values in the AC power time series to 0
# for :py:func:`pvanalytics.features.daytime.power_or_irradiance` to work
# properly. Negative erroneous data may affect daytime mask assignments.
data.loc[data['ac_power__752'] < 0, 'ac_power__752'] = 0
# %%
# Now, use :py:func:`pvanalytics.features.daytime.power_or_irradiance`
# to mask day periods in the time series.
predicted_day_night_mask = power_or_irradiance(series=data['ac_power__752'],
freq=freq)
# %%
# Function :py:func:`pvlib.solarposition.sun_rise_set_transit_spa` is
# used to get ground-truth sunrise and sunset times for each day at the site
# location, and a SPA-daytime mask is calculated based on these times. Data
# associated with SPA daytime periods is labeled as True, and data associated
# with SPA nighttime periods is labeled as False.
# SPA sunrise and sunset times are used here as a point of comparison to the
# :py:func:`pvanalytics.features.daytime.power_or_irradiance` outputs.
# SPA-based sunrise and sunset values are not
# needed to run :py:func:`pvanalytics.features.daytime.power_or_irradiance`.
sunrise_sunset_df = pvlib.solarposition.sun_rise_set_transit_spa(
data.index, latitude, longitude)
data['sunrise_time'] = sunrise_sunset_df['sunrise']
# Here, we check GHI data in field 'irradiance_ghi__7981'.
# :py:func:`pvanalytics.quality.irradiance.clearsky_limits`
# returns a mask that identifies data that falls between
# lower and upper limits. The defaults (used here)
# are upper bound of 110% of clear-sky GHI, and
# no lower bound.
clearsky_limit_mask = clearsky_limits(data['irradiance_ghi__7981'],
clearsky['ghi'])
# %%
# Mask nighttime values in the GHI time series using the
# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
# We will then remove nighttime values from the GHI time series.
day_night_mask = power_or_irradiance(series=data['irradiance_ghi__7981'],
freq=freq)
# %%
# Plot the 'irradiance_ghi__7981' data stream and its associated clearsky GHI
# data stream. Mask the GHI time series by its clearsky_limit_mask for daytime
# periods.
# Please note that a simple Ineichen model with static monthly turbidities
# isn't always accurate, as in this case. Other models that may provide better
# clear-sky estimates include McClear or PSM3.
data['irradiance_ghi__7981'].plot()
clearsky['ghi'].plot()
data.loc[clearsky_limit_mask & day_night_mask]['irradiance_ghi__7981'].plot(
ls='', marker='.')
plt.legend(labels=["RMIS GHI", "Clearsky GHI", "Under Clearsky Limit"],
loc="upper left")
plt.xlabel("Date")
