def archetypes_mapper(locator, update_architecture_dbf,
update_air_conditioning_systems_dbf,
update_indoor_comfort_dbf, update_internal_loads_dbf,
update_supply_systems_dbf, update_schedule_operation_cea,
buildings):
"""
algorithm to query building properties from statistical database
Archetypes_HVAC_properties.csv. for more info check the integrated demand
model of Fonseca et al. 2015. Appl. energy.
:param InputLocator locator: an InputLocator instance set to the scenario to work on
:param boolean update_architecture_dbf: if True, update the construction and architecture properties.
:param boolean update_indoor_comfort_dbf: if True, get properties about thermal comfort.
:param boolean update_air_conditioning_systems_dbf: if True, get properties about types of HVAC systems, otherwise False.
:param boolean update_internal_loads_dbf: if True, get properties about internal loads, otherwise False.
The following files are created by this script, depending on which flags were set:
- building_HVAC: .dbf
describes the queried properties of HVAC systems.
- architecture.dbf
describes the queried properties of architectural features
- building_thermal: .shp
describes the queried thermal properties of buildings
- indoor_comfort.shp
describes the queried thermal properties of buildings
"""
# get occupancy and age files
building_typology_df = dbf_to_dataframe(locator.get_building_typology())
# validate list of uses in case study
list_uses = get_list_of_uses_in_case_study(building_typology_df)
# get occupant densities from archetypes schedules
occupant_densities = {}
occ_densities = pd.read_excel(locator.get_database_use_types_properties(),
'INTERNAL_LOADS').set_index('code')
for use in list_uses:
if occ_densities.loc[use, 'Occ_m2pax'] > 0.0:
occupant_densities[use] = 1 / occ_densities.loc[use, 'Occ_m2pax']
else:
occupant_densities[use] = 0.0
# get properties about the construction and architecture
if update_architecture_dbf:
architecture_mapper(locator, building_typology_df)
# get properties about types of HVAC systems
if update_air_conditioning_systems_dbf:
aircon_mapper(locator, building_typology_df)
if update_indoor_comfort_dbf:
indoor_comfort_mapper(list_uses, locator, occupant_densities,
building_typology_df)
if update_internal_loads_dbf:
internal_loads_mapper(list_uses, locator, occupant_densities,
building_typology_df)
if update_schedule_operation_cea:
calc_mixed_schedule(locator, building_typology_df, buildings)
if update_supply_systems_dbf:
supply_mapper(locator, building_typology_df)
def data_helper(locator, region, overwrite_technology_folder,
update_architecture_dbf, update_HVAC_systems_dbf,
update_indoor_comfort_dbf, update_internal_loads_dbf,
update_supply_systems_dbf, update_schedule_operation_cea,
buildings):
"""
algorithm to query building properties from statistical database
Archetypes_HVAC_properties.csv. for more info check the integrated demand
model of Fonseca et al. 2015. Appl. energy.
:param InputLocator locator: an InputLocator instance set to the scenario to work on
:param boolean update_architecture_dbf: if True, update the construction and architecture properties.
:param boolean update_indoor_comfort_dbf: if True, get properties about thermal comfort.
:param boolean update_HVAC_systems_dbf: if True, get properties about types of HVAC systems, otherwise False.
:param boolean update_internal_loads_dbf: if True, get properties about internal loads, otherwise False.
The following files are created by this script, depending on which flags were set:
- building_HVAC: .dbf
describes the queried properties of HVAC systems.
- architecture.dbf
describes the queried properties of architectural features
- building_thermal: .shp
describes the queried thermal properties of buildings
- indoor_comfort.shp
describes the queried thermal properties of buildings
"""
# get technology database
if overwrite_technology_folder:
# copy all the region-specific archetypes to the scenario's technology folder
get_technology_related_databases(locator, region)
# get occupancy and age files
building_occupancy_df = dbf_to_dataframe(locator.get_building_occupancy())
building_age_df = dbf_to_dataframe(locator.get_building_age())
# validate list of uses in case study
list_uses = get_list_of_uses_in_case_study(building_occupancy_df)
# get occupant densities from archetypes schedules
occupant_densities = {}
occ_densities = pd.read_excel(locator.get_archetypes_properties(),
'INTERNAL_LOADS').set_index('Code')
for use in list_uses:
if occ_densities.loc[use, 'Occ_m2pax'] > 0.0:
occupant_densities[use] = 1 / occ_densities.loc[use, 'Occ_m2pax']
else:
occupant_densities[use] = 0.0
# prepare shapefile to store results (a shapefile with only names of buildings
names_df = building_age_df[['Name']]
# define main use:
building_occupancy_df['mainuse'] = calc_mainuse(building_occupancy_df,
list_uses)
# dataframe with joined data for categories
categories_df = building_occupancy_df.merge(building_age_df, on='Name')
# get properties about the construction and architecture
if update_architecture_dbf:
architecture_DB = pd.read_excel(locator.get_archetypes_properties(),
'ARCHITECTURE')
architecture_DB['Code'] = architecture_DB.apply(lambda x: calc_code(
x['building_use'], x['year_start'], x['year_end'], x['standard']),
axis=1)
categories_df['cat_built'] = calc_category(architecture_DB,
categories_df, 'built', 'C')
retrofit_category = ['envelope', 'roof', 'windows']
for category in retrofit_category:
categories_df['cat_' + category] = calc_category(
architecture_DB, categories_df, category, 'R')
prop_architecture_df = get_prop_architecture(categories_df,
architecture_DB,
list_uses)
# write to dbf file
prop_architecture_df_merged = names_df.merge(prop_architecture_df,
on="Name")
fields = [
'Name', 'Hs_ag', 'Hs_bg', 'Ns', 'Es', 'void_deck', 'wwr_north',
'wwr_west', 'wwr_east', 'wwr_south', 'type_cons', 'type_leak',
'type_roof', 'type_wall', 'type_win', 'type_shade'
]
dataframe_to_dbf(prop_architecture_df_merged[fields],
locator.get_building_architecture())
# get properties about types of HVAC systems
if update_HVAC_systems_dbf:
construction_properties_hvac = pd.read_excel(
locator.get_archetypes_properties(), 'HVAC')
construction_properties_hvac[
'Code'] = construction_properties_hvac.apply(
lambda x: calc_code(x['building_use'], x['year_start'], x[
'year_end'], x['standard']),
axis=1)
categories_df['cat_HVAC'] = calc_category(construction_properties_hvac,
categories_df, 'HVAC', 'R')
# define HVAC systems types
prop_HVAC_df = categories_df.merge(construction_properties_hvac,
left_on='cat_HVAC',
right_on='Code')
# write to shapefile
fields = [
'Name', 'type_cs', 'type_hs', 'type_dhw', 'type_ctrl', 'type_vent',
'heat_starts', 'heat_ends', 'cool_starts', 'cool_ends'
]
prop_HVAC_df_merged = names_df.merge(prop_HVAC_df, on="Name")
dataframe_to_dbf(prop_HVAC_df_merged[fields],
locator.get_building_air_conditioning())
if update_indoor_comfort_dbf:
comfort_DB = pd.read_excel(locator.get_archetypes_properties(),
'INDOOR_COMFORT')
# define comfort
prop_comfort_df = categories_df.merge(comfort_DB,
left_on='mainuse',
right_on='Code')
# write to shapefile
fields = [
'Name', 'Tcs_set_C', 'Ths_set_C', 'Tcs_setb_C', 'Ths_setb_C',
'Ve_lpspax', 'RH_min_pc', 'RH_max_pc'
]
prop_comfort_df_merged = names_df.merge(prop_comfort_df, on="Name")
prop_comfort_df_merged = calculate_average_multiuse(
fields, prop_comfort_df_merged, occupant_densities, list_uses,
comfort_DB)
dataframe_to_dbf(prop_comfort_df_merged[fields],
locator.get_building_comfort())
if update_internal_loads_dbf:
internal_DB = pd.read_excel(locator.get_archetypes_properties(),
'INTERNAL_LOADS')
# define comfort
prop_internal_df = categories_df.merge(internal_DB,
left_on='mainuse',
right_on='Code')
# write to shapefile
fields = [
'Name', 'Occ_m2pax', 'Qs_Wpax', 'X_ghpax', 'Ea_Wm2', 'El_Wm2',
'Ed_Wm2', 'Qcre_Wm2', 'Vww_lpdpax', 'Vw_lpdpax', 'Qhpro_Wm2',
'Qcpro_Wm2', 'Epro_Wm2'
]
prop_internal_df_merged = names_df.merge(prop_internal_df, on="Name")
prop_internal_df_merged = calculate_average_multiuse(
fields, prop_internal_df_merged, occupant_densities, list_uses,
internal_DB)
dataframe_to_dbf(prop_internal_df_merged[fields],
locator.get_building_internal())
if update_schedule_operation_cea:
if buildings == []:
buildings = locator.get_zone_building_names()
calc_mixed_schedule(locator, building_occupancy_df, buildings)
if update_supply_systems_dbf:
supply_DB = pd.read_excel(locator.get_archetypes_properties(),
'SUPPLY')
supply_DB['Code'] = supply_DB.apply(lambda x: calc_code(
x['building_use'], x['year_start'], x['year_end'], x['standard']),
axis=1)
categories_df['cat_supply'] = calc_category(supply_DB, categories_df,
'HVAC', 'R')
# define HVAC systems types
prop_supply_df = categories_df.merge(supply_DB,
left_on='cat_supply',
right_on='Code')
# write to shapefile
prop_supply_df_merged = names_df.merge(prop_supply_df, on="Name")
fields = ['Name', 'type_cs', 'type_hs', 'type_dhw', 'type_el']
dataframe_to_dbf(prop_supply_df_merged[fields],
locator.get_building_supply())