Large capacity

quartz_solar_forecast/forecast.py

@@ -1,11 +1,13 @@
 from datetime import datetime, timedelta
+import logging
 
 import pandas as pd
 
 from quartz_solar_forecast.data import get_nwp, make_pv_data
 from quartz_solar_forecast.forecasts import forecast_v1_tilt_orientation, TryolabsSolarPowerPredictor
 from quartz_solar_forecast.pydantic_models import PVSite
 
+log = logging.getLogger(__name__)
 
 def predict_ocf(
     site: PVSite, model=None, ts: datetime | str = None, nwp_source: str = "icon"
@@ -25,13 +27,27 @@ def predict_ocf(
     if isinstance(ts, str):
         ts = datetime.fromisoformat(ts)
 
+    if site.capacity_kwp > 4:
+        log.warning("Your site capacity is greater than 4kWp, "
+                    "however the model is trained on sites with capacity <= 4kWp."
+                    "We therefore will run the model with a capacity of 4 kWp, "
+                    "and we'll scale the results afterwards.")
+        capacity_kwp_original = site.capacity_kwp
+        site.capacity_kwp = 4
+    else:
+        capacity_kwp_original = site.capacity_kwp
+
     # make pv and nwp data from nwp_source
     nwp_xr = get_nwp(site=site, ts=ts, nwp_source=nwp_source)
     pv_xr = make_pv_data(site=site, ts=ts)
 
     # load and run models
     pred_df = forecast_v1_tilt_orientation(nwp_source, nwp_xr, pv_xr, ts, model=model)
 
+    # scale the results if the capacity is different
+    if capacity_kwp_original != site.capacity_kwp:
+        pred_df["power_kw"] = pred_df["power_kw"] * capacity_kwp_original / site.capacity_kwp
+
     return pred_df
 
 

tests/test_forecast.py

@@ -2,6 +2,9 @@
 from quartz_solar_forecast.pydantic_models import PVSite
 from datetime import datetime, timedelta
 
+import numpy as np
+
+
 def test_run_forecast():
     # make input data
     site = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=1.25)
@@ -55,7 +58,22 @@ def test_run_forecast_historical():
     print("\n Prediction based on UKMO NWP\n")
     print(predications_df_ukmo)
     print(f" Max: {predications_df_ukmo['power_kw'].max()}")
-    
+
     print("\n Prediction based on XGB\n")
     print(predications_df_xgb)
 
+
+def test_large_capacity():
+
+    # make input data
+    site = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=4)
+    site_large = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=4000)
+    ts = datetime.today() - timedelta(weeks=2)
+
+    # run model with icon, gfs and ukmo nwp
+    predications_df = run_forecast(site=site, model="gb", ts=ts, nwp_source="gfs")
+    predications_df_large = run_forecast(site=site_large, model="gb", ts=ts, nwp_source="gfs")
+
+    assert np.round(predications_df["power_kw"].sum() * 1000, 8) == np.round(
+        predications_df_large["power_kw"].sum(), 8
+    )