Clear-Sky Detection

Identifying periods of clear-sky conditions using measured irradiance.

Identifying and filtering for clear-sky conditions is a useful way to reduce noise when analyzing measured data. This example shows how to use pvanalytics.features.clearsky.reno() to identify clear-sky conditions using measured GHI data. For this example we’ll use GHI measurements from NREL in Golden CO.

import pvanalytics
from pvanalytics.features.clearsky import reno
import pvlib
import matplotlib.pyplot as plt
import pandas as pd
import pathlib

First, read in the GHI measurements. For this example we’ll use an example file included in pvanalytics covering a single day, but the same process applies to data of any length.

pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
ghi_file = pvanalytics_dir / 'data' / 'midc_bms_ghi_20220120.csv'
data = pd.read_csv(ghi_file, index_col=0, parse_dates=True)

# or you can fetch the data straight from the source using pvlib:
# date = pd.to_datetime('2022-01-20')
# data = pvlib.iotools.read_midc_raw_data_from_nrel('BMS', date, date)

measured_ghi = data['Global CMP22 (vent/cor) [W/m^2]']

Now model clear-sky irradiance for the location and times of the measured data:

location = pvlib.location.Location(39.742, -105.18)
clearsky = location.get_clearsky(data.index)
clearsky_ghi = clearsky['ghi']

Finally, use pvanalytics.features.clearsky.reno() to identify measurements during clear-sky conditions:

is_clearsky = reno(measured_ghi, clearsky_ghi)

# clear-sky times indicated in black
measured_ghi.plot()
measured_ghi[is_clearsky].plot(ls='', marker='o', ms=2, c='k')
plt.ylabel('Global Horizontal Irradiance [W/m2]')
plt.show()

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