described non_param_assumptions in more detail

config_data/technology_definition.csv

@@ -35,7 +35,7 @@ heat_pump_ASHP_hydrogen,hydrogen,3.6,3.6,2050,1,linear,2010,tech_heating_temp_de
 heat_pump_GSHP_hydrogen,hydrogen,4.9,4.9,2050,1,linear,2010,tech_heating_temp_dep,1,OK_,,,4.9,,,,,,,,
 district_heating_gas,gas,1,1,2050,1,linear,2010,tech_district_heating,1,,,,,,,,,,,,
 district_heating_electricity,electricity,1,1,2050,1,linear,2010,tech_district_heating,1,,,,,,,,,,,,
-district_heating_biomass,biomass,1,1,2050,1,linear,2010,district_heating,1,,,,,,,,,,,,
+district_heating_biomass,biomass,1,1,2050,1,linear,2010,tech_district_heating,1,,,,,,,,,,,,
 ss_cooling_tech_electricity,electricity,1,1,2050,1,linear,2010,tech_cooling_const,1,,,,,,,,,,,,
 ss_cooling_tech_gas,gas,1,1,2050,1,linear,2010,tech_cooling_const,1,,,,,,,,,,,,
 ss_cooling_tech_oil,oil,1,1,2050,1,linear,2010,tech_cooling_const,1,,,,,,,,,,,,

energy_demand/assumptions/param_assumptions.py

@@ -313,15 +313,17 @@ def load_param_assump(paths, assumptions):
         "units": '%'})
 
     # How much of the floorarea is cooled in end year (example: 0.5 --> 50% of floorarea is cooled)
-    strategy_vars['cooled_floorarea__ss_cooling_humidification'] = 0.3 #TODO FIND NUMBER
+    # Carbon Trust. (2012). Air conditioning. Maximising comfort, minimising energy consumption.
+    strategy_vars['cooled_floorarea__ss_cooling_humidification'] = 0.4
 
     # Year until floor area change is fully realised
     strategy_vars['cooled_floorarea_yr_until_changed'] = yr_until_changed_all_things
+
     # Penetration of cooling devices
     # COLING_OENETRATION ()
     # Or Assumkp Peneetration curve in relation to HDD from PAPER #Residential
     # Assumption on recovered heat (lower heat demand based on heat recovery)
-    
+
     # ============================================================
     # Heat recycling & Reuse
     # ============================================================

energy_demand/enduse_func.py

@@ -466,16 +466,16 @@ def demand_management(
     # ------------------------------
     # If peak shifting implemented, calculate new lp
     # ------------------------------
-    peak_shift_crit = True
     if peak_shift_crit:
 
         # Calculate average for every day
         if mode_constrained:
-            # Calculate average for every day for a single fueltype
-            average_fuel_yd = np.mean(fuel_yh, axis=1)
+            axis = 1
         else:
-            # Calculate average for every day for multiple fueltypes
-            average_fuel_yd = np.mean(fuel_yh, axis=2)
+            axis = 2
+
+        # Calculate average for every day
+        average_fuel_yd = np.mean(fuel_yh, axis=axis)
 
         # Calculate load factors (only inter_day load shifting as for now)
         loadfactor_yd_cy = lf.calc_lf_d(
@@ -586,19 +586,25 @@ def assign_lp_no_techs(enduse, sector, load_profiles, fuel_new_y):
 
     Returns
     -------
-    fuel_yh
-    fuel_peak_dh
-    fuel_peak_h
+    fuel_yh : array
+        Fuel yh
+    fuel_peak_dh : array
+        Fuel for peak dh
+    fuel_peak_h : array
+        Fuel of peak hour
     """
-    _fuel = fuel_new_y[:, np.newaxis, np.newaxis]
+    fuel = fuel_new_y[:, np.newaxis, np.newaxis]
 
     fuel_yh = load_profiles.get_lp(
-        enduse, sector, 'dummy_tech', 'shape_yh') * _fuel
+        enduse, sector, 'dummy_tech', 'shape_yh') * fuel
 
-    # Read dh profile from peak day
+    # Read dh profile from peak day. Get this day
+    # where the fuel demand across all fueltypes i
+    # highest
     peak_day = get_peak_day_all_fueltypes(fuel_yh)
 
-    shape_peak_dh = lp.abs_to_rel(fuel_yh[:, peak_day, :])
+    shape_peak_dh = lp.abs_to_rel(
+        fuel_yh[:, peak_day, :])
 
     enduse_peak_yd_factor = load_profiles.get_lp(
         enduse, sector, 'dummy_tech', 'enduse_peak_yd_factor')

energy_demand/main.py

@@ -162,7 +162,7 @@ def energy_demand_model(data, fuel_in=0, fuel_in_elec=0):
     # Load data
     data = {}
     data['criterias'] = {}
-    data['criterias']['mode_constrained'] = True #constrained_by_technologies
+    data['criterias']['mode_constrained'] = False #constrained_by_technologies
     data['criterias']['plot_HDD_chart'] = False
     data['criterias']['virtual_building_stock_criteria'] = True
     data['criterias']['spatial_exliclit_diffusion'] = False
@@ -238,15 +238,15 @@ def energy_demand_model(data, fuel_in=0, fuel_in_elec=0):
     # ----------------------------------
     # Calculating COOLING CDD PARAMETER
     # ----------------------------------
-    data['assumptions']['cdd_weekend_cfactors'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+    data['assumptions']['cdd_weekend_cfactors'] = hdd_cdd.calc_weekend_corr_f(
         data['assumptions']['model_yeardays_daytype'], 
         data['assumptions']['ss_t_cooling_weekend_factor'])
 
-    data['assumptions']['ss_weekend_f'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+    data['assumptions']['ss_weekend_f'] = hdd_cdd.calc_weekend_corr_f(
         data['assumptions']['model_yeardays_daytype'], 
         data['assumptions']['ss_weekend_factor'])
 
-    data['assumptions']['is_weekend_f'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+    data['assumptions']['is_weekend_f'] = hdd_cdd.calc_weekend_corr_f(
         data['assumptions']['model_yeardays_daytype'], 
         data['assumptions']['is_weekend_factor'])
 

energy_demand/profiles/hdd_cdd.py

@@ -94,16 +94,32 @@ def averaged_temp(temp_yh, nr_day_to_av):
 
     return effective_temp_yh
 
-def get_cdd_weekend_correctionfactors(model_yeardays_daytype, wkend_factor):
-    """
+def calc_weekend_corr_f(model_yeardays_daytype, wkend_factor):
+    """Based on the factor 'wkend_factor' daily correction
+    factors get calculated for every modelled day in a year.abs
+
+    Arguments
+    ---------
+    model_yeardays_daytype : list
+        Daytype for every modelled days in the base year
+    wkend_factor : float
+        This factors states how much energy deamnd differs
+        depending on whether a day is a weekend or working day
+        (e.g. 0.8 means that on a weekend, 80% less demand occurs)
+
+    Returns
+    --------
+    cdd_weekend_f : array
+        Factor to multiply cdd calculations for every
+        day in a year
     """
-    cdd_weekend_cfactors = np.ones((365))
+    cdd_weekend_f = np.ones((365))
 
     for day_nr, day in enumerate(model_yeardays_daytype):
         if day == 'holiday':
-            cdd_weekend_cfactors[day_nr] = wkend_factor
+            cdd_weekend_f[day_nr] = wkend_factor
 
-    return cdd_weekend_cfactors
+    return cdd_weekend_f
 
 def calc_cdd(t_base_cooling, temp_yh, nr_day_to_av):
     """Calculate cooling degree days

energy_demand/read_write/read_data.py

@@ -210,7 +210,7 @@ def calc_av_per_season_fueltype(results_every_year, seasons, model_yeardays_dayt
     results_every_year : 
 
     seasons : 
-        
+
     model_yeardays_daytype : 
 
     Returns
@@ -1106,10 +1106,32 @@ def get_position(headings, name):
 
     Arguments
     ---------
+    headings : list
+        List with names
+    name : str
+        Name of entry to find
+    
+    Returns
+    -------
+    position : int
+        Position in list
     """
     for position, value in enumerate(headings):
         if str(value) == str(name):
             return position
 
 def read_np_array_from_txt(path_file_to_read):
-    return np.loadtxt(path_file_to_read, delimiter=',')
+    """Read np array from textfile
+
+    Arguments
+    ---------
+    path_file_to_read : str
+        File to path with stored array
+
+    Return
+    ------
+    txt_array : array
+        Array containing read text
+    """
+    txt_array = np.loadtxt(path_file_to_read, delimiter=',')
+    return txt_array

run.py

@@ -10,7 +10,6 @@
 from smif.model.sector_model import SectorModel
 from pkg_resources import Requirement, resource_filename
 from pyproj import Proj, transform
-from pyinstrument import Profiler
 
 from energy_demand.plotting import plotting_results
 from energy_demand.basic import basic_functions
@@ -33,7 +32,6 @@
 # must match smif project name for Local Authority Districts
 REGION_SET_NAME = 'lad_uk_2016'
 NR_OF_MODELLEd_REGIONS = 391 # uk: 391, england.: 380
-PROFILER = False
 
 class EDWrapper(SectorModel):
     """Energy Demand Wrapper
@@ -56,18 +54,18 @@ def before_model_run(self, data=None):
         data = defaultdict(dict, data)
 
         # Criteria
-        data['criterias']['mode_constrained'] = True                    # True: Technologies are defined in ED model and fuel is provided, False: Heat is delievered not per technologies
+        data['criterias']['mode_constrained'] = False                    # True: Technologies are defined in ED model and fuel is provided, False: Heat is delievered not per technologies
         data['criterias']['virtual_building_stock_criteria'] = True     # True: Run virtual building stock model
         data['criterias']['plot_HDD_chart'] = False                     # True: Plotting of HDD vs gas chart
         data['criterias']['validation_criteria'] = False                 # True: Plot validation plots
         data['criterias']['spatial_exliclit_diffusion'] = False         # True: Spatial explicit calculations
         data['criterias']['writeYAML'] = False
-        data['criterias']['write_to_txt'] = True # True
-        data['criterias']['beyond_supply_outputs'] = True  # True             # If only for smif: FAlse, for other plots: True
+        data['criterias']['write_to_txt'] = True
+        data['criterias']['beyond_supply_outputs'] = False              # If only for smif: FAlse, for other plots: True
         data['criterias']['plot_crit'] = True
         data['criterias']['plot_tech_lp'] = True
 
-        data['sim_param']['base_yr'] = 2015                             # Base year
+        data['sim_param']['base_yr'] = 2015 # Base year
         data['sim_param']['curr_yr'] = data['sim_param']['base_yr']
         self.user_data['base_yr'] = data['sim_param']['base_yr']
 
@@ -168,20 +166,18 @@ def before_model_run(self, data=None):
         # ----------------------------------
         # Calculating COOLING CDD PARAMETER
         # ----------------------------------
-        data['assumptions']['cdd_weekend_cfactors'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+        data['assumptions']['cdd_weekend_cfactors'] = hdd_cdd.calc_weekend_corr_f(
             data['assumptions']['model_yeardays_daytype'], 
             data['assumptions']['ss_t_cooling_weekend_factor'])
 
-        data['assumptions']['ss_weekend_f'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+        data['assumptions']['ss_weekend_f'] = hdd_cdd.calc_weekend_corr_f(
             data['assumptions']['model_yeardays_daytype'], 
             data['assumptions']['ss_weekend_factor'])
 
-        data['assumptions']['is_weekend_f'] = hdd_cdd.get_cdd_weekend_correctionfactors(
+        data['assumptions']['is_weekend_f'] = hdd_cdd.calc_weekend_corr_f(
             data['assumptions']['model_yeardays_daytype'], 
             data['assumptions']['is_weekend_factor'])
 
-
-
         # ------------
         # Load load profiles of technologies
         # ------------
@@ -365,17 +361,8 @@ def simulate(self, timestep, data=None):
         # ---------------------------------------------
         # Run energy demand model
         # ---------------------------------------------
-        if PROFILER:
-            profiler = Profiler(use_signal=False)
-            profiler.start()
-
         sim_obj = energy_demand_model(data)
 
-        if PROFILER:
-            profiler.stop()
-            logging.info("Profiler Results")
-            logging.info(profiler.output_text(unicode=True, color=True))
-
         # ------------------------------------------------
         # Validation base year: Hourly temporal validation
         # ------------------------------------------------

tests/profiles/test_hdd_cdd.py

@@ -3,6 +3,18 @@
 from energy_demand.profiles import hdd_cdd
 import numpy as np
 
+def calc_weekend_corr_f():
+    """Testing
+    """
+    wkend_factor = 0.5
+    model_yeardays_daytype = ['working_day', 'holiday']
+    result = hdd_cdd.calc_weekend_corr_f(
+        model_yeardays_daytype=model_yeardays_daytype,
+        wkend_factor=wkend_factor)
+
+    assert result[0] == 1.0
+    assert result[1] == wkend_factor
+
 def test_averaged_temp():
     """Testing
     """

yaml_parameters_scenario.yml

@@ -11,7 +11,7 @@
   climate_change_temp_d__Oct: 0
   climate_change_temp_d__Sep: 0
   climate_change_temp_diff_yr_until_changed: 2050
-  cooled_floorarea__ss_cooling_humidification: 0.3
+  cooled_floorarea__ss_cooling_humidification: 0.4
   cooled_floorarea_yr_until_changed: 2050
   demand_management_improvement__is_compressed_air: 0
   demand_management_improvement__is_drying_separation: 0