Enable usage of custom weather data

Instead of only DWD weather data files, custom
weather data can now be used.
In the config dictionary, set
'weather_data_type' to 'None' and store a
DataFrame containing the columns 'TAMB' (ambient
temperature in °C) and 'CCOVER' (cloud coverage
in octas) in the key 'weather_file'.

Author: jnettels

lpagg/VDI4655.py

@@ -71,6 +71,7 @@
 called 'config_file', the location of which is defined down below.
 """
 
+import os
 import pandas as pd
 import functools
 import logging
@@ -98,6 +99,32 @@ def run_demandlib(weather_data, cfg):
     """
     from demandlib import vdi
 
+    def climate_from_custom_weather(weather, try_region):
+        """Create a Climate object from a DataFrame with weather data."""
+        vdi.Climate().check_try_region(try_region)
+
+        weather = weather.resample("D").mean()
+
+        weather.loc[weather["CCOVER"] >= 5, "cloud_category"] = "B"
+        weather.loc[weather["CCOVER"] < 5, "cloud_category"] = "H"
+
+        fn_energy_factors = os.path.join(
+            os.path.dirname(vdi.__file__),
+            "vdi_data",
+            "VDI_4655_Typtag-Faktoren.csv",
+        )
+        energy_factors = pd.read_csv(
+            fn_energy_factors,
+            index_col=[0, 1, 2],
+        ).loc[try_region]
+
+        climate = vdi.Climate(
+            temperature=weather["TAMB"],
+            cloud_coverage=weather["cloud_category"],
+            energy_factors=energy_factors,
+            )
+        return climate
+
     settings = cfg['settings']
     houses_list = settings['houses_list_VDI']
 
@@ -153,6 +180,11 @@ def run_demandlib(weather_data, cfg):
     try:
         if settings['weather_file'] is None:
             climate = vdi.Climate().from_try_data(int(try_region))
+        elif isinstance(settings['weather_file'], vdi.Climate):
+            climate = settings['weather_file']
+        elif isinstance(settings['weather_file'], pd.DataFrame):
+            climate = climate_from_custom_weather(
+                settings['weather_file'], try_region)
         else:
             climate = vdi.Climate().from_dwd_weather_file(
                 settings['weather_file'], try_region)

lpagg/agg.py

@@ -135,8 +135,10 @@ def perform_configuration(config_file='', cfg=None, ignore_errors=False):
                                 cfg['settings'].get('result_folder',
                                                     'Result')))
 
-    weather_file = os.path.join(cfg['base_folder'], settings['weather_file'])
-    cfg['settings']['weather_file'] = os.path.abspath(weather_file)
+    if not isinstance(settings['weather_file'], pd.DataFrame):
+        weather_file = os.path.join(cfg['base_folder'],
+                                    settings['weather_file'])
+        cfg['settings']['weather_file'] = os.path.abspath(weather_file)
 
     language = cfg['settings'].get('language', 'en')
     if language == 'de':
@@ -522,7 +524,7 @@ def postprocess_unique_profiles(agg_dict, cfg,
             txt = ("Annual sums of individual profiles do not "
                    "match the input sums. When using dtype 'float32', "
                    "this can be expected to occur.")
-            if df_unique.sum() == df_lc.sum().sum():
+            if df_unique.sum().round(8) == df_lc.sum().sum().round(8):
                 txt += (" However, the total annual sum of all profiles is "
                         "correct.")
                 logger.warning(txt)
@@ -601,15 +603,12 @@ def load_weather_file(cfg):
     except TypeError:  # Read as Timestamp objects otherwise
         datetime_start = settings['start']
         datetime_end = settings['end']
-#    datetime_start = datetime.datetime(2017,1,1,00,00,00) # Example
-#    datetime_end = datetime.datetime(2018,1,1,00,00,00)
+
     interpolation_freq = pd.Timedelta(settings['intervall'])
-#    interpolation_freq = pd.Timedelta('14 minutes')
-#    interpolation_freq = pd.Timedelta('1 hours')
     remove_leapyear = settings.get('remove_leapyear', False)
-
     settings['interpolation_freq'] = interpolation_freq
-    logger.info('Read and interpolate the data in weather file '+weather_file)
+    logger.info('Read and interpolate the data in weather file %s',
+                weather_file)
 
     # Call external method in weather_converter.py:
     weather_data = lpagg.weather_converter.interpolate_weather_file(
@@ -620,11 +619,15 @@ def load_weather_file(cfg):
                                     interpolation_freq,
                                     remove_leapyear)
 
-    # Analyse weather data
-    if logger.isEnabledFor(logging.INFO):
-        lpagg.weather_converter.analyse_weather_file(
-                weather_data, interpolation_freq, weather_file,
-                print_folder=cfg['print_folder'])
+    try:
+        # Analyse weather data
+        if logger.isEnabledFor(logging.INFO):
+            lpagg.weather_converter.analyse_weather_file(
+                    weather_data, interpolation_freq, weather_file,
+                    print_folder=cfg['print_folder'])
+    except KeyError:
+        pass
+
     weather_data.index.name = 'Time'
     return weather_data
 

lpagg/weather_converter.py

@@ -484,23 +484,30 @@ def interpolate_weather_file(weather_file_path,
 #    plot_value = 'CCOVER'
 #    plot_value = 'PAMB'
 
-    weather_file = os.path.basename(weather_file_path)
 
     # Read the file and store it in a DataFrame
     if weather_data_type == 'IGS' or weather_data_type == 'TRNSYS':
         weather_data = read_IGS_weather_file(weather_file_path)
     elif weather_data_type == 'DWD':
         weather_data = read_DWD_weather_file(weather_file_path)
+    elif weather_data_type is None:
+        # Assume the object already is a DataFrame with correct columns
+        weather_data = weather_file_path
     else:
         logger.error('Weather data type "'+weather_data_type+'" unknown!')
         exit()
 
     # Assumption: The IGS weather files always start at January 01.
     current_year = datetime_start.year
     newyear = datetime.datetime(current_year, 1, 1)
-    # Convert hours of year to DateTime and make that the index of DataFrame
-    weather_data.index = pd.to_timedelta(weather_data['HOUR'],
-                                         unit='h') + newyear
+
+    if weather_data_type is not None:
+        weather_file = os.path.basename(weather_file_path)
+        # Convert hours of year to DateTime and make that the index of DataFrame
+        weather_data.index = pd.to_timedelta(weather_data['HOUR'],
+                                             unit='h') + newyear
+    else:
+        weather_file = "Weather Data"
 
     # Infer the time frequency of the original data
     original_freq = pd.infer_freq(weather_data.index)
@@ -568,7 +575,7 @@ def interpolate_weather_file(weather_file_path,
 
     # Remove leapyear from DataFrame (optional)
     if calendar.isleap(current_year) is True:
-        logger.warn(str(current_year)+' is a leap year. Be careful!')
+        logger.warning(str(current_year)+' is a leap year. Be careful!')
     if remove_leapyear is True:
         weather_data = weather_data[~((weather_data.index.month == 2) &
                                       (weather_data.index.day == 29))]