QCrad Consistency for Irradiance Data

Check consistency of GHI, DHI and DNI using QCRad criteria.

Identifying and filtering out invalid irradiance data is a useful way to reduce noise during analysis. In this example, we use pvanalytics.quality.irradiance.check_irradiance_consistency_qcrad() to check the consistency of GHI, DHI and DNI data using QCRad criteria. For this example we will use data from the RMIS weather system located on the NREL campus in Colorado, USA.

import pvanalytics
from pvanalytics.quality.irradiance import check_irradiance_consistency_qcrad
import pvlib
import matplotlib.pyplot as plt
import pandas as pd
import pathlib

First, read in data from the RMIS NREL system. This data set contains 5-minute right-aligned data. It includes POA, GHI, DNI, DHI, and GNI measurements.

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

Now generate solar zenith estimates for the location, based on the data’s time zone and site latitude-longitude coordinates.

latitude = 39.742
longitude = -105.18
time_zone = "Etc/GMT+7"
data = data.tz_localize(time_zone)
solar_position = pvlib.solarposition.get_solarposition(data.index,
                                                       latitude,
                                                       longitude)

Use pvanalytics.quality.irradiance.check_irradiance_consistency_qcrad() to generate the QCRAD consistency mask.

qcrad_consistency_mask = check_irradiance_consistency_qcrad(
    solar_zenith=solar_position['zenith'],
    ghi=data['irradiance_ghi__7981'],
    dhi=data['irradiance_dhi__7983'],
    dni=data['irradiance_dni__7982'])

Plot the GHI, DHI, and DNI data streams with the QCRAD consistency mask overlay. This mask applies to all 3 data streams.

fig = data[['irradiance_ghi__7981', 'irradiance_dhi__7983',
            'irradiance_dni__7982']].plot()
# Highlight periods where the QCRAD consistency mask is True
fig.fill_between(data.index, fig.get_ylim()[0], fig.get_ylim()[1],
                 where=qcrad_consistency_mask[0], alpha=0.4)
fig.legend(labels=["RMIS GHI", "RMIS DHI", "RMIS DNI", "QCRAD Consistent"],
           loc="upper center")
plt.xlabel("Date")
plt.ylabel("Irradiance (W/m^2)")
plt.tight_layout()
plt.show()

Plot the GHI, DHI, and DNI data streams with the diffuse ratio limit mask overlay. This mask is true when the DHI / GHI ratio passes the limit test.

fig = data[['irradiance_ghi__7981', 'irradiance_dhi__7983',
            'irradiance_dni__7982']].plot()
# Highlight periods where the GHI ratio passes the limit test
fig.fill_between(data.index, fig.get_ylim()[0], fig.get_ylim()[1],
                 where=qcrad_consistency_mask[1], alpha=0.4)
fig.legend(labels=["RMIS GHI", "RMIS DHI", "RMIS DNI",
                   "Within Diffuse Ratio Limit"],
           loc="upper center")
plt.xlabel("Date")
plt.ylabel("Irradiance (W/m^2)")
plt.tight_layout()
plt.show()

previous

QCrad Limits for Irradiance Data

next

Component Sum Equations for Irradiance Data