Add functions to fit and convert IAM models

docs/examples/reflections/plot_convert_iam_models.py

@@ -0,0 +1,154 @@
+
+"""
+IAM Model Conversion
+====================
+
+Illustrates how to convert from one IAM model to a different model using
+:py:func:`~pvlib.iam.convert`
+
+"""
+
+# %%
+# An incidence angle modifier (IAM) model quantifies the fraction of direct
+# irradiance is that is reflected away from a module's surface. Three popular
+# IAM models are Martin-Ruiz :py:func:`~pvlib.iam.martin_ruiz`, physical
+# :py:func:`~pvlib.iam.physical`, and ASHRAE py:func:`~pvlib.iam.ashrae`.
+# Each model requires one or more parameters.
+#
+# Here, we show how to use
+# :py:func:`~pvlib.iam.convert` to estimate parameters for a desired target
+# IAM model from a source IAM model. Model conversion requires a weight
+# function that assigns more influence to some AOI values than others.
+# We illustrate how to provide a custom weight function to
+# :py:func:`~pvlib.iam.convert`.
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pvlib.tools import cosd
+from pvlib.iam import (ashrae, martin_ruiz, physical, convert)
+
+# %%
+# Converting from one IAM model to another model
+# ----------------------------------------------
+#
+# Here we'll show how to convert from the Martin-Ruiz model to the
+# physical and the ASHRAE models.
+
+# Compute IAM values using the martin_ruiz model.
+aoi = np.linspace(0, 90, 100)
+martin_ruiz_params = {'a_r': 0.16}
+martin_ruiz_iam = martin_ruiz(aoi, **martin_ruiz_params)
+
+# Get parameters for the physical model and compute IAM using these parameters.
+physical_params = convert('martin_ruiz', martin_ruiz_params, 'physical')
+physical_iam = physical(aoi, **physical_params)
+
+# Get parameters for the ASHRAE model and compute IAM using these parameters.
+ashrae_params = convert('martin_ruiz', martin_ruiz_params, 'ashrae')
+ashrae_iam = ashrae(aoi, **ashrae_params)
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 5), sharey=True)
+
+# Plot each model's IAM vs. angle-of-incidence (AOI).
+ax1.plot(aoi, martin_ruiz_iam, label='Martin-Ruiz')
+ax1.plot(aoi, physical_iam, label='physical')
+ax1.set_xlabel('AOI (degrees)')
+ax1.set_title('Convert from Martin-Ruiz to physical')
+ax1.legend()
+
+ax2.plot(aoi, martin_ruiz_iam, label='Martin-Ruiz')
+ax2.plot(aoi, ashrae_iam, label='ASHRAE')
+ax2.set_xlabel('AOI (degrees)')
+ax2.set_title('Convert from Martin-Ruiz to ASHRAE')
+ax2.legend()
+
+ax1.set_ylabel('IAM')
+plt.show()
+
+
+# %%
+# The weight function
+# -------------------
+# :py:func:`pvlib.iam.convert` uses a weight function when computing residuals
+# between the two models. The default weight
+# function is :math:`1 - \sin(aoi)`. We can instead pass a custom weight
+# function to :py:func:`pvlib.iam.convert`.
+#
+# In some cases, the choice of weight function has a minimal effect on the
+# returned model parameters. This is especially true when converting between
+# the Martin-Ruiz and physical models, because the curves described by these
+# models can match quite closely. However, when conversion involves the ASHRAE
+# model, the choice of weight function can have a meaningful effect on the
+# returned parameters for the target model.
+#
+# Here we'll show examples of both of these cases, starting with an example
+# where the choice of weight function does not have much impact. In doing
+# so, we'll show how to pass in a custom weight function of our choice.
+
+# Compute IAM using the Martin-Ruiz model.
+aoi = np.linspace(0, 90, 100)
+martin_ruiz_params = {'a_r': 0.16}
+martin_ruiz_iam = martin_ruiz(aoi, **martin_ruiz_params)
+
+# Get parameters for the physical model ...
+
+# ... using the default weight function.
+physical_params_default = convert('martin_ruiz', martin_ruiz_params,
+                                  'physical')
+physical_iam_default = physical(aoi, **physical_params_default)
+
+
+# ... using a custom weight function.
+def weight_function(aoi):
+    return cosd(aoi)
+
+
+physical_params_custom = convert('martin_ruiz', martin_ruiz_params, 'physical',
+                                 weight=weight_function)
+physical_iam_custom = physical(aoi, **physical_params_custom)
+
+# Plot IAM vs AOI.
+plt.plot(aoi, martin_ruiz_iam, label='Martin-Ruiz')
+plt.plot(aoi, physical_iam_default, label='Default weight function')
+plt.plot(aoi, physical_iam_custom, label='Custom weight function')
+plt.xlabel('AOI (degrees)')
+plt.ylabel('IAM')
+plt.title('Martin-Ruiz to physical')
+plt.legend()
+plt.show()
+
+# %%
+# For this choice of source and target models, the weight function has little
+# effect on the target model's parameters.
+#
+# Now we'll look at an example where the weight function does affect the
+# output.
+
+# Get parameters for the ASHRAE model ...
+
+# ... using the default weight function.
+ashrae_params_default = convert('martin_ruiz', martin_ruiz_params, 'ashrae')
+ashrae_iam_default = ashrae(aoi, **ashrae_params_default)
+
+# ... using the custom weight function
+ashrae_params_custom = convert('martin_ruiz', martin_ruiz_params, 'ashrae',
+                               weight=weight_function)
+ashrae_iam_custom = ashrae(aoi, **ashrae_params_custom)
+
+# Plot IAM vs AOI.
+plt.plot(aoi, martin_ruiz_iam, label='Martin-Ruiz')
+plt.plot(aoi, ashrae_iam_default, label='Default weight function')
+plt.plot(aoi, ashrae_iam_custom, label='Custom weight function')
+plt.xlabel('AOI (degrees)')
+plt.ylabel('IAM')
+plt.title('Martin-Ruiz to ASHRAE')
+plt.legend()
+plt.show()
+
+# %%
+# In this case, each of the two ASHRAE looks quite different.
+# Finding the right weight function and parameters in such cases will require
+# knowing where you want the target model to be more accurate. The default
+# weight function was chosen because it yielded IAM models that produce
+# similar annual insolation for a simulated PV system TODO add reference.

docs/examples/reflections/plot_fit_iam_models.py

@@ -0,0 +1,98 @@
+
+"""
+IAM Model Fitting
+================================
+
+Illustrates how to fit an IAM model to data using :py:func:`~pvlib.iam.fit`
+
+"""
+
+# %%
+# An incidence angle modifier (IAM) model quantifies the fraction of direct
+# irradiance is that is reflected away from a module's surface. Three popular
+# IAM models are Martin-Ruiz :py:func:`~pvlib.iam.martin_ruiz`, physical
+# :py:func:`~pvlib.iam.physical`, and ASHRAE py:func:`~pvlib.iam.ashrae`.
+# Each model requires one or more parameters.
+#
+# Here, we show how to use
+# :py:func:`~pvlib.iam.fit` to estimate a model's parameters from data.
+#
+# Model fitting require a weight function that assigns
+# more influence to some AOI values than others. We illustrate how to provide
+# a custom weight function to :py:func:`~pvlib.iam.fit`.
+
+import numpy as np
+from random import uniform
+import matplotlib.pyplot as plt
+
+from pvlib.tools import cosd
+from pvlib.iam import (martin_ruiz, physical, fit)
+
+
+# %%
+# Fitting an IAM model to data
+# ----------------------------
+#
+# Here, we'll show how to fit an IAM model to data.
+# We'll generate some data by perturbing output from the Martin-Ruiz model to
+# mimic measured data and then we'll fit the physical model to the perturbed
+# data.
+
+# Create and perturb IAM data.
+aoi = np.linspace(0, 90, 100)
+params = {'a_r': 0.16}
+iam = martin_ruiz(aoi, **params)
+data = iam * np.array([uniform(0.98, 1.02) for _ in range(len(iam))])
+
+# Get parameters for the physical model by fitting to the perturbed data.
+physical_params = fit(aoi, data, 'physical')
+
+# Compute IAM with the fitted physical model parameters.
+physical_iam = physical(aoi, **physical_params)
+
+# Plot IAM vs. AOI
+plt.scatter(aoi, data, c='darkorange', label='Data')
+plt.plot(aoi, physical_iam, label='physical')
+plt.xlabel('AOI (degrees)')
+plt.ylabel('IAM')
+plt.title('Fitting the physical model to data')
+plt.legend()
+plt.show()
+
+
+# %%
+# The weight function
+# -------------------
+# :py:func:`pvlib.iam.fit` uses a weight function when computing residuals
+# between the model abd data. The default weight
+# function is :math:`1 - \sin(aoi)`. We can instead pass a custom weight
+# function to :py:func:`pvlib.iam.fit`.
+#
+
+# Define a custom weight function.
+def weight_function(aoi):
+    return cosd(aoi)
+
+
+physical_params_custom = fit(aoi, data, 'physical', weight=weight_function)
+
+physical_iam_custom = physical(aoi, **physical_params_custom)
+
+# Plot IAM vs AOI.
+fig, ax = plt.subplots(2, 1, figsize=(5, 8))
+ax[0].plot(aoi, data, '.', label='Data (from Martin-Ruiz model)')
+ax[0].plot(aoi, physical_iam, label='With default weight function')
+ax[0].plot(aoi, physical_iam_custom, label='With custom weight function')
+ax[0].set_xlabel('AOI (degrees)')
+ax[0].set_ylabel('IAM')
+ax[0].legend()
+
+ax[1].plot(aoi, physical_iam_custom - physical_iam, label='Custom - default')
+ax[1].set_xlabel('AOI (degrees)')
+ax[1].set_ylabel('Diff. in IAM')
+ax[1].legend()
+plt.tight_layout()
+plt.show()
+
+print("Parameters with default weights: " + str(physical_params))
+print("Parameters with custom weights: " + str(physical_params_custom))

docs/sphinx/source/reference/pv_modeling/iam.rst

@@ -17,3 +17,5 @@ Incident angle modifiers
    iam.marion_integrate
    iam.schlick
    iam.schlick_diffuse
+   iam.convert
+   iam.fit

docs/sphinx/source/whatsnew/v0.10.3.rst

@@ -12,6 +12,8 @@ Enhancements
 * :py:func:`pvlib.bifacial.infinite_sheds.get_irradiance` and
   :py:func:`pvlib.bifacial.infinite_sheds.get_irradiance_poa` now include
   shaded fraction in returned variables. (:pull:`1871`)
+* Added :py:func:`~pvlib.iam.convert` and :py:func:`~pvlib.iam.fit` that
+  convert between IAM models, and that fit an IAM model to data. (:issue:`1824`, :pull:`1827`)
 
 Bug fixes
 ~~~~~~~~~
@@ -32,3 +34,5 @@ Contributors
 * Miguel Sánchez de León Peque (:ghuser:`Peque`)
 * Will Hobbs (:ghuser:`williamhobbs`)
 * Anton Driesse (:ghuser:`adriesse`)
+* Abigail Jones (:ghuser:`ajonesr`)
+* Cliff Hansen (:ghuser:`cwhanse`)