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openclimatefix · Dec 12, 2023 · 53c2e1c · 53c2e1c
1 parent 6a9e7f7
commit 53c2e1c
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Showing 2 changed files with 3 additions and 4 deletions.
diff --git a/quartz_solar_forecast/eval/nwp.py b/quartz_solar_forecast/eval/nwp.py
@@ -106,12 +106,10 @@ def get_nwp_for_one_timestamp_one_location(timestamp: pd.Timestamp, latitude, lo
     times = pd.to_datetime(data_at_location.time.values) + pd.to_timedelta(
         data_at_location.step.values, unit="h"
     )
-    print(times)
 
     # convert to pandas dataframe
     df = pd.DataFrame(times, columns=["time"])
     for variable in variables:
-        print(variable)
         df[variable] = data_at_location[variable].values
 
     # make wind speed out of u and v

diff --git a/quartz_solar_forecast/eval/pv.py b/quartz_solar_forecast/eval/pv.py
@@ -64,19 +64,20 @@ def get_pv_truth(testset: pd.DataFrame):
         # Calculate future timestamps up to the max horizon
         for i in range(0, 49):  # 48 hours in steps of 1 hour
             future_datetime = base_datetime + pd.DateOffset(hours=i)
-            horizon = i * 60  # Convert hours to minutes
+            horizon = i  # horizon in hours
 
             try:
                 # Attempt to select data for the future datetime
                 selected_data = pv_ds[pv_id].sel(datetime=future_datetime)
                 value = selected_data.values.item()
+                value = value /1000 *12 # to convert from wh to kw
             except KeyError:
                 # If data is not found for the future datetime, set value as NaN
                 value = np.nan
 
             # Add the data to the DataFrame
             combined_data.append(pd.DataFrame(
-                {"pv_id": pv_id, "timestamp": base_datetime, "value": value, "horizon_hour": horizon}, index=[i])
+                {"pv_id": pv_id, "timestamp": future_datetime, "value": value, "horizon_hour": horizon}, index=[i])
             )
     combined_data = pd.concat(combined_data)
     return combined_data