def post_install_setup_minimum(args):
"""If not all data are available, this scripts allows to
create dummy datas (temperature and service sector load profiles)
"""
path_config_file = args.config_file
config = data_loader.read_config_file(path_config_file)
path_local_data = config['PATHS']['path_local_data']
# ==========================================
# Post installation setup witout access to non publicy available data
# ==========================================
print(
"... running initialisation scripts with only publicly available data")
local_paths = data_loader.get_local_paths(path_config_file)
# Create folders to input data
raw_folder = os.path.join(path_local_data, '_raw_data')
basic_functions.create_folder(raw_folder)
basic_functions.create_folder(config['PATHS']['path_processed_data'])
basic_functions.create_folder(local_paths['path_post_installation_data'])
basic_functions.create_folder(local_paths['load_profiles'])
basic_functions.create_folder(local_paths['rs_load_profile_txt'])
basic_functions.create_folder(local_paths['ss_load_profile_txt'])
# Load data
data = {}
data['paths'] = data_loader.load_paths(path_config_file)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data[
'fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(
data['paths'])
# Assumptions
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=2015,
weather_by=config['CONFIG']['user_defined_weather_by'],
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'],
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'])
# Read in residential submodel shapes
run(data['paths'], local_paths, config['CONFIG']['base_yr'])
# --------
# Dummy service sector load profiles
# --------
dummy_sectoral_load_profiles(local_paths, path_config_file)
print(
"Successfully finished post installation setup with open source data")
def run():
"""Function to write out assumptions
"""
path_main = os.path.dirname(
os.path.abspath(__file__))[:-25] #Remove 'energy_demand'
data = data_loader.load_paths(path_main,
'Y:\01-Data_NISMOD\data_energy_demand')
data = data_loader.load_fuels(data)
data['assumptions'] = load_assumptions(data)
# Write out temperature assumptions
'''write_data.write_out_temp_assumptions(
os.path.join(data['paths']['path_assumptions_db'], "assumptions_climate_change_temp.csv"),
data['assumptions']['climate_change_temp_diff_month'])
# Write out sigmoid parameters
write_data.write_out_sim_param(
os.path.join(
path_main,
'data',
'data_scripts',
'assumptions_from_db',
'assumptions_sim_param.csv'),
data['sim_param']
)
'''
return
def test_assign_by_fuel_tech_p():
"""
"""
path_main = resource_filename(Requirement.parse("energy_demand"),
'config_data')
# Load data
data = {}
data['paths'] = data_loader.load_paths(path_main)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(
data['paths'], data['lookups'])
data['local_paths'] = data_loader.load_local_paths(path_main)
#Load assumptions
base_yr = 2015
data['assumptions'] = non_param_assumptions.Assumptions(
base_yr=base_yr,
curr_yr=None,
simulated_yrs=None,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
param_assumptions.load_param_assump(data['paths'], data['local_paths'],
data['assumptions'])
rs_fuel_tech_p_by, ss_fuel_tech_p_by, is_fuel_tech_p_by = assumptions_fuel_shares.assign_by_fuel_tech_p(
data['enduses'], data['sectors'], data['lookups']['fueltypes'],
data['lookups']['fueltypes_nr'])
def dummy_sectoral_load_profiles(local_paths, path_main):
"""
"""
create_folders_to_file(os.path.join(local_paths['ss_load_profile_txt'], "dumm"), "_processed_data")
paths = data_loader.load_paths(path_main)
lu = lookup_tables.basic_lookups()
dict_enduses, dict_sectors, dict_fuels = data_loader.load_fuels(paths, lu)
for enduse in dict_enduses['ss_enduses']:
for sector in dict_sectors['ss_sectors']:
joint_string_name = str(sector) + "__" + str(enduse)
# Flat profiles
load_peak_shape_dh = np.full((24), 1)
shape_non_peak_y_dh = np.full((365, 24), 1/24)
shape_peak_yd_factor = 1.0
shape_non_peak_yd = np.full((365), 1/365)
write_data.create_txt_shapes(
joint_string_name,
local_paths['ss_load_profile_txt'],
load_peak_shape_dh,
shape_non_peak_y_dh,
shape_peak_yd_factor,
shape_non_peak_yd)
def test_assign_by_fuel_tech_p(config_file):
"""
"""
config = data_loader.read_config_file(config_file)
# Load data
data = {}
data['paths'] = config['CONFIG_DATA']
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data[
'fuels'], _, _ = data_loader.load_fuels(data['paths'])
data['local_paths'] = data_loader.get_local_paths(path_main)
#Load assumptions
base_yr = 2015
data['assumptions'] = general_assumptions.Assumptions(
submodels_names=['a'],
base_yr=base_yr,
curr_yr=None,
sim_yrs=None,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
strategy_vars_def.load_param_assump(data['paths'], data['local_paths'],
data['assumptions'])
fuel_tech_p_by = fuel_shares.assign_by_fuel_tech_p(
data['enduses'], data['sectors'], data['lookups']['fueltypes'],
data['lookups']['fueltypes_nr'])
def dummy_sectoral_load_profiles(local_paths, path_main):
"""Create dummy sectoral load profiles
Arguments
---------
local_paths : dict
Paths
path_main : str
Main path
"""
create_folders_to_file(
os.path.join(local_paths['ss_load_profile_txt'], "dumm"), "_processed_data")
paths = data_loader.load_paths(path_main)
dict_enduses, dict_sectors, _, _, _ = data_loader.load_fuels(paths)
for enduse in dict_enduses['service']:
for sector in dict_sectors['service']:
joint_string_name = str(sector) + "__" + str(enduse)
# Flat profiles
load_peak_shape_dh = np.full((24), 1)
shape_non_peak_y_dh = np.full((365, 24), 1/24)
shape_non_peak_yd = np.full((365), 1/365)
write_data.create_txt_shapes(
joint_string_name,
local_paths['ss_load_profile_txt'],
load_peak_shape_dh,
shape_non_peak_y_dh,
shape_non_peak_yd)
def run(path_main, local_data_path):
"""Function to run script
"""
print("... start script {}".format(os.path.basename(__file__)))
# Execute assumptions file to write out assumptions
temperature_data = read_weather_data_script_data(
os.path.join(path_main, 'data', 'data_scripts', 'weather_data',
'weather_data.csv'))
data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(data)
data['assumptions'] = assumptions.load_assumptions(data)
assumptions_temp_change = data['assumptions'][
'climate_change_temp_diff_month']
'''assumptions_temp_change = read_assumption(
os.path.join(
local_data_path,
'data',
'data_scripts',
'assumptions_from_db',
'assumptions_climate_change_temp.csv'
)
)
sim_param = s_shared_functions.read_assumption_sim_param(
os.path.join(
local_data_path,
'data',
'data_scripts',
'assumptions_from_db',
'assumptions_sim_param.csv'
)
)'''
sim_param = data['sim_param']
temp_climate_change = change_temp_climate_change(temperature_data,
assumptions_temp_change,
sim_param)
# Write out temp_climate_change
write_chanted_temp_data(
os.path.join(path_main, 'data', 'data_scripts', 'weather_data',
'weather_data_changed_climate.csv'), temp_climate_change)
print("... finished script {}".format(os.path.basename(__file__)))
return
def test_load_param_assump():
"""
"""
path_main = resource_filename(Requirement.parse("energy_demand"), "config_data")
# Load data
data = {}
data['paths'] = data_loader.load_paths(path_main)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(data['paths'], data['lookups'])
sim_param_expected = {}
sim_param_expected['base_yr'] = 2015
# Dummy test
assert sim_param_expected['base_yr'] == 2015
return
def run(path_main, local_data_path):
"""Function run script
"""
print("... start script {}".format(os.path.basename(__file__)))
# Load data and assumptions
base_data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(base_data)
base_data['assumptions'] = assumptions.load_assumptions(base_data)
base_data[
'weather_stations'], temperature_data = data_loader.load_data_temperatures(
os.path.join(base_data['paths']['path_scripts_data'],
'weather_data'))
base_data['temperature_data'] = {}
for weather_station, base_yr_temp in temperature_data.items():
base_data['temperature_data'][weather_station] = {2015: base_yr_temp}
# IMPROVE TODO: LOAD FLOOR AREA DATA
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
base_data = data_loader.dummy_data_generation(base_data)
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# Disaggregation
base_data = disaggregate_base_demand(base_data)
#Write to csv file disaggregated demand
write_disagg_fuel(
os.path.join(path_main, 'data', 'data_scripts', 'disaggregated',
'rs_fueldata_disagg.csv'),
base_data['rs_fueldata_disagg'])
write_disagg_fuel_sector(
os.path.join(path_main, 'data', 'data_scripts', 'disaggregated',
'ss_fueldata_disagg.csv'),
base_data['ss_fueldata_disagg'])
write_disagg_fuel_sector(
os.path.join(path_main, 'data', 'data_scripts', 'disaggregated',
'is_fueldata_disagg.csv'),
base_data['is_fueldata_disagg'])
write_disagg_fuel_ts(
os.path.join(path_main, 'data', 'data_scripts', 'disaggregated',
'ts_fueldata_disagg.csv'),
base_data['ts_fueldata_disagg'])
print("... finished script {}".format(os.path.basename(__file__)))
return
def test_load_non_param_assump():
"""
"""
path_main = resource_filename(Requirement.parse("energy_demand"), "config_data")
# Load data
data = {}
paths = data_loader.load_paths(path_main)
lu = lookup_tables.basic_lookups()
enduses, sectors, _ = data_loader.load_fuels(paths, lu)
non_param_assumptions.Assumptions(
base_yr=2015,
curr_yr=None,
simulated_yrs=None,
paths=paths,
enduses=enduses,
sectors=sectors,
fueltypes=lu['fueltypes'],
fueltypes_nr=lu['fueltypes_nr'])
return _, model_run_object
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 all other paths
# --------------------
data['paths'] = config['CONFIG_DATA']
data['local_paths'] = config['DATA_PATHS']
data['result_paths'] = basic_functions.get_result_paths(path_new_scenario)
for folder, folder_path in data['result_paths'].items():
basic_functions.create_folder(folder_path)
# ----------------------------------------------------------------------
# Load data
# ----------------------------------------------------------------------
data['scenario_data'] = defaultdict(dict)
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(data['paths'])
data['regions'] = read_data.get_region_names(name_region_set)
data['reg_coord'] = basic_functions.get_long_lat_decimal_degrees(read_data.get_region_centroids(name_region_set))
data['scenario_data']['population'] = data_loader.read_scenario_data(name_population_dataset)
data['scenario_data']['gva_industry'] = data_loader.read_scenario_data_gva(name_gva_dataset, all_dummy_data=False)
data['scenario_data']['gva_per_head'] = data_loader.read_scenario_data(name_gva_dataset_per_head)
# -----------------------------
# Assumptions
# -----------------------------
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=config['CONFIG']['base_yr'],
weather_by=config['CONFIG']['user_defined_weather_by'],
def run(path_main, local_data_path):
"""Function run script
"""
print("... start script {}".format(os.path.basename(__file__)))
path_out = os.path.join(path_main, 'data', 'data_scripts')
# Load data and assumptions
base_data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(base_data)
base_data['assumptions'] = assumptions.load_assumptions(base_data)
# Read in Services
rs_service_tech_by_p = read_data.read_service_data_service_tech_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'rs_service_tech_by_p.csv'))
ss_service_tech_by_p = read_data.read_service_data_service_tech_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'ss_service_tech_by_p.csv'))
is_service_tech_by_p = read_data.read_service_data_service_tech_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'is_service_tech_by_p.csv'))
rs_service_fueltype_by_p = read_data.read_service_fueltype_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'rs_service_fueltype_by_p.csv'))
ss_service_fueltype_by_p = read_data.read_service_fueltype_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'ss_service_fueltype_by_p.csv'))
is_service_fueltype_by_p = read_data.read_service_fueltype_by_p(
os.path.join(base_data['paths']['path_scripts_data'], 'services',
'is_service_fueltype_by_p.csv'))
# Calculate technologies with more, less and constant service based on service switch assumptions
rs_tech_increased_service, rs_tech_decreased_share, rs_tech_constant_share = get_tech_future_service(
rs_service_tech_by_p,
base_data['assumptions']['rs_share_service_tech_ey_p'])
ss_tech_increased_service, ss_tech_decreased_share, ss_tech_constant_share = get_tech_future_service(
ss_service_tech_by_p,
base_data['assumptions']['ss_share_service_tech_ey_p'])
is_tech_increased_service, is_tech_decreased_share, is_tech_constant_share = get_tech_future_service(
is_service_tech_by_p,
base_data['assumptions']['is_share_service_tech_ey_p'])
# Calculate sigmoid diffusion curves based on assumptions about fuel switches
# --Residential
rs_installed_tech, rs_sig_param_tech = get_sig_diffusion(
base_data, base_data['assumptions']['rs_service_switches'],
base_data['assumptions']['rs_fuel_switches'],
base_data['rs_all_enduses'], rs_tech_increased_service,
base_data['assumptions']['rs_share_service_tech_ey_p'],
base_data['assumptions']['rs_enduse_tech_maxL_by_p'],
rs_service_fueltype_by_p, rs_service_tech_by_p,
base_data['assumptions']['rs_fuel_tech_p_by'])
# --Service
ss_installed_tech, ss_sig_param_tech = get_sig_diffusion(
base_data, base_data['assumptions']['ss_service_switches'],
base_data['assumptions']['ss_fuel_switches'],
base_data['ss_all_enduses'], ss_tech_increased_service,
base_data['assumptions']['ss_share_service_tech_ey_p'],
base_data['assumptions']['ss_enduse_tech_maxL_by_p'],
ss_service_fueltype_by_p, ss_service_tech_by_p,
base_data['assumptions']['ss_fuel_tech_p_by'])
# --Industry
is_installed_tech, is_sig_param_tech = get_sig_diffusion(
base_data, base_data['assumptions']['is_service_switches'],
base_data['assumptions']['is_fuel_switches'],
base_data['is_all_enduses'], is_tech_increased_service,
base_data['assumptions']['is_share_service_tech_ey_p'],
base_data['assumptions']['is_enduse_tech_maxL_by_p'],
is_service_fueltype_by_p, is_service_tech_by_p,
base_data['assumptions']['is_fuel_tech_p_by'])
# Write out to csv
write_installed_tech(os.path.join(path_out, 'rs_installed_tech.csv'),
rs_installed_tech)
write_installed_tech(os.path.join(path_out, 'ss_installed_tech.csv'),
ss_installed_tech)
write_installed_tech(os.path.join(path_out, 'is_installed_tech.csv'),
is_installed_tech)
write_sig_param_tech(os.path.join(path_out, 'rs_sig_param_tech.csv'),
rs_sig_param_tech)
write_sig_param_tech(os.path.join(path_out, 'ss_sig_param_tech.csv'),
ss_sig_param_tech)
write_sig_param_tech(os.path.join(path_out, 'is_sig_param_tech.csv'),
is_sig_param_tech)
write_installed_tech(
os.path.join(path_out, 'rs_tech_increased_service.csv'),
rs_tech_increased_service)
write_installed_tech(
os.path.join(path_out, 'ss_tech_increased_service.csv'),
ss_tech_increased_service)
write_installed_tech(
os.path.join(path_out, 'is_tech_increased_service.csv'),
is_tech_increased_service)
write_installed_tech(os.path.join(path_out, 'rs_tech_decreased_share.csv'),
rs_tech_decreased_share)
write_installed_tech(os.path.join(path_out, 'ss_tech_decreased_share.csv'),
ss_tech_decreased_share)
write_installed_tech(os.path.join(path_out, 'is_tech_decreased_share.csv'),
is_tech_decreased_share)
write_installed_tech(os.path.join(path_out, 'rs_tech_constant_share.csv'),
rs_tech_constant_share)
write_installed_tech(os.path.join(path_out, 'ss_tech_constant_share.csv'),
ss_tech_constant_share)
write_installed_tech(os.path.join(path_out, 'is_tech_constant_share.csv'),
is_tech_constant_share)
print("... finished script {}".format(os.path.basename(__file__)))
return
def run(path_main, local_data_path):
"""Function to run script
"""
print("... start script {}".format(os.path.basename(__file__)))
path_out = os.path.join(path_main, 'data', 'data_scripts', 'services')
# Load data and assumptions
base_data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(base_data)
base_data['assumptions'] = assumptions.load_assumptions(base_data)
# RESIDENTIAL: Convert base year fuel input assumptions to energy service
rs_service_tech_by_p, rs_service_fueltype_tech_by_p, rs_service_fueltype_by_p = get_service_fueltype_tech(
base_data['assumptions']['technology_list'],
base_data['assumptions']['hybrid_technologies'],
base_data['lu_fueltype'],
base_data['assumptions']['rs_fuel_tech_p_by'],
base_data['rs_fuel_raw_data_enduses'],
base_data['assumptions']['technologies']
)
# SERVICE: Convert base year fuel input assumptions to energy service
fuels_aggregated_across_sectors = ss_sum_fuel_enduse_sectors(
base_data['ss_fuel_raw_data_enduses'],
base_data['ss_all_enduses'],
base_data['nr_of_fueltypes'])
ss_service_tech_by_p, ss_service_fueltype_tech_by_p, ss_service_fueltype_by_p = get_service_fueltype_tech(
base_data['assumptions']['technology_list'],
base_data['assumptions']['hybrid_technologies'],
base_data['lu_fueltype'],
base_data['assumptions']['ss_fuel_tech_p_by'],
fuels_aggregated_across_sectors,
base_data['assumptions']['technologies']
)
# INDUSTRY
fuels_aggregated_across_sectors = ss_sum_fuel_enduse_sectors(
base_data['is_fuel_raw_data_enduses'],
base_data['is_all_enduses'],
base_data['nr_of_fueltypes'])
is_service_tech_by_p, is_service_fueltype_tech_by_p, is_service_fueltype_by_p = get_service_fueltype_tech(
base_data['assumptions']['technology_list'],
base_data['assumptions']['hybrid_technologies'],
base_data['lu_fueltype'],
base_data['assumptions']['is_fuel_tech_p_by'],
fuels_aggregated_across_sectors,
base_data['assumptions']['technologies']
)
# Write to csv files
write_service_tech_by_p(
os.path.join(path_out, 'rs_service_tech_by_p.csv'),
rs_service_tech_by_p)
write_service_tech_by_p(
os.path.join(path_out, 'ss_service_tech_by_p.csv'),
ss_service_tech_by_p)
write_service_tech_by_p(
os.path.join(path_out, 'is_service_tech_by_p.csv'),
is_service_tech_by_p)
write_service_fueltype_tech_by_p(
os.path.join(path_out, 'rs_service_fueltype_tech_by_p.csv'),
rs_service_fueltype_tech_by_p)
write_service_fueltype_tech_by_p(
os.path.join(path_out, 'ss_service_fueltype_tech_by_p.csv'),
ss_service_fueltype_tech_by_p)
write_service_fueltype_tech_by_p(
os.path.join(path_out, 'is_service_fueltype_tech_by_p.csv'),
is_service_fueltype_tech_by_p)
write_service_fueltype_by_p(
os.path.join(path_out, 'rs_service_fueltype_by_p.csv'),
rs_service_fueltype_by_p)
write_service_fueltype_by_p(
os.path.join(path_out, 'ss_service_fueltype_by_p.csv'),
ss_service_fueltype_by_p)
write_service_fueltype_by_p(
os.path.join(path_out, 'is_service_fueltype_by_p.csv'),
is_service_fueltype_by_p)
print("... finished script {}".format(os.path.basename(__file__)))
return
def post_install_setup(args):
"""Run this function after installing the energy_demand
model with smif and putting the data folder with all necessary
data into a local drive. This scripts only needs to be
executed once after the energy_demand model has been installed
Arguments
----------
args : object
Arguments defined in ``./cli/__init__.py``
"""
print("... start running initialisation scripts")
# Paths
path_main = resource_filename(Requirement.parse("energy_demand"), "config_data")
path_results = resource_filename(Requirement.parse("energy_demand"), "results")
local_data_path = args.local_data
# Initialise logger
logger_setup.set_up_logger(
os.path.join(local_data_path, "logging_post_install_setup.log"))
logging.info("... start local energy demand calculations")
# Load data
base_yr = 2015
data = {}
data['paths'] = data_loader.load_paths(path_main)
data['local_paths'] = data_loader.load_local_paths(local_data_path)
data['result_paths'] = data_loader.load_result_paths(path_results)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(
data['paths'], data['lookups'])
# Assumptions
data['assumptions'] = non_param_assumptions.Assumptions(
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
# Delete all previous data from previous model runs
basic_functions.del_previous_setup(data['local_paths']['data_processed'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
# Create folders and subfolder for data_processed
basic_functions.create_folder(data['local_paths']['data_processed'])
basic_functions.create_folder(data['local_paths']['path_post_installation_data'])
basic_functions.create_folder(data['local_paths']['dir_raw_weather_data'])
basic_functions.create_folder(data['local_paths']['dir_changed_weather_station_data'])
basic_functions.create_folder(data['local_paths']['load_profiles'])
basic_functions.create_folder(data['local_paths']['rs_load_profile_txt'])
basic_functions.create_folder(data['local_paths']['ss_load_profile_txt'])
basic_functions.create_folder(data['local_paths']['dir_disaggregated'])
print("... Read in temperature data from raw files")
s_raw_weather_data.run(
data['local_paths'])
print("... Read in service submodel load profiles")
s_ss_raw_shapes.run(
data['paths'],
data['local_paths'],
data['lookups'])
print("... Read in residential submodel load profiles")
s_rs_raw_shapes.run(
data['paths'],
data['local_paths'],
base_yr)
print("... successfully finished setup")
return
def post_install_setup_minimum(args):
"""If not all data are available, this scripts allows to
create dummy datas (temperature and service sector load profiles)
Arguments
---------
path_local_data : str
Path to `energy_demand_data` folder
path_energy_demand : str
Path to energy demand python files
"""
path_energy_demand = resource_filename(Requirement.parse("energy_demand"), "config_data")
path_local_data = args.local_data
# ==========================================
# Post installation setup witout access to non publicy available data
# ==========================================
print("... running initialisation scripts with only publicly available data")
# Load paths
local_paths = data_loader.load_local_paths(path_local_data)
# Create folders to input data
raw_folder = os.path.join(path_local_data, '_raw_data')
processed_folder = os.path.join(path_local_data, '_processed_data')
basic_functions.create_folder(raw_folder)
basic_functions.create_folder(processed_folder)
basic_functions.create_folder(local_paths['path_post_installation_data'])
basic_functions.create_folder(local_paths['dir_raw_weather_data'])
basic_functions.create_folder(local_paths['dir_changed_weather_station_data'])
basic_functions.create_folder(local_paths['load_profiles'])
basic_functions.create_folder(local_paths['rs_load_profile_txt'])
basic_functions.create_folder(local_paths['ss_load_profile_txt'])
basic_functions.create_folder(local_paths['dir_disaggregated'])
# Load data
base_yr = 2015
data = {}
data['paths'] = data_loader.load_paths(path_energy_demand)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(
data['paths'], data['lookups'])
# Assumptions
data['assumptions'] = non_param_assumptions.Assumptions(
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
# Read in residential submodel shapes
run(data['paths'], local_paths, base_yr)
# ==========================================
# Create not publica available files
# ==========================================
# --------
# Generate dummy weather stations
# --------
dummy_raw_weather_station(local_paths)
# --------
# Generate dummy temperatures
# --------
dummy_raw_weather_data(local_paths)
# --------
# Dummy service sector load profiles
# --------
dummy_sectoral_load_profiles(local_paths, path_energy_demand)
print("Successfully finished post installation setup with open source 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
def run(path_main, local_data_path):
"""Function to run script
"""
print("... start script {}".format(os.path.basename(__file__)))
base_data = data_loader.load_paths(path_main, local_data_path)
base_data = data_loader.load_fuels(base_data)
base_data['assumptions'] = assumptions.load_assumptions(base_data)
_, ss_sectors, ss_enduses = read_data.read_csv_base_data_service(
os.path.join(path_main, 'data', 'submodel_service',
'data_service_by_fuel_end_uses.csv'),
base_data['nr_of_fueltypes'])
# Iterate sectors and read in shape
for sector in ss_sectors:
# Match electricity shapes for every sector
if sector == 'community_arts_leisure':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "Community")
elif sector == 'education':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "Education")
elif sector == 'emergency_services':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "_all_elec")
elif sector == 'health':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "Health")
elif sector == 'hospitality':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "_all_elec")
elif sector == 'military':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "_all_elec")
elif sector == 'offices':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "Offices")
elif sector == 'retail':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "Retail")
elif sector == 'storage':
sector_folder_path_elec = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "_all_elec")
else:
sys.exit(
"Error: The sector {} could not be assigned".format(sector))
# ------------------------------------------------------
# Assign same shape across all enduse for service sector
# ------------------------------------------------------
for enduse in ss_enduses:
print("Enduse service: {} in sector '{}'".format(enduse, sector))
# Select shape depending on enduse
if enduse in [
'ss_water_heating', 'ss_space_heating', 'ss_other_gas'
]: #TODO
folder_path = os.path.join(
base_data['paths']['folder_raw_carbon_trust'], "_all_gas")
else:
if enduse == 'ss_other_electricity' or enduse == 'ss_cooling_and_ventilation':
folder_path = os.path.join(
base_data['paths']['folder_raw_carbon_trust'],
"_all_elec")
else:
folder_path = sector_folder_path_elec
# Read in shape from carbon trust metering trial dataset
shape_non_peak_y_dh, load_peak_shape_dh, shape_peak_yd_factor, shape_non_peak_yd = read_raw_carbon_trust_data(
folder_path)
# Write shapes to txt
joint_string_name = str(sector) + "__" + str(enduse)
s_shared_functions.create_txt_shapes(
joint_string_name,
os.path.join(path_main, 'data', 'data_scripts',
'load_profiles', 'ss_submodel'),
load_peak_shape_dh, shape_non_peak_y_dh, shape_peak_yd_factor,
shape_non_peak_yd)
# ---------------------
# Compare Jan and Jul
# ---------------------
#ss_read_data.compare_jan_jul(main_dict_dayyear_absolute)
print("... finished script {}".format(os.path.basename(__file__)))
return
def post_install_setup(args):
"""Run this function after installing the energy_demand
model with smif and putting the data folder with all necessary
data into a local drive. This scripts only needs to be
executed once after the energy_demand model has been installed
Arguments
----------
args : object
Arguments defined in ``./cli/__init__.py``
"""
print("... start running initialisation scripts", flush=True)
path_config_file = args.local_data
config = data_loader.read_config_file(path_config_file)
local_data_path = config['PATHS']['path_local_data']
path_results = resource_filename(Requirement.parse("energy_demand"),
"results")
local_data_path = args.local_data
path_config = config['PATHS']['path_energy_demand_config']
base_yr = config['CONFIG']['base_yr']
data = {}
data['paths'] = config['CONFIG_DATA']
data['local_paths'] = config['DATA_PATHS']
data['result_paths'] = config['RESULT_DATA']
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, \
lookup_sector_enduses = data_loader.load_fuels(data['paths'])
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'])
# Delete all previous data from previous model runs
basic_functions.del_previous_setup(data['local_paths']['data_processed'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
basic_functions.del_previous_setup(
data['local_paths']['path_post_installation_data'])
# Create folders and subfolder for data_processed
folders_to_create = [
data['local_paths']['data_processed'],
data['local_paths']['path_post_installation_data'],
data['local_paths']['load_profiles'],
data['local_paths']['rs_load_profile_txt'],
data['local_paths']['ss_load_profile_txt']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
print("... Read in residential submodel load profiles", flush=True)
s_rs_raw_shapes.run(data['paths'], data['local_paths'], base_yr)
print("... Read in service submodel load profiles", flush=True)
s_ss_raw_shapes.run(data['paths'], data['local_paths'], data['lookups'])
# Input data preparation
print("Generate additional data", flush=True)
# Extract NISMOD population data
path_to_zip_file = os.path.join(
local_data_path, "population-economic-smif-csv-from-nismod-db.zip")
path_extraction = os.path.join(local_data_path, 'scenarios',
"MISTRAL_pop_gva")
zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')
zip_ref.extractall(path_extraction)
zip_ref.close()
# Complete gva and pop data for every sector
data_pop = os.path.join(local_data_path, "scenarios", "MISTRAL_pop_gva",
"data")
path_geography = os.path.join(
local_data_path, "scenarios",
"uk_pop_principal_2015_2050_MSOA_england.csv")
geography_name = "region" # "lad_uk_2016"
script_data_preparation_MISTRAL_pop_gva.run(
path_to_folder=data_pop,
path_MSOA_baseline=path_geography,
MSOA_calculations=False,
geography_name="region") # "lad_uk_2016"
print("... successfully finished setup")
return
config.read(os.path.join(path_config, 'wrapperconfig.ini'))
# --------
# Other configuration
# --------
base_yr = 2015
sim_yrs = [2015, 2050]
paths = data_loader.load_paths(path_main)
data_path = os.path.normpath(config['PATHS']['path_local_data'])
local_paths = data_loader.get_local_paths(data_path)
data = {}
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(paths)
assumptions = general_assumptions.Assumptions(base_yr=user_defined_base_yr,
curr_yr=2015,
sim_yrs=sim_yrs,
paths=paths,
enduses=data['enduses'],
sectors=data['sectors'])
# Write parameters to YAML file
_, _ = strategy_vars_def.load_param_assump(assumptions)
print("==========")
print("Finishged generating YAML file with scenario parameters")
print("==========")
data['criterias'][
'beyond_supply_outputs'] = True # Wheater all results besides integraded smif run are calculated
data['criterias'][
'plot_tech_lp'] = True # Wheater all individual load profils are plotted
# Paths
data['paths'] = data_loader.load_paths(path_main)
data['local_paths'] = data_loader.load_local_paths(local_data_path)
result_path = os.path.dirname(__file__).split(
"energy_demand\\energy_demand")[0]
data['result_paths'] = data_loader.load_result_paths(
os.path.join(result_path, '_result_data'))
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(
data['paths'], data['lookups'])
# local scrap
data['regions'] = data_loader.load_LAC_geocodes_info(
os.path.join(local_data_path, '_raw_data',
'_quick_and_dirty_spatial_disaggregation',
'infuse_dist_lyr_2011_saved.csv'))
#reg_new = {}
#for i in range(NR_OF_MODELLEd_REGIONS):
# _entries = list(data['regions'].keys())
# reg_new[_entries[i]] = data['regions'][_entries[i]]
#data['regions'] = reg_new
# GVA
gva_data = {}
