Component Sum Equations for Irradiance Data

Estimate GHI, DHI, and DNI using the component sum equations, with nighttime corrections.

Estimating GHI, DHI, and DNI using the component sum equations is useful if the associated field is missing, or as a comparison to an existing physical data stream.

import pvanalytics
from pvanalytics.quality.irradiance import calculate_component_sum_series
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. This is done using the pvlib.solarposition.get_solarposition() function.

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)

Get the clearsky DNI values associated with the current location, using the pvlib.solarposition.get_solarposition() function. These clearsky values are used to calculate DNI data.

site = pvlib.location.Location(latitude, longitude, tz=time_zone)
clearsky = site.get_clearsky(data.index)

Use pvanalytics.quality.irradiance.calcuate_ghi_component() to estimate GHI measurements using DHI and DNI measurements

component_sum_ghi = calculate_component_sum_series(
    solar_zenith=solar_position['zenith'],
    dhi=data['irradiance_dhi__7983'],
    dni=data['irradiance_dni__7982'],
    zenith_limit=90,
    fill_night_value='equation')

Plot the ‘irradiance_ghi__7981’ data stream against the estimated component sum GHI, for comparison

data['irradiance_ghi__7981'].plot()
component_sum_ghi.plot()
plt.legend(labels=["RMIS GHI", "Component Sum GHI"],
           loc="upper left")
plt.xlabel("Date")
plt.ylabel("GHI (W/m^2)")
plt.tight_layout()
plt.show()

Use pvanalytics.quality.irradiance.calcuate_dhi_component() to estimate DHI measurements using GHI and DNI measurements

component_sum_dhi = calculate_component_sum_series(
    solar_zenith=solar_position['zenith'],
    dni=data['irradiance_dni__7982'],
    ghi=data['irradiance_ghi__7981'],
    zenith_limit=90,
    fill_night_value='equation')

Plot the ‘irradiance_dhi__7983’ data stream against the estimated component sum GHI, for comparison

data['irradiance_dhi__7983'].plot()
component_sum_dhi.plot()
plt.legend(labels=["RMIS DHI", "Component Sum DHI"],
           loc="upper left")
plt.xlabel("Date")
plt.ylabel("GHI (W/m^2)")
plt.tight_layout()
plt.show()

Use pvanalytics.quality.irradiance.calcuate_dni_component() to estimate DNI measurements using GHI and DHI measurements

component_sum_dni = calculate_component_sum_series(
    solar_zenith=solar_position['zenith'],
    dhi=data['irradiance_dhi__7983'],
    ghi=data['irradiance_ghi__7981'],
    dni_clear=clearsky['dni'],
    zenith_limit=90,
    fill_night_value='equation')

Plot the ‘irradiance_dni__7982’ data stream against the estimated component sum GHI, for comparison

data['irradiance_dni__7982'].plot()
component_sum_dni.plot()
plt.legend(labels=["RMIS DNI", "Component Sum DNI"],
           loc="upper left")
plt.xlabel("Date")
plt.ylabel("DNI (W/m^2)")
plt.tight_layout()
plt.show()

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