if __name__ == "__main__":
"""
"""
print('Start HIRE')
instrument_profiler = True
# Paths
path_main = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")
local_data_path = r'Y:\01-Data_NISMOD\data_energy_demand'
# Load data
base_data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(base_data)
base_data = data_loader.load_data_tech_profiles(base_data)
base_data = data_loader.load_data_profiles(base_data)
base_data['assumptions'] = assumptions.load_assumptions(base_data)
base_data['weather_stations'], base_data['temperature_data'] = data_loader.load_data_temperatures(
os.path.join(base_data['paths']['path_scripts_data'], 'weather_data')
)
# >>>>>>>>>>>>>>>DUMMY DATA GENERATION
# Population
# GVA
# Floor Area
# ...
base_data = data_loader.dummy_data_generation(base_data)
# <<<<<<<<<<<<<<<<<< FINISHED DUMMY GENERATION DATA
# Load data from script calculations
base_data = read_data.load_script_data(base_data)
def load_data_before_simulation(data, sim_yrs, config, curr_yr):
# ---------
# Configuration
# -----------
base_yr = config['CONFIG']['base_yr']
weather_yr_scenario = config['CONFIG']['weather_yr_scenario']
path_new_scenario = config['PATHS']['path_new_scenario']
data['data_path'] = os.path.normpath(config['PATHS']['path_local_data'])
data['processed_path'] = os.path.normpath(
config['PATHS']['path_processed_data'])
data['result_path'] = os.path.normpath(config['PATHS']['path_result_data'])
data['paths'] = config['CONFIG_DATA']
# Downloaded (FTP) data
data['local_paths'] = config['DATA_PATHS']
# ------------------------------------------------
# Load Inputs
# ------------------------------------------------
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, \
lookup_sector_enduses = data_loader.load_fuels(config['CONFIG_DATA'])
# ------------------------------------------------
# Load Assumptions
# ------------------------------------------------
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=base_yr,
weather_by=config['CONFIG']['user_defined_weather_by'],
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'],
curr_yr=curr_yr,
sim_yrs=sim_yrs,
paths=config['CONFIG_DATA'],
enduses=data['enduses'],
sectors=data['sectors'],
reg_nrs=len(data['regions']))
# ------------------------------------------
# Make selection of regions to model
# ------------------------------------------
if config['CRITERIA']['reg_selection']:
region_selection = read_data.get_region_selection(
os.path.join(config['DATA_PATHS']['local_path_datafolder'],
"region_definitions",
config['CRITERIA']['reg_selection_csv_name']))
#region_selection = ['E02003237', 'E02003238']
setattr(data['assumptions'], 'reg_nrs', len(region_selection))
else:
region_selection = data['regions']
# Create .ini file with simulation parameter
write_data.write_simulation_inifile(path_new_scenario, data,
region_selection)
# -------------------------------------------
# Weather year specific initialisations
# -------------------------------------------
path_folder_weather_yr = os.path.join(
os.path.join(path_new_scenario,
str(weather_yr_scenario) + "__" + "all_stations"))
data['weather_result_paths'] = basic_functions.get_result_paths(
path_folder_weather_yr)
folders_to_create = [
path_folder_weather_yr, data['weather_result_paths']['data_results'],
data['weather_result_paths']['data_results_PDF'],
data['weather_result_paths']['data_results_validation'],
data['weather_result_paths']['data_results_model_run_results_txt']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
# ------------------------------------------------
# Load load profiles of technologies
# ------------------------------------------------
data['tech_lp'] = data_loader.load_data_profiles(
config['CONFIG_DATA'], config['DATA_PATHS'],
data['assumptions'].model_yeardays,
data['assumptions'].model_yeardays_daytype)
# Obtain population data for disaggregation
if config['CRITERIA']['msoa_crit']:
name_population_dataset = config['DATA_PATHS'][
'path_population_data_for_disaggregation_msoa']
else:
name_population_dataset = config['DATA_PATHS'][
'path_population_data_for_disaggregation_lad']
data['pop_for_disag'] = data_loader.read_scenario_data(
name_population_dataset, region_name='region', value_name='value')
# ------------------------------------------------
# Load building related data
# ------------------------------------------------
if config['CRITERIA']['virtual_building_stock_criteria']:
data['scenario_data']['floor_area']['rs_floorarea'], data[
'scenario_data']['floor_area']['ss_floorarea'], data[
'service_building_count'], rs_regions_without_floorarea, ss_regions_without_floorarea = data_loader.floor_area_virtual_dw(
data['regions'],
data['sectors'],
config['DATA_PATHS'],
data['scenario_data']['population'][
data['assumptions'].base_yr],
base_yr=data['assumptions'].base_yr)
# Add all areas with no floor area data
data['assumptions'].update("rs_regions_without_floorarea",
rs_regions_without_floorarea)
data['assumptions'].update("ss_regions_without_floorarea",
ss_regions_without_floorarea)
else:
# ------------------------------------------------
# Load floor area directly from scenario
# ------------------------------------------------
data['scenario_data']['floor_area']['rs_floorarea'] = {}
data['scenario_data']['floor_area']['rs_floorarea'] = data[
'scenario_data']['rs_floorarea']
data['scenario_data']['floor_area']['ss_floorarea'] = data[
'scenario_data']['ss_floorarea']
data['scenario_data']['service_building_count'][
data['assumptions'].base_yr] = {}
return data
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'])
strategy_vars = data_loader.replace_variable(user_defined_vars, strategy_vars)
# Replace strategy variables not defined in csv files
strategy_vars_out = strategy_vars_def.autocomplete_strategy_vars(
strategy_vars,
narrative_crit=True)
data['assumptions'].update('strategy_vars', strategy_vars_out)
# -----------------------------------------------------------------------------
# Load necessary data
# -------------------------------------------------------------------------------
data['tech_lp'] = data_loader.load_data_profiles(
data['paths'], data['local_paths'],
data['assumptions'].model_yeardays,
data['assumptions'].model_yeardays_daytype,)
technologies = general_assumptions.update_technology_assumption(
data['assumptions'].technologies,
data['assumptions'].strategy_vars['f_eff_achieved'],
data['assumptions'].strategy_vars['gshp_fraction'])
data['assumptions'].technologies.update(technologies)
if config['CRITERIA']['virtual_building_stock_criteria']:
data['scenario_data']['floor_area']['rs_floorarea'], data['scenario_data']['floor_area']['ss_floorarea'], data['service_building_count'], rs_regions_without_floorarea, ss_regions_without_floorarea = data_loader.floor_area_virtual_dw(
data['regions'],
data['sectors'],
data['local_paths'],
data['scenario_data']['population'][data['assumptions'].base_yr],
data['assumptions'].base_yr)