Clearsky Limits for Irradiance Data

Checking the clearsky limits of irradiance data.

Identifying and filtering out invalid irradiance data is a useful way to reduce noise during analysis. In this example, we use pvanalytics.quality.irradiance.clearsky_limits() to identify irradiance values that do not exceed a limit based on a clear-sky model. For this example we will use GHI data from the RMIS weather system located on the NREL campus in CO.

import pvanalytics
from pvanalytics.quality.irradiance import clearsky_limits
from pvanalytics.features.daytime import power_or_irradiance
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 POA, GHI, DNI, DHI, and GNI measurements, but only the GHI is relevant here.

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)
freq = '5min'
# Make the datetime index tz-aware.
data.index = data.index.tz_localize("Etc/GMT+7")

Now model clear-sky irradiance for the location and times of the measured data. You can do this using pvlib.location.Location.get_clearsky(), using the lat-long coordinates associated the RMIS NREL system.

location = pvlib.location.Location(39.7407, -105.1686)
clearsky = location.get_clearsky(data.index)

Use pvanalytics.quality.irradiance.clearsky_limits(). Here, we check GHI data in field ‘irradiance_ghi__7981’. 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 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")
plt.ylabel("GHI (W/m^2)")
plt.tight_layout()
plt.show()

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Clearsky Limits for Daily Insolation

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QCrad Limits for Irradiance Data