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Fix kimber cleaning threshold and grace period resolution dependence #2943

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Fix kimber cleaning threshold and grace period resolution dependence #2943

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b09dab7
Fix kimber cleaning threshold and grace period resolution dependence
CameronGordonn Oct 30, 2026
dd0c9fe
Speed up kimber cleaning event search, clarify whatsnew
CameronGordonn Nov 12, 2026
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8 changes: 8 additions & 0 deletions docs/sphinx/source/whatsnew/v0.16.2.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,13 @@ Bug fixes
is scalar (:issue:`2881`, :pull:`2882`)
* :py:func:`pvlib.scaling.wvm` no longer returns all NaNs when ``positions``
contains a single site (:issue:`2895`, :pull:`2896`)
* :py:func:`pvlib.soiling.kimber` now cleans when rainfall equals
``cleaning_threshold``, and the grace period lasts ``grace_period`` days
from the cleaning event at any input time resolution. Light rain after a
cleaning event no longer extends the grace period. Results will differ
from previous versions; with daily input, panels now stay clean for
``grace_period`` days after the day of the cleaning event, rather than
``grace_period - 1``. (:issue:`2796`, :pull:`2943`)

Enhancements
~~~~~~~~~~~~
Expand Down Expand Up @@ -78,3 +85,4 @@ Contributors
* :ghuser:`bfs2021`
* :ghuser:`xiajiahang`
* Jakub Krasuski (:ghuser:`Blizzeq`)
* Cameron Gordon (:ghuser:`CameronGordonn`)
58 changes: 46 additions & 12 deletions pvlib/soiling.py
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Expand Up @@ -117,6 +117,34 @@ def hsu(rainfall, cleaning_threshold, surface_tilt, pm2_5, pm10,
return soiling_ratio


def _kimber_cleaning_events(times, rainfall, cleaning_threshold,
rain_accum_period, tol=1e-6):
"""
Flag each time step where ``cleaning_threshold`` of new rain has fallen
within ``rain_accum_period``. Rain counted toward an event is used up by
it, so only a further ``cleaning_threshold`` of rain makes a new event.
``tol`` [mm] absorbs floating point error in the running sum.
"""
rainfall = np.nan_to_num(rainfall) # missing rainfall counts as none
events = np.zeros(len(rainfall), dtype=bool)
# a step without rain can't raise the running sum, so it can't be an
# event; loop over rainy steps only, which are usually a small minority
rainy = np.flatnonzero(rainfall)
times, rainfall = times[rainy], rainfall[rainy]
start, total = 0, 0.0
for i in range(len(rainfall)):
total += rainfall[i]
while times[i] - times[start] >= rain_accum_period:
total -= rainfall[start]
start += 1
if total < tol:
total = 0.0 # no rain in window, drop accumulated rounding error
elif total >= cleaning_threshold - tol:
events[rainy[i]] = True
start, total = i + 1, 0.0
return events


def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
grace_period=14, max_soiling=0.3, manual_wash_dates=None,
initial_soiling=0, rain_accum_period=24):
Expand All @@ -125,11 +153,11 @@ def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
daily loss rate using the Kimber model.

Kimber soiling model [1]_ assumes soiling builds up at a daily rate unless
the daily rainfall is greater than a threshold. The model also assumes that
if daily rainfall has exceeded the threshold within a grace period, then
the ground is too damp to cause soiling build-up. The model also assumes
there is a maximum soiling build-up. Scheduled manual washes and rain
events are assumed to reset soiling to zero.
the daily rainfall meets a cleaning threshold. The model also assumes that
for a grace period after rainfall that meets the threshold, the ground is
too damp to cause soiling build-up. The model also assumes there is a
maximum soiling build-up. Scheduled manual washes and rain events are
assumed to reset soiling to zero.

Parameters
----------
Expand All @@ -140,7 +168,7 @@ def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
soiling_loss_rate: float, default 0.0015
Fraction of energy lost due to one day of soiling. [unitless]
grace_period : int, default 14
Number of days after a rainfall event when it's assumed the ground is
Number of days after a cleaning event when it's assumed the ground is
damp, and so it's assumed there is no soiling. [days]
max_soiling : float, default 0.3
Maximum fraction of energy lost due to soiling. Soiling will build up
Expand Down Expand Up @@ -179,6 +207,14 @@ def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
Rainfall thresholds and grace periods may also vary by region. Please
consult [1]_ for more information.

A cleaning event occurs each time `cleaning_threshold` of new rain has
fallen within `rain_accum_period`, and the grace period starts at that
time step. Rain counted toward a cleaning event cannot count toward
another, so light rain after a storm does not extend the grace period.
With the default `rain_accum_period` of 24 hours and daily input, a day
is a cleaning event if its rainfall meets `cleaning_threshold`, as in
[1]_. Missing rainfall values are treated as zero.

References
----------
.. [1] "The Effect of Soiling on Large Grid-Connected Photovoltaic Systems
Expand All @@ -198,21 +234,19 @@ def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
timestep_interval = (rain_index_vals[1] - rain_index_vals[0])
day_fraction = timestep_interval / np.timedelta64(24, 'h')

# accumulate rainfall
accumulated_rainfall = rainfall.rolling(
rain_accum_period, closed='right').sum()

# soiling rate
soiling = np.ones_like(rainfall.values) * soiling_loss_rate * day_fraction
soiling[0] = initial_soiling
soiling = np.cumsum(soiling)
soiling = pd.Series(soiling, index=rainfall.index, name='soiling')

# rainfall events that clean the panels
rain_events = accumulated_rainfall > cleaning_threshold
rain_events = pd.Series(_kimber_cleaning_events(
rain_index_vals, rainfall.to_numpy(dtype=float), cleaning_threshold,
np.timedelta64(rain_accum_period)), index=rainfall.index)

# grace periods windows during which ground is assumed damp, so no soiling
grace_windows = rain_events.rolling(grace_period, closed='right').sum() > 0
grace_windows = rain_events.rolling(grace_period, closed='both').sum() > 0

# clean panels by subtracting soiling for indices in grace period windows
cleaning = pd.Series(float('NaN'), index=rainfall.index)
Expand Down
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