Add gallery example for modeling with interval averages

docs/examples/plot_interval_transposition_error.py

@@ -0,0 +1,170 @@
+"""
+Modeling with interval averages
+===============================
+
+Transposing interval-averaged irradiance data
+"""
+
+# %%
+# This example shows how failing to account for the difference between
+# instantaneous and interval-averaged time series data can introduce
+# error in the modeling process. An instantaneous time series
+# represents discrete measurements taken at each timestamp, while
+# an interval-averaged time series represents the average value across
+# each data interval.  For example, the value of an interval-averaged
+# hourly time series at 11:00 represents the average value between
+# 11:00 (inclusive) and 12:00 (exclusive), assuming the series is left-labeled.
+# For a right-labeled time series it would be the average value
+# between 10:00 (exclusive) and 11:00 (inclusive).  Sometimes timestamps
+# are center-labeled, in which case it would be the
+# average value between 10:30 and 11:30.
+# Interval-averaged time series are common in
+# field data, where the datalogger averages high-frequency measurements
+# into low-frequency averages for archiving purposes.
+#
+# It is important to account for this difference when using
+# interval-averaged weather data for modeling.  This example
+# focuses on calculating solar position appropriately for
+# irradiance transposition, but this concept is relevant for
+# other steps in the modeling process as well.
+#
+# This example calculates a POA irradiance timeseries at 1-second
+# resolution as a "ground truth" value.  Then it performs the
+# transposition again at lower resolution using interval-averaged
+# irradiance components, once using a half-interval shift and
+# once just using the unmodified timestamps. The difference
+# affects the solar position calculation: for example, assuming
+# we have average irradiance for the interval 11:00 to 12:00,
+# and it came from a left-labeled time series, naively using
+# the unmodified timestamp will calculate solar position for 11:00,
+# meaning the calculated solar position is used to represent
+# times as far as an hour away.  A better option would be to
+# calculate the solar position at 11:30 to reduce the maximum
+# timing error to only half an hour.
+
+import pvlib
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# %%
+# First, we'll define a helper function that we can re-use several
+# times in the following code:
+
+
+def transpose(irradiance, timeshift):
+    """
+    Transpose irradiance components to plane-of-array, incorporating
+    a timeshift in the solar position calculation.
+
+    Parameters
+    ----------
+        irradiance: DataFrame
+            Has columns dni, ghi, dhi
+        timeshift: float
+            Number of minutes to shift for solar position calculation
+    Outputs:
+        Series of POA irradiance
+    """
+    idx = irradiance.index
+    # calculate solar position for shifted timestamps:
+    idx = idx + pd.Timedelta(timeshift, unit='min')
+    solpos = location.get_solarposition(idx)
+    # but still report the values with the original timestamps:
+    solpos.index = irradiance.index
+
+    poa_components = pvlib.irradiance.get_total_irradiance(
+        surface_tilt=20,
+        surface_azimuth=180,
+        solar_zenith=solpos['apparent_zenith'],
+        solar_azimuth=solpos['azimuth'],
+        dni=irradiance['dni'],
+        ghi=irradiance['ghi'],
+        dhi=irradiance['dhi'],
+        model='isotropic',
+    )
+    return poa_components['poa_global']
+
+
+# %%
+# Now, calculate the "ground truth" irradiance data.  We'll simulate
+# clear-sky irradiance components at 1-second intervals and calculate
+# the corresponding POA irradiance.  At such a short timescale, the
+# difference between instantaneous and interval-averaged irradiance
+# is negligible.
+
+# baseline: all calculations done at 1-second scale
+location = pvlib.location.Location(40, -80, tz='Etc/GMT+5')
+times = pd.date_range('2019-06-01 05:00', '2019-06-01 19:00',
+                      freq='1s', tz='Etc/GMT+5')
+solpos = location.get_solarposition(times)
+clearsky = location.get_clearsky(times, solar_position=solpos)
+poa_1s = transpose(clearsky, timeshift=0)  # no shift needed for 1s data
+
+# %%
+# Now, we will aggregate the 1-second values into interval averages.
+# To see how the averaging interval affects results, we'll loop over
+# a few common data intervals and accumulate the results.
+
+fig, ax = plt.subplots(figsize=(5, 3))
+
+results = []
+
+for timescale_minutes in [1, 5, 10, 15, 30, 60]:
+
+    timescale_str = f'{timescale_minutes}min'
+    # get the "true" interval average of poa as the baseline for comparison
+    poa_avg = poa_1s.resample(timescale_str).mean()
+    # get interval averages of irradiance components to use for transposition
+    clearsky_avg = clearsky.resample(timescale_str).mean()
+
+    # low-res interval averages of 1-second data, with NO shift
+    poa_avg_noshift = transpose(clearsky_avg, timeshift=0)
+
+    # low-res interval averages of 1-second data, with half-interval shift
+    poa_avg_halfshift = transpose(clearsky_avg, timeshift=timescale_minutes/2)
+
+    df = pd.DataFrame({
+        'ground truth': poa_avg,
+        'modeled, half shift': poa_avg_halfshift,
+        'modeled, no shift': poa_avg_noshift,
+    })
+    error = df.subtract(df['ground truth'], axis=0)
+    # add another trace to the error plot
+    error['modeled, no shift'].plot(ax=ax, label=timescale_str)
+    # calculate error statistics and save for later
+    stats = error.abs().mean()  # average absolute error across daylight hours
+    stats['timescale_minutes'] = timescale_minutes
+    results.append(stats)
+
+ax.legend(ncol=2)
+ax.set_ylabel('Transposition Error [W/m$^2$]')
+fig.tight_layout()
+
+df_results = pd.DataFrame(results).set_index('timescale_minutes')
+print(df_results)
+
+# %%
+# The errors shown above are the average absolute difference in :math:`W/m^2`.
+# In this example, using the timestamps unadjusted creates an error that
+# increases with increasing interval length, up to a ~40% error
+# at hourly resolution.  In contrast, incorporating a half-interval shift
+# so that solar position is calculated in the middle of the interval
+# instead of the edge reduces the error by one or two orders of magnitude:
+
+fig, ax = plt.subplots(figsize=(5, 3))
+df_results[['modeled, no shift', 'modeled, half shift']].plot.bar(rot=0, ax=ax)
+ax.set_ylabel('Mean Absolute Error [W/m$^2$]')
+ax.set_xlabel('Transposition Timescale [minutes]')
+fig.tight_layout()
+
+# %%
+# We can also plot the underlying time series results of the last
+# iteration (hourly in this case).  The modeled irradiance using
+# no shift is effectively time-lagged compared with ground truth.
+# In contrast, the half-shift model is nearly identical to the ground
+# truth irradiance.
+
+fig, ax = plt.subplots(figsize=(5, 3))
+ax = df.plot(ax=ax, style=[None, ':', None], lw=3)
+ax.set_ylabel('Irradiance [W/m$^2$]')
+fig.tight_layout()

docs/sphinx/source/whatsnew/v0.9.0.rst

@@ -128,7 +128,9 @@ Documentation
 ~~~~~~~~~~~~~
 
 * Update intro tutorial to highlight the use of historical meteorological data
-  and to make the procedural and object oriented results match exactly.
+  and to make the procedural and OO results match exactly. (:issue:`1116`, :pull:`1144`)
+* Add a gallery example showing how to appropriately use interval-averaged
+  weather data for modeling. (:pull:`1152`)
 * Update documentation links in :py:func:`pvlib.iotools.get_psm3`
 
 Requirements