Perez coeff ModelChain example

docs/examples/irradiance-transposition/use_perez_modelchain.py

@@ -0,0 +1,123 @@
+"""
+Use different Perez coefficients with the ModelChain
+====================================================
+
+This example demonstrates how to customize the ModelChain
+to use site-specifc Perez coefficients.
+"""
+
+# %%
+# The :py:class:`pvlib.modelchain.ModelChain` object provides a useful method
+# for easily constructing a model with a simple, unified interface.
+# However, a user may want to customize their models in various ways.
+# One such example is during the irradiance transposition step.
+# The Perez models perform very well on field data, but
+# they require a set of fitted coefficients from various sites.
+# It has been noted that these coefficients can be specific to
+# various climates, so users may see a boost in model performance
+# when using the correct set of parameters.
+# However, the base  :py:class:`~pvlib.modelchain.ModelChain`
+# only supports the default coefficients.
+# This example shows how the  :py:class:`~pvlib.modelchain.ModelChain` can
+# be adjusted to use a different set of Perez coefficients.
+
+import pandas as pd
+from pvlib.pvsystem import PVSystem
+from pvlib.modelchain import ModelChain
+from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
+from pvlib import iotools, location, irradiance
+import pvlib
+import os
+import matplotlib.pyplot as plt
+
+# load in TMY weather data from North Carolina included with pvlib
+PVLIB_DIR = pvlib.__path__[0]
+DATA_FILE = os.path.join(PVLIB_DIR, 'data', '723170TYA.CSV')
+
+tmy, metadata = iotools.read_tmy3(DATA_FILE, coerce_year=1990,
+                                  map_variables=True)
+
+weather_data = pd.DataFrame({'ghi': tmy['ghi'], 'dhi': tmy['dhi'],
+                             'dni': tmy['dni'],
+                             'temp_air': tmy['temp_air'],
+                             'wind_speed': tmy['wind_speed']})
+
+loc = location.Location.from_tmy(metadata)
+
+surface_tilt = metadata['latitude']
+surface_azimuth = 180
+
+# define an example module and inverter
+sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
+cec_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')
+sandia_module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
+cec_inverter = cec_inverters['ABB__MICRO_0_25_I_OUTD_US_208__208V_']
+
+temp_params = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
+
+# define the system and ModelChain
+system = PVSystem(arrays=None,
+                  surface_tilt=surface_tilt,
+                  surface_azimuth=surface_azimuth,
+                  module_parameters=sandia_module,
+                  inverter_parameters=cec_inverter,
+                  temperature_model_parameters=temp_params)
+
+mc = ModelChain(system, location=loc)
+
+# %%
+# Now, let's calculate POA irradiance values outside of the ``ModelChain``.
+# We do this for both the default Perez coefficients and the desired
+# alternative Perez coefficients.  This enables comparison at the end.
+
+# Cape Canaveral seems like the most likely match for climate
+model_perez = 'capecanaveral1988'
+
+solar_position = loc.get_solarposition(times=weather_data.index)
+dni_extra = irradiance.get_extra_radiation(weather_data.index)
+
+POA_irradiance = irradiance.get_total_irradiance(
+    surface_tilt=surface_tilt,
+    surface_azimuth=surface_azimuth,
+    dni=weather_data['dni'],
+    ghi=weather_data['ghi'],
+    dhi=weather_data['dhi'],
+    solar_zenith=solar_position['apparent_zenith'],
+    solar_azimuth=solar_position['azimuth'],
+    model='perez',
+    dni_extra=dni_extra)
+
+POA_irradiance_new_perez = irradiance.get_total_irradiance(
+    surface_tilt=surface_tilt,
+    surface_azimuth=surface_azimuth,
+    dni=weather_data['dni'],
+    ghi=weather_data['ghi'],
+    dhi=weather_data['dhi'],
+    solar_zenith=solar_position['apparent_zenith'],
+    solar_azimuth=solar_position['azimuth'],
+    model='perez',
+    model_perez=model_perez,
+    dni_extra=dni_extra)
+
+# %%
+# Now, run the ``ModelChain`` with both sets of irradiance data and compare:
+
+mc.run_model_from_poa(POA_irradiance)
+ac_power_default = mc.results.ac
+
+mc.run_model_from_poa(POA_irradiance_new_perez)
+ac_power_new_perez = mc.results.ac
+
+start, stop = '1990-05-05 06:00:00', '1990-05-05 19:00:00'
+plt.plot(ac_power_default.loc[start:stop],
+         label="Default Composite Perez Model")
+plt.plot(ac_power_new_perez.loc[start:stop],
+         label="Cape Canaveral Perez Model")
+plt.xticks(rotation=90)
+plt.ylabel("AC Power ($W$)")
+plt.legend()
+plt.tight_layout()
+plt.show()
+# %%
+# Note that there is a small, but noticeable difference from the default
+# coefficients that may add up over longer periods of time.

docs/sphinx/source/whatsnew/v0.11.1.rst

@@ -35,6 +35,11 @@ Documentation
 * Added gallery example on calculating cell temperature for
   floating PV. (:pull:`2110`)
 
+* Added gallery example demonstrating how to use
+  different Perez coefficients in a ModelChain.
+  (:issue:`2127`, :pull:`2148`)
+
+
 Requirements
 ~~~~~~~~~~~~
 
@@ -45,4 +50,5 @@ Contributors
 * Leonardo Micheli (:ghuser:`lmicheli`)
 * Echedey Luis (:ghuser:`echedey-ls`)
 * Rajiv Daxini (:ghuser:`RDaxini`)
+* Ben Pierce (:ghuser:`bgpierc`)