Add bifacial scaling to Townsend snow loss model

pvlib/snow.py

@@ -248,7 +248,8 @@ def _townsend_effective_snow(snow_total, snow_events):
 
 def loss_townsend(snow_total, snow_events, surface_tilt, relative_humidity,
                   temp_air, poa_global, slant_height, lower_edge_height,
-                  string_factor=1.0, angle_of_repose=40):
+                  string_factor=1.0, angle_of_repose=40,
+                  front_side_fraction=None):
     '''
     Calculates monthly snow loss based on the Townsend monthly snow loss
     model.
@@ -293,6 +294,13 @@ def loss_townsend(snow_total, snow_events, surface_tilt, relative_humidity,
         Piled snow angle, assumed to stabilize at 40°, the midpoint of
         25°-55° avalanching slope angles. [deg]
 
+    front_side_fraction : numeric or array-like, default None
+        Optional multiplier applied to the calculated loss fraction. For
+        bifacial systems, this can be used to scale the snow loss by the
+        front-side energy fraction from a no-soiling simulation. For example,
+        use 0.9 when 90% of monthly energy is from the front side and 10% is
+        from the rear side. If None, no bifacial adjustment is applied. [-]
+
     Returns
     -------
     loss : array-like
@@ -310,6 +318,10 @@ def loss_townsend(snow_total, snow_events, surface_tilt, relative_humidity,
     publication of [1]_, as described in [2]_.
     The definition for snow events documented above is based on [3]_.
 
+    For bifacial systems, [2]_ recommends including both front-side and
+    rear-side insolation in ``poa_global``. The resulting loss may then be
+    scaled by the front-side energy fraction using ``front_side_fraction``.
+
     References
     ----------
     .. [1] Townsend, Tim & Powers, Loren. (2011). Photovoltaics and snow: An
@@ -384,4 +396,8 @@ def loss_townsend(snow_total, snow_events, surface_tilt, relative_humidity,
         * string_factor
     )
 
+    if front_side_fraction is not None:
+        front_side_fraction = np.asarray(front_side_fraction)
+        loss_fraction = loss_fraction * front_side_fraction
+
     return np.clip(loss_fraction, 0, 1)

tests/test_snow.py

@@ -247,3 +247,27 @@ def test_loss_townsend_cases(poa_global, surface_tilt, slant_height,
         poa_global, slant_height, lower_edge_height, string_factor)
     actual = np.around(actual * 100)
     assert np.allclose(expected, actual)
+
+
+def test_loss_townsend_front_side_fraction():
+    snow_total = np.array([25.4, 25.4, 12.7, 2.54, 0, 0, 0, 0, 0, 0, 12.7,
+                           25.4])
+    snow_events = np.array([2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3])
+    surface_tilt = 20
+    relative_humidity = np.full(12, 80)
+    temp_air = np.full(12, 0)
+    poa_global = np.full(12, 350000)
+    slant_height = 2.54
+    lower_edge_height = 0.254
+    front_side_fraction = 0.9
+
+    unadjusted = snow.loss_townsend(
+        snow_total, snow_events, surface_tilt, relative_humidity, temp_air,
+        poa_global, slant_height, lower_edge_height)
+
+    adjusted = snow.loss_townsend(
+        snow_total, snow_events, surface_tilt, relative_humidity, temp_air,
+        poa_global, slant_height, lower_edge_height,
+        front_side_fraction=front_side_fraction)
+
+    np.testing.assert_allclose(adjusted, unadjusted * front_side_fraction)