use module temp in place of cell temp if not using sapm

api/solarperformanceinsight_api/compute.py

@@ -509,9 +509,17 @@ def compare_expected_and_actual(job: models.StoredJob, si: storage.StorageInterf
     save_results_to_db(job.object_id, result_list, si)
 
 
+def _zero_div(a, b):
+    out = a / b
+    a_almost_zero = abs(a) < 1e-16
+    nans = pd.isnull(out)  # nan when 0 / 0,  a > 0 /0 => inf
+    out[a_almost_zero & nans] = 0.0
+    return out
+
+
 def _temp_factor(gamma, t_ref, t_actual):
     t0 = 25.0
-    return (1 - gamma * (t_actual - t0)) / (1 - gamma * (t_ref - t0))
+    return _zero_div(1 - gamma * (t_actual - t0), 1 - gamma * (t_ref - t0))
 
 
 def _get_mc_dc(mcresult: ModelChainResult, num_arrays: int) -> pd.DataFrame:
@@ -523,6 +531,26 @@ def _get_mc_dc(mcresult: ModelChainResult, num_arrays: int) -> pd.DataFrame:
     return pd.DataFrame({"performance": out})  # type: ignore
 
 
+def _get_temp(
+    weather_df: Tuple[pd.DataFrame, ...],
+    cell_temp: Tuple[pd.Series, ...],
+    arrays: List[models.PVArray],
+    tshift: dt.timedelta,
+) -> Tuple[pd.Series, ...]:
+    out = []
+    for df, ct, arr in zip(weather_df, cell_temp, arrays):
+        if (
+            not isinstance(
+                arr.temperature_model_parameters, models.SAPMTemperatureParameters
+            )
+            and "module_temperature" in df.columns
+        ):
+            out.append(df["module_temperature"].shift(freq=tshift))  # type: ignore
+        else:
+            out.append(ct)
+    return tuple(out)
+
+
 def _calculate_weather_adjusted_predicted_performance(
     job: models.StoredJob, si: storage.StorageInterface
 ) -> Tuple[List[DBResult], pd.Series]:
@@ -589,7 +617,8 @@ def _calculate_weather_adjusted_predicted_performance(
         # instead of full modelchain calculation.
         # since it sets class properties, copy for the actuals calculations
         chain_actual = deepcopy(chain)
-        pac0 = job.definition.system_definition.inverters[i].inverter_parameters._pac0
+        inv = job.definition.system_definition.inverters[i]
+        pac0 = inv.inverter_parameters._pac0
         num_arrays = len(chain.system.arrays)
         gammas = [
             arr._gamma for arr in job.definition.system_definition.inverters[i].arrays
@@ -624,16 +653,23 @@ def _calculate_weather_adjusted_predicted_performance(
             chain_actual.results.total_irrad, chain_actual.results.effective_irradiance
         )
         # use cell temperature from the pvlib modelchain
-        # If air temp + wind speed or module temperature were supplied, they are
-        # converted to cell temperature via the array's temperature model
-        t_ref = chain.results.cell_temperature
-        t_actual = chain_actual.results.cell_temperature
+        # If air temp + wind speed were supplied, they are converted
+        # to cell temperature via the array's temperature model.  If
+        # module_temperature was supplied and the temperature model is
+        # sapm, it is converted to cell_temperature, otherwise module
+        # temperature is used in place of cell_temperature
+        t_ref = _get_temp(
+            ref_weather, chain.results.cell_temperature, inv.arrays, tshift
+        )
+        t_actual = _get_temp(
+            actual_weather, chain_actual.results.cell_temperature, inv.arrays, tshift
+        )
 
         # mean of array POArat * TempFactor for this inverter
         # could make more sense to use weighted mean with weights set
         # by array power percentage
         # another alternative, calculate average POArat and TempFactor separately
-        poa_rat = [pa / pr for pa, pr in zip(poa_actual, poa_ref)]
+        poa_rat = [_zero_div(pa, pr) for pa, pr in zip(poa_actual, poa_ref)]
         tempfactor = list(map(_temp_factor, gammas, t_ref, t_actual))
         poa_rat_x_temp_factor = adjust(  # modelchain outputs are all shifted
             sum([p * t for p, t in zip(poa_rat, tempfactor)]) / num_arrays

api/solarperformanceinsight_api/tests/test_compute.py

@@ -895,9 +895,25 @@ def actual_params(irr_type, temp_type, weather_gran, perf_gran):
 
 
 @pytest.fixture()
-def pvsyst_system():
+def sandia_inverter():
+    return dict(
+        Paco=5000,
+        Pdco=5447.35,
+        Vdco=48,
+        Pso=18.11,
+        C0=-1.1e-5,
+        C1=3.26e-4,
+        C2=-5.6e-3,
+        C3=-1.2e-3,
+        Pnt=1.5,
+    )
+
+
+@pytest.fixture()
+def pvsyst_system(sandia_inverter):
     sysdict = deepcopy(models.SYSTEM_EXAMPLE)
     inv = sysdict["inverters"][0]
+    inv["inverter_parameters"] = sandia_inverter
     inv1 = deepcopy(inv)
     inv1["name"] = "inverter 2"
     inv1["arrays"] = [inv1["arrays"][0], inv1["arrays"][0]]
@@ -906,7 +922,7 @@ def pvsyst_system():
 
 
 @pytest.fixture()
-def cec_system():
+def cec_system(sandia_inverter):
     sysdict = deepcopy(models.SYSTEM_EXAMPLE)
     modparams = models.CECModuleParameters(
         alpha_sc=0.00208,
@@ -920,8 +936,10 @@ def cec_system():
         Adjust=13.095,
     )
     inv = sysdict["inverters"][0]
+    inv["inverter_parameters"] = sandia_inverter
     arr = inv["arrays"][0]
     arr["module_parameters"] = modparams
+    arr["strings"] = 1
     inv["arrays"] = [arr]
     inv1 = deepcopy(inv)
     inv1["name"] = "inverter 2"
@@ -1173,3 +1191,41 @@ def test_compare_predicted_and_actual_cec(
     )
     assert "difference" in ser
     assert "ratio" in ser
+
+
+def test_compare_predicted_and_actual_cec_module_temp_as_expected(
+    mockup_predicted_actual,
+    auth0_id,
+    nocommit_transaction,
+    cec_system,
+):
+    actual_params = dict(
+        irradiance_type="poa",
+        temperature_type="cell",
+        weather_granularity="system",
+        performance_granularity="inverter",
+    )
+    pred_params = dict(
+        irradiance_type="standard",
+        temperature_type="module",
+        weather_granularity="system",
+        data_available="weather only",
+    )
+    si = storage.StorageInterface(user=auth0_id)
+    job, save = mockup_predicted_actual(cec_system, pred_params, actual_params)
+    compute.compare_predicted_and_actual(job, si)
+    assert save.call_count == 1
+    reslist = save.call_args[0][1]
+
+    perf = reslist[2]
+    assert perf.type == "weather adjusted performance"
+    assert perf.schema_path == "/inverters/0"
+    iob = BytesIO(perf.data)
+    iob.seek(0)
+    # 2021-02-01 11:00:00-07:00    1069.703613 if using cell temp from 20C
+    assert (
+        pd.read_feather(iob)
+        .set_index("time")
+        .loc["2021-02-01 11:00:00-07:00", "performance"]
+        > 1070
+    )