def test_get_season():
"""testing
"""
result = date_prop.get_season(year_to_model=2018)
assert result['summer'][
0] == 151 #2018 regular year, first of june julianday - 1 for python
assert result['autumn'][
0] == 243 #2018 regular year, first of june julianday - 1 for python
assert result['winter'][
0] == 334 #2018 regular year, first of june julianday - 1 for python
assert result['spring'][
0] == 59 #2018 regular year, first of june julianday - 1 for python
def main(path_data_ed, path_shapefile_input, path_out_plots, plot_crit_dict):
"""Figure II plots
"""
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
all_result_folders = os.listdir(path_data_ed)
paths_folders_result = []
weather_yrs = []
weather_station_per_y = {}
all_calculated_yrs_paths = []
for result_folder in all_result_folders:
try:
split_path_name = result_folder.split("__")
weather_yr = int(split_path_name[0])
weather_yrs.append(weather_yr)
try:
weather_station = int(split_path_name[1])
except:
weather_station = "all_stations"
try:
weather_station_per_y[weather_yr].append(weather_station)
except:
weather_station_per_y[weather_yr] = [weather_station]
# Collect all paths to simulation result folders
paths_folders_result.append(
os.path.join(path_data_ed, result_folder))
tupyle_yr_path = (weather_yr,
os.path.join(path_data_ed, result_folder))
all_calculated_yrs_paths.append(tupyle_yr_path)
except ValueError:
pass
# -----------
# Used across different plots
# -----------
data = {}
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(os.path.join(path_data_ed))
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions'][
'yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
population_data = read_data.read_scenaric_population_data(
os.path.join(path_data_ed, 'model_run_pop'))
####################################################################
# Plotting weather variability results for all weather stations (Fig 2b)
####################################################################
weather_yr_container = defaultdict(dict)
for weather_yr, result_folder in all_calculated_yrs_paths:
results_container = read_data.read_in_results(
os.path.join(result_folder, 'model_run_results_txt'),
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
weather_yr_container['tot_fueltype_yh'][
weather_yr] = results_container[
'tot_fueltype_yh'] #tot_fueltype_yh
weather_yr_container['results_enduse_every_year'][
weather_yr] = results_container['ed_fueltype_regs_yh']
# ####################################################################
# Create plot with regional and non-regional plots for second paper
# Compare hdd calculations and disaggregation of regional and local
# ####################################################################
if plot_crit_dict['plot_scenarios_sorted']:
fig_p2_annual_hours_sorted.run(
data_input=weather_yr_container['results_enduse_every_year'],
regions=data['regions'],
simulation_yrs_to_plot=[2015], # Simulation year to plot
fueltype_str='electricity',
path_shapefile=path_shapefile_input,
fig_name=os.path.join(path_out_plots,
"fig_paper_IIb_weather_var_map.pdf"))
def plot_fig_spatio_temporal_validation(path_regional_calculations,
path_rolling_elec_demand,
path_temporal_elec_validation,
path_temporal_gas_validation,
path_non_regional_elec_2015,
path_out_plots,
plot_show=False):
"""
Create plot with regional and non-regional plots for second paper
Compare hdd calculations and disaggregation of regional and local
"""
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
all_result_folders = os.listdir(path_regional_calculations)
paths_folders_result = []
data_container = defaultdict(dict)
ed_fueltype_regs_yh = defaultdict(dict)
weather_yr_station_tot_fueltype_yh = defaultdict(dict)
residential_results = defaultdict(dict)
for scenario_folder in all_result_folders:
result_folders = os.listdir(
os.path.join(path_regional_calculations, scenario_folder))
for result_folder in result_folders:
try:
split_path_name = result_folder.split("__")
weather_yr = int(split_path_name[0])
try:
weather_station = int(split_path_name[1])
except:
weather_station = "all_stations"
paths_folders_result.append(
os.path.join(path_regional_calculations, result_folder))
data = {}
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(
os.path.join(
path_regional_calculations,
all_result_folders[0])) # last result folder
data['assumptions']['seasons'] = date_prop.get_season(
year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data[
'assumptions']['yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
results_container = read_data.read_in_results(
os.path.join(path_regional_calculations, scenario_folder,
"{}__{}".format(weather_yr, weather_station),
'model_run_results_txt'),
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
weather_yr_station_tot_fueltype_yh[weather_yr][
weather_station] = results_container['tot_fueltype_yh']
ed_fueltype_regs_yh[weather_yr][
weather_station] = results_container['ed_fueltype_regs_yh']
residential_results[weather_yr][
weather_station] = results_container['residential_results']
except ValueError:
pass
data_container['ed_fueltype_regs_yh'] = ed_fueltype_regs_yh
data_container['tot_fueltype_yh'] = weather_yr_station_tot_fueltype_yh
data_container['residential_results'] = residential_results
data_container = dict(data_container)
# -------------------------------------------------
# Collect non regional 2015 elec data
# Calculated with all regional weather stations
# -------------------------------------------------
year_non_regional = 2015
path_with_txt = os.path.join(
path_non_regional_elec_2015, "{}__{}".format(str(year_non_regional),
"all_stations"),
'model_run_results_txt')
demand_year_non_regional = read_data.read_in_results(
path_with_txt, data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
tot_fueltype_yh = demand_year_non_regional['tot_fueltype_yh']
fueltype_int = tech_related.get_fueltype_int('electricity')
non_regional_elec_2015 = tot_fueltype_yh[year_non_regional][fueltype_int]
# ---Collect real electricity data of year 2015
elec_2015_indo, _ = elec_national_data.read_raw_elec_2015(
path_rolling_elec_demand)
# Factor data as total sum is not identical
f_diff_elec = np.sum(non_regional_elec_2015) / np.sum(elec_2015_indo)
elec_factored_yh = f_diff_elec * elec_2015_indo
# *****************************************************************
# Temporal validation
# Compare regional and non regional and actual demand over time
# *****************************************************************
simulation_yr_to_plot = 2015
winter_week, spring_week, summer_week, autumn_week = date_prop.get_seasonal_weeks(
)
# Peak day
peak_day, _ = enduse_func.get_peak_day_single_fueltype(elec_factored_yh)
# Convert days to hours
period_to_plot = list(range(0, 400))
period_to_plot = date_prop.get_8760_hrs_from_yeardays(winter_week)
period_to_plot = date_prop.get_8760_hrs_from_yeardays([peak_day])
period_to_plot_winter = date_prop.get_8760_hrs_from_yeardays(winter_week)
period_to_plot_spring = date_prop.get_8760_hrs_from_yeardays(spring_week)
fig_p2_temporal_validation.run_fig_p2_temporal_validation(
data_input=data_container['tot_fueltype_yh'],
weather_yr=2015,
fueltype_str='electricity',
simulation_yr_to_plot=simulation_yr_to_plot, # Simulation year to plot
period_h=period_to_plot,
validation_elec_2015=elec_factored_yh,
non_regional_elec_2015=non_regional_elec_2015,
fig_name=os.path.join(path_out_plots, "temporal_validation_elec.pdf"),
titel="yearday: {}".format(peak_day),
y_lim_val=55,
plot_validation=False,
plot_show=plot_show)
fueltype_gas = tech_related.get_fueltype_int('gas')
fig_p2_temporal_validation.run_fig_p2_temporal_validation(
data_input=data_container['tot_fueltype_yh'],
weather_yr=2015,
fueltype_str='gas',
simulation_yr_to_plot=simulation_yr_to_plot, # Simulation year to plot
period_h=period_to_plot,
validation_elec_2015=None,
non_regional_elec_2015=tot_fueltype_yh[year_non_regional]
[fueltype_gas],
fig_name=os.path.join(path_out_plots, "temporal_validation_gas.pdf"),
titel="yearday: {}".format(peak_day),
y_lim_val=250,
plot_validation=False,
plot_show=plot_show)
# -------------------
# Spatial validation (not with maps)
# -------------------
# non_regional: All weather station, spatially disaggregated TODO Give BETTER NAMES
# regional: Only one weather station for whole countr but still data for every region
weather_yr = 2015
fig_p2_spatial_val.run(
simulation_yr_to_plot=simulation_yr_to_plot,
demand_year_non_regional=demand_year_non_regional[
'residential_results'][weather_yr],
demand_year_regional=data_container['residential_results'][weather_yr],
fueltypes=data['lookups']['fueltypes'],
fig_path=path_out_plots,
path_temporal_elec_validation=path_temporal_elec_validation,
path_temporal_gas_validation=path_temporal_gas_validation,
regions=data['regions'],
plot_crit=plot_show)
def __init__(self,
lookup_enduses=None,
lookup_sector_enduses=None,
base_yr=None,
weather_by=None,
simulation_end_yr=None,
curr_yr=None,
sim_yrs=None,
paths=None,
enduses=None,
sectors=None,
reg_nrs=None):
"""Constructor
"""
self.lookup_enduses = lookup_enduses
self.lookup_sector_enduses = lookup_sector_enduses
self.submodels_names = lookup_tables.basic_lookups()['submodels_names']
self.nr_of_submodels = len(self.submodels_names)
self.fueltypes = lookup_tables.basic_lookups()['fueltypes']
self.fueltypes_nr = lookup_tables.basic_lookups()['fueltypes_nr']
self.base_yr = base_yr
self.weather_by = weather_by
self.reg_nrs = reg_nrs
self.simulation_end_yr = simulation_end_yr
self.curr_yr = curr_yr
self.sim_yrs = sim_yrs
# ============================================================
# Spatially modelled variables
#
# If spatial explicit diffusion is modelled, all parameters
# or technologies having a spatial explicit diffusion need
# to be defined.
# ============================================================
self.spatial_explicit_diffusion = 0 #0: False, 1: True
# Define all variables which are affected by regional diffusion
self.spatially_modelled_vars = [] # ['smart_meter_p']
# Define technologies which are affected by spatial explicit diffusion
self.techs_affected_spatial_f = ['heat_pumps_electricity']
# Max penetration speed
self.speed_con_max = 1 #1.5 # 1: uniform distribution >1: regional differences
# ============================================================
# Model calibration factors
# ============================================================
#
# These calibration factors are used to match the modelled
# electrictiy demand better with the validation data.
#
# Weekend effects are used to distribut energy demands
# between working and weekend days. With help of these
# factors, the demand on weekends and holidays can be
# be lowered compared to working days.
# This factor can be applied either directly to an enduse
# or to the hdd or cdd calculations (to correct cooling
# or heating demand)
#
# f_ss_cooling_weekend : float
# Weekend effect for cooling enduses
# f_ss_weekend : float
# WWeekend effect for service submodel enduses
# f_is_weekend : float
# Weekend effect for industry submodel enduses
# f_mixed_floorarea : float
# Share of floor_area which is assigned to either
# residential or non_residential floor area
# ------------------------------------------------------------
self.f_ss_cooling_weekend = 0.45 # Temporal calibration factor
self.f_ss_weekend = 0.8 # Temporal calibration factor
self.f_is_weekend = 0.45 # Temporal calibration factor
# ============================================================
# Modelled day related factors
# ============================================================
# model_yeardays_date : dict
# Contains for the base year for each days
# the information wheter this is a working or holiday
# ------------------------------------------------------------
self.model_yeardays = list(range(365))
# Calculate dates
self.model_yeardays_date = []
for yearday in self.model_yeardays:
self.model_yeardays_date.append(
date_prop.yearday_to_date(base_yr, yearday))
# ============================================================
# Dwelling stock related assumptions
# ============================================================
#
# Assumptions to generate a virtual dwelling stock
#
# assump_diff_floorarea_pp : float
# Change in floor area per person (%, 1=100%)
# assump_diff_floorarea_pp_yr_until_changed : int
# Year until this change in floor area happens
# dwtype_distr_by : dict
# Housing Stock Distribution by Type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_distr_fy : dict
# welling type distribution end year
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_by : dict
# Floor area per dwelling type (Annex Table 3.1)
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_fy : dict
# Floor area per dwelling type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_age_distr : dict
# Floor area per dwelling type
# Source: Housing Energy Fact Sheet)
# yr_until_changed : int
# Year until change is realised
#
# https://www.gov.uk/government/statistics/english-housing-survey-2014-to-2015-housing-stock-report
# ------------------------------------------------------------
yr_until_changed_all_things = 2050
self.dwtype_distr_by = {
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_distr_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_floorarea_by = {
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
self.dwtype_floorarea_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
# (Average builing age within age class, fraction)
# The newest category of 2015 is added to implement change in refurbishing rate
# For the base year, this is set to zero (if e.g. with future scenario set to 5%, then
# proportionally to base year distribution number of houses are refurbished)
self.dwtype_age_distr = {
2015: {
'1918': 0.21,
'1941': 0.36,
'1977.5': 0.3,
'1996.5': 0.08,
'2002': 0.05
}
}
# ============================================================
# Scenario drivers
# ============================================================
#
# For every enduse the relevant factors which affect enduse
# consumption can be added in a list.
#
# Note: If e.g. floorarea and population are added, the
# effects will be overestimates (i.e. no multi-
# collinearity are considered).
#
# scenario_drivers : dict
# Scenario drivers per enduse
# ------------------------------------------------------------
self.scenario_drivers = {
# --Residential
'rs_space_heating':
['floorarea',
'hlc'], # Do not use HDD or pop because otherweise double count
'rs_water_heating': ['population'],
'rs_lighting': ['population', 'floorarea'],
'rs_cooking': ['population'],
'rs_cold': ['population'],
'rs_wet': ['population'],
'rs_consumer_electronics': ['population', 'gva'],
'rs_home_computing': ['population'],
# --Service
'ss_space_heating': ['floorarea'],
'ss_water_heating': ['population'],
'ss_lighting': ['floorarea'],
'ss_catering': ['population'],
'ss_ICT_equipment': ['population'],
'ss_cooling_humidification': ['floorarea', 'population'],
'ss_fans': ['floorarea', 'population'],
'ss_small_power': ['population'],
'ss_cooled_storage': ['population'],
'ss_other_gas': ['population'],
'ss_other_electricity': ['population'],
# Industry
'is_high_temp_process': ['gva'],
'is_low_temp_process': ['gva'],
'is_drying_separation': ['gva'],
'is_motors': ['gva'],
'is_compressed_air': ['gva'],
'is_lighting': ['gva'],
'is_space_heating': ['gva'],
'is_other': ['gva'],
'is_refrigeration': ['gva']
}
# ============================================================
# Cooling related assumptions
# ============================================================
# assump_cooling_floorarea : int
# The percentage of cooled floor space in the base year
#
# Literature
# ----------
# Abela, A. et al. (2016). Study on Energy Use by Air
# Conditioning. Bre, (June), 31. Retrieved from
# https://www.bre.co.uk/filelibrary/pdf/projects/aircon-energy-use/StudyOnEnergyUseByAirConditioningFinalReport.pdf
# ------------------------------------------------------------
# See Abela et al. (2016) & Carbon Trust. (2012). Air conditioning. Maximising comfort, minimising energy consumption
self.cooled_ss_floorarea_by = 0.35
# ============================================================
# Smart meter related base year assumptions
# ============================================================
# smart_meter_p_by : int
# The percentage of households with smart meters in by
# ------------------------------------------------------------
self.smart_meter_assump = {}
# Currently in 2017 8.6 mio smart meter installed of 27.2 mio households --> 31.6%
# https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/671930/Smart_Meters_2017_update.pdf)
# In 2015, 5.8 % percent of all househods had one: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/533060/2016_Q1_Smart_Meters_Report.pdf
self.smart_meter_assump['smart_meter_p_by'] = 0.05
# Long term smart meter induced general savings, purley as
# a result of having a smart meter (e.g. 0.03 --> 3% savings)
# DECC 2015: Smart Metering Early Learning Project: Synthesis report
# https://www.gov.uk/government/publications/smart-metering-early-learning-project-and-small-scale-behaviour-trials
# Reasonable assumption is between 0.03 and 0.01 (DECC 2015)
self.smart_meter_assump['savings_smart_meter'] = {
# Residential
'rs_cold': 0.03,
'rs_cooking': 0.03,
'rs_lighting': 0.03,
'rs_wet': 0.03,
'rs_consumer_electronics': 0.03,
'rs_home_computing': 0.03,
'rs_space_heating': 0.03,
'rs_water_heating': 0.03,
# Service
'ss_space_heating': 0.03,
'ss_water_heating': 0.03,
'ss_cooling_humidification': 0.03,
'ss_fans': 0.03,
'ss_lighting': 0.03,
'ss_catering': 0.03,
'ss_small_power': 0.03,
'ss_ICT_equipment': 0.03,
'ss_cooled_storage': 0.03,
'ss_other_gas': 0.03,
'ss_other_electricity': 0.03,
# Industry submodule
'is_high_temp_process': 0,
'is_low_temp_process': 0,
'is_drying_separation': 0,
'is_motors': 0,
'is_compressed_air': 0,
'is_lighting': 0,
'is_space_heating': 0,
'is_other': 0,
'is_refrigeration': 0
}
# ============================================================
# Base temperature assumptions
# ============================================================
#
# Parameters related to smart metering
#
# rs_t_heating : int
# Residential submodel base temp of heating of base year
# rs_t_cooling_by : int
# Residential submodel base temp of cooling of base year
# ...
#
# Note
# ----
# Because demand for cooling cannot directly be linked to
# calculated cdd, the paramters 'ss_t_base_cooling' is used
# as a calibration factor. By artifiallcy lowering this
# parameter, the energy demand assignement over the days
# in a year is improved.
# ------------------------------------------------------------
t_bases = {
'rs_t_heating': 15.5,
'ss_t_heating': 15.5,
'ss_t_cooling': 5,
'is_t_heating': 15.5
}
self.t_bases = DummyClass(t_bases)
# ============================================================
# Enduses lists affed by hdd/cdd
# ============================================================
#
# These lists show for which enduses temperature related
# calculations are performed.
#
# enduse_space_heating : list
# All enduses for which hdd are used for yd calculations
# ss_enduse_space_cooling : list
# All service submodel enduses for which cdd are used for
# yd calculations
# ------------------------------------------------------------
self.enduse_space_heating = [
'rs_space_heating', 'ss_space_heating', 'is_space_heating'
]
self.ss_enduse_space_cooling = ['ss_cooling_humidification']
# ============================================================
# Industry related
#
# High temperature processing (high_temp_ process) dominates
# energy consumption in the iron and steel
#
# ---- Steel production - Enduse: is_high_temp_process, Sector: basic_metals
# With industry service switch, the future shares of 'is_temp_high_process'
# in sector 'basic_metals' can be set for 'basic_oxygen_furnace',
# 'electric_arc_furnace', and 'SNG_furnace' can be specified
#
# ---- Cement production - Enduse: is_high_temp_process, Sector: non_metallic_mineral_products
# Dry kilns, semidry kilns can be set
# ============================================================
# Share of cold rolling in steel manufacturing
self.p_cold_rolling_steel_by = 0.2 # Estimated based on https://aceroplatea.es/docs/EuropeanSteelFigures_2015.pdf
self.eff_cold_rolling_process = 1.8 # 80% more efficient than hot rolling Fruehan et al. (2002)
self.eff_hot_rolling_process = 1.0 # 100% assumed efficiency
# ============================================================
# Assumption related to heat pump technologies
# ============================================================
#
# Assumptions related to technologies
#
# gshp_fraction : list
# Fraction of installed gshp_fraction heat pumps in base year
# ASHP = 1 - gshp_fraction
# ------------------------------------------------------------
self.gshp_fraction = 0.1
# Load defined technologies
self.technologies, self.tech_list = read_data.read_technologies(
paths['path_technologies'])
self.installed_heat_pump_by = tech_related.generate_ashp_gshp_split(
self.gshp_fraction)
# Add heat pumps to technologies
self.technologies, self.tech_list[
'heating_non_const'], self.heat_pumps = tech_related.generate_heat_pump_from_split(
self.technologies, self.installed_heat_pump_by, self.fueltypes)
# ============================================================
# Fuel Stock Definition
# Provide for every fueltype of an enduse the share of fuel
# which is used by technologies in the base year
# ============================================================$
fuel_tech_p_by = fuel_shares.assign_by_fuel_tech_p(
enduses, sectors, self.fueltypes, self.fueltypes_nr)
# ========================================
# Get technologies of an enduse and sector
# ========================================
self.specified_tech_enduse_by = helpers.get_def_techs(fuel_tech_p_by)
_specified_tech_enduse_by = helpers.add_undef_techs(
self.heat_pumps, self.specified_tech_enduse_by,
self.enduse_space_heating)
self.specified_tech_enduse_by = _specified_tech_enduse_by
# ========================================
# General other info
# ========================================
self.seasons = date_prop.get_season(year_to_model=base_yr)
self.model_yeardays_daytype, self.yeardays_month, self.yeardays_month_days = date_prop.get_yeardays_daytype(
year_to_model=base_yr)
# ========================================
# Helper functions
# ========================================
self.fuel_tech_p_by, self.specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies, fuel_tech_p_by, self.specified_tech_enduse_by)
# ========================================
# Calculations with assumptions
# ========================================
self.cdd_weekend_cfactors = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_cooling_weekend)
self.ss_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_weekend)
self.is_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_is_weekend)
# ========================================
# Testing
# ========================================
testing_functions.testing_fuel_tech_shares(self.fuel_tech_p_by)
testing_functions.testing_tech_defined(self.technologies,
self.specified_tech_enduse_by)
def process_scenarios(path_to_scenarios, year_to_model=2015):
"""Iterate folder with scenario results and plot charts
Arguments
----------
path_to_scenarios : str
Path to folders with stored results
year_to_model : int, default=2015
Year of base year
"""
# -----------
# Charts to plot
# -----------
heat_pump_range_plot = True # Plot of changing scenario values stored in scenario name
# Delete folder results if existing
path_result_folder = os.path.join(
path_to_scenarios, "__results_hp_chart")
basic_functions.delete_folder(path_result_folder)
seasons = date_prop.get_season(
year_to_model=year_to_model)
model_yeardays_daytype, _, _ = date_prop.get_yeardays_daytype(
year_to_model=year_to_model)
lookups = lookup_tables.basic_lookups()
# Get all folders with scenario run results (name of folder is scenario)
scenarios_hp = os.listdir(path_to_scenarios)
scenario_data = {}
for scenario_hp in scenarios_hp:
print("HP SCENARIO " + str(scenario_hp))
print(path_to_scenarios)
scenario_data[scenario_hp] = {}
# Simulation information is read in from .ini file for results
path_fist_scenario = os.path.join(path_to_scenarios, scenario_hp)
# -------------------------------
# Iterate folders and get results
# -------------------------------
scenarios = os.listdir(path_fist_scenario)
for scenario in scenarios:
enduses, assumptions, reg_nrs, regions = data_loader.load_ini_param(
os.path.join(path_fist_scenario, scenario))
# Add scenario name to folder
scenario_data[scenario_hp][scenario] = {}
path_to_result_files = os.path.join(
path_fist_scenario,
scenario,
'model_run_results_txt')
scenario_data[scenario_hp][scenario] = read_data.read_in_results(
path_result=path_to_result_files,
seasons=seasons,
model_yeardays_daytype=model_yeardays_daytype)
# -----------------------
# Generate result folder
# -----------------------
basic_functions.create_folder(path_result_folder)
# -------------------------------
# Generate plot with heat pump ranges
# -------------------------------
if heat_pump_range_plot:
plotting_multiple_scenarios.plot_heat_pump_chart_multiple(
lookups,
regions,
hp_scenario_data=scenario_data,
fig_name=os.path.join(path_result_folder, "comparison_hp_share_peak_h.pdf"),
txt_name=os.path.join(path_result_folder, "comparison_hp_share_peak_h.txt"),
fueltype_str_input='electricity',
plotshow=True)
return
def main(path_data_energy_demand, path_shapefile_input):
"""Read in all results and plot PDFs
Arguments
----------
path_data_energy_demand : str
Path to results
path_shapefile_input : str
Path to shapefile
"""
print("Start processing")
# ---------
# Criterias
# ---------
write_shapefiles = False # Write shapefiles
spatial_results = True # Spatial geopanda maps
# Set up logger
logger_setup.set_up_logger(
os.path.join(path_data_energy_demand, "plotting.log"))
# ------------------
# Load necessary inputs for read in
# ------------------
data = {}
data['local_paths'] = data_loader.load_local_paths(path_data_energy_demand)
data['result_paths'] = data_loader.load_result_paths(
os.path.join(path_data_energy_demand, '_result_data'))
data['lookups'] = lookup_tables.basic_lookups()
# ---------------
# Folder cleaning
# ---------------
basic_functions.del_previous_setup(
data['result_paths']['data_results_PDF'])
basic_functions.del_previous_setup(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(data['result_paths']['data_results_PDF'])
basic_functions.create_folder(
data['result_paths']['data_results_shapefiles'])
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['reg_nrs'], data[
'regions'] = data_loader.load_ini_param(
os.path.join(path_data_energy_demand, '_result_data'))
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions'][
'yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_model_yeardays_daytype(
year_to_model=2015)
# Read scenario data
data['scenario_data'] = {}
data['scenario_data'][
'population'] = read_data.read_scenaric_population_data(
data['result_paths']['model_run_pop'])
# --------------------------------------------
# Reading in results from different model runs
# Read in and plot in same step if memory is a problem
# --------------------------------------------
results_container = read_data.read_in_results(
data['result_paths']['data_results_model_runs'],
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
# ----------------
# Write results to CSV files and merge with shapefile
# ----------------
if write_shapefiles:
write_data.create_shp_results(data, results_container,
data['local_paths'], data['lookups'],
data['regions'])
# ------------------------------
# Plotting other results
# ------------------------------
plotting_results.run_all_plot_functions(results_container, data['reg_nrs'],
data['regions'], data['lookups'],
data['result_paths'],
data['assumptions'],
data['enduses'])
# ------------------------------
# Plotting spatial results
# ------------------------------
print("... plotting spatial results")
if spatial_results:
logging.info("Create spatial geopanda files")
result_mapping.create_geopanda_files(
data, results_container,
data['result_paths']['data_results_shapefiles'], data['regions'],
data['lookups']['fueltypes_nr'], data['lookups']['fueltypes'],
path_shapefile_input)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
def __init__(self,
base_yr=None,
curr_yr=None,
simulated_yrs=None,
paths=None,
enduses=None,
sectors=None,
fueltypes=None,
fueltypes_nr=None):
yr_until_changed_all_things = 2050
# Simulation parameters
self.base_yr = base_yr
self.curr_yr = curr_yr
self.simulated_yrs = simulated_yrs
# ============================================================
# Spatially modelled variables
# ============================================================
# Define all variables which are affected by regional diffusion
self.spatially_modelled_vars = ['smart_meter_improvement_p']
# Define technologies which are affected by spatial explicit diffusion
self.techs_affected_spatial_f = ['heat_pumps_electricity']
# ============================================================
# Model calibration factors
# ============================================================
#
# These calibration factors are used to match the modelled
# electrictiy demand better with the validation data.
#
# Weekend effects are used to distribut energy demands
# between working and weekend days. With help of these
# factors, the demand on weekends and holidays can be
# be lowered compared to working days.
# This factor can be applied either directly to an enduse
# or to the hdd or cdd calculations (to correct cooling
# or heating demand)
#
# f_ss_cooling_weekend : float
# Weekend effect for cooling enduses
# f_ss_weekend : float
# WWeekend effect for service submodel enduses
# f_is_weekend : float
# Weekend effect for industry submodel enduses
# f_mixed_floorarea : float
# Share of floor_area which is assigned to either
# residential or non_residential floor area
# ------------------------------------------------------------
# Temporal calibration factors
self.f_ss_cooling_weekend = 0.45 # 0.55
self.f_ss_weekend = 0.75 # 0.7
self.f_is_weekend = 0.45 # 0.4
# Spatial calibration factor
self.f_mixed_floorarea = 0.5 # 0.5
# ============================================================
# Modelled day related factors
# ============================================================
#
# model_yeardays_nrs : int
# Number of modelled yeardays (default=365)
# model_yearhours_nrs : int
# Number of modelled yearhours (default=8760)
# model_yeardays_date : dict
# Contains for the base year for each days
# the information wheter this is a working or holiday
# ------------------------------------------------------------
self.model_yeardays = list(range(365))
# Calculate dates
self.model_yeardays_date = []
for yearday in self.model_yeardays:
self.model_yeardays_date.append(
date_prop.yearday_to_date(base_yr, yearday))
self.model_yeardays_nrs = len(self.model_yeardays)
self.model_yearhours_nrs = len(self.model_yeardays) * 24
# ============================================================
# Dwelling stock related assumptions
# ============================================================
#
# Assumptions to generate a virtual dwelling stock
#
# assump_diff_floorarea_pp : float
# Change in floor area per person (%, 1=100%)
# assump_diff_floorarea_pp_yr_until_changed : int
# Year until this change in floor area happens
# dwtype_distr_by : dict
# Housing Stock Distribution by Type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_distr_fy : dict
# welling type distribution end year
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_by : dict
# Floor area per dwelling type (Annex Table 3.1)
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_fy : dict
# Floor area per dwelling type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_age_distr : dict
# Floor area per dwelling type
# Source: Housing Energy Fact Sheet)
# yr_until_changed : int
# Year until change is realised
#
# https://www.gov.uk/government/statistics/english-housing-survey-2014-to-2015-housing-stock-report
# ------------------------------------------------------------
self.assump_diff_floorarea_pp = 1.0
self.assump_diff_floorarea_pp_yr_until_changed = yr_until_changed_all_things
self.dwtype_distr_by = {
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_distr_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_floorarea_by = {
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
self.dwtype_floorarea_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
# (Average builing age within age class, fraction)
# The newest category of 2015 is added to implement change in refurbishing rate
# For the base year, this is set to zero (if e.g. with future scenario set to 5%, then
# proportionally to base year distribution number of houses are refurbished)
self.dwtype_age_distr = {
2015: {
'1918': 0.21,
'1941': 0.36,
'1977.5': 0.3,
'1996.5': 0.08,
'2002': 0.05
}
}
# ============================================================
# Scenario drivers
# ============================================================
#
# For every enduse the relevant factors which affect enduse
# consumption can be added in a list.
#
# Note: If e.g. floorarea and population are added, the
# effects will be overestimates (i.e. no multi-
# collinearity are considered).
#
# scenario_drivers : dict
# Scenario drivers per enduse
# ------------------------------------------------------------
self.scenario_drivers = {}
# --Residential SubModel
self.scenario_drivers['rs_submodule'] = {
'rs_space_heating':
['floorarea',
'hlc'], # Do not use HDD or pop because otherweise double count
'rs_water_heating': ['population'],
'rs_lighting': ['population', 'floorarea'],
'rs_cooking': ['population'],
'rs_cold': ['population'],
'rs_wet': ['population'],
'rs_consumer_electronics':
['population'
], #GVA TODO. As soon as GVA is avaiable, drive it with GVA
'rs_home_computing': ['population']
} #GVA
# --Service Submodel (Table 5.5a)
self.scenario_drivers['ss_submodule'] = {
'ss_space_heating': ['floorarea'],
'ss_water_heating': ['population'],
'ss_lighting': ['floorarea'],
'ss_catering': ['population'],
'ss_ICT_equipment': ['population'],
'ss_cooling_humidification': ['floorarea', 'population'],
'ss_fans': ['floorarea', 'population'],
'ss_small_power': ['population'],
'ss_cooled_storage': ['population'],
'ss_other_gas': ['population'],
'ss_other_electricity': ['population']
}
# --Industry Submodel
self.scenario_drivers['is_submodule'] = {
'is_high_temp_process': ['gva'],
'is_low_temp_process': ['gva'],
'is_drying_separation': ['gva'],
'is_motors': ['gva'],
'is_compressed_air': ['gva'],
'is_lighting': ['gva'],
'is_space_heating': ['gva'],
'is_other': ['gva'],
'is_refrigeration': ['gva']
}
# ============================================================
# Cooling related assumptions
# ============================================================
#
# Parameters related to cooling enduses are defined here.
#
# assump_cooling_floorarea : int
# The percentage of cooled floor space in the base year
#
# Literature
# ----------
# Abela, A. et al. (2016). Study on Energy Use by Air
# Conditioning. Bre, (June), 31. Retrieved from
# https://www.bre.co.uk/filelibrary/pdf/projects/aircon-energy-use
# /StudyOnEnergyUseByAirConditioningFinalReport.pdf
# ------------------------------------------------------------
# See Abela et al. (2016)
# Carbon Trust. (2012). Air conditioning. Maximising comfort, minimising energy consumption
self.cooled_ss_floorarea_by = 0.35
# ============================================================
# Smart meter related base year assumptions
# ============================================================
#
# Parameters related to smart metering
#
# smart_meter_p_by : int
# The percentage of households with smart meters in by
# smart_meter_diff_params : dict
# Sigmoid diffusion parameter of smater meters
# ------------------------------------------------------------
self.smart_meter_assump = {}
self.smart_meter_assump['smart_meter_p_by'] = 0.1
self.smart_meter_assump['smart_meter_diff_params'] = {
'sig_midpoint': 0,
'sig_steepness': 1
}
# ============================================================
# Base temperature assumptions
# ============================================================
#
# Parameters related to smart metering
#
# rs_t_heating_by : int
# Residential submodel base temp of heating of base year
# rs_t_cooling_by : int
# Residential submodel base temp of cooling of base year
# base_temp_diff_params : dict
# Sigmoid temperature diffusion parameters
# ...
#
# Note
# ----
# Because demand for cooling cannot directly be linked to
# calculated cdd, the paramters 'ss_t_cooling_by' is used
# as a calibration factor. By artifiallcy lowering this
# parameter, the energy demand assignement over the days
# in a year is improved.
# ------------------------------------------------------------
t_bases = {}
t_bases['rs_t_heating_by'] = 15.5 #
#t_bases['rs_t_cooling_by'] = Not implemented
t_bases['ss_t_heating_by'] = 15.5 #
t_bases['ss_t_cooling_by'] = 5 # Orig: 5
t_bases['is_t_heating_by'] = 15.5 #
#self.t_bases['is_t_cooling_by'] = Not implemented
self.t_bases = DummyClass(t_bases)
self.base_temp_diff_params = {
'sig_midpoint': 0,
'sig_steepness': 1,
'yr_until_changed': yr_until_changed_all_things
}
# ============================================================
# Enduses lists affed by hdd/cdd
# ============================================================
#
# These lists show for which enduses temperature related
# calculations are performed.
#
# enduse_space_heating : list
# All enduses for which hdd are used for yd calculations
# enduse_rs_space_cooling : list
# All residential enduses for which cdd are used for
# yd calculations
# ss_enduse_space_cooling : list
# All service submodel enduses for which cdd are used for
# yd calculations
# ------------------------------------------------------------
self.enduse_space_heating = [
'rs_space_heating', 'ss_space_heating', 'is_space_heating'
]
self.enduse_rs_space_cooling = []
self.ss_enduse_space_cooling = ['ss_cooling_humidification']
# ============================================================
# Industry submodel related parameters
# ============================================================
#
# Assumptions related to industrial enduses
#
# Overal changes in industry related enduse can be changed
# in 'enduse_overall_change_enduses'
#
# ------------------------------------------------------------
# --------------------------------------------
# heating
# --------------------------------------------
# --> No scenario drivers but driven by switches
# --------------------------------------------
# lighting
#
# No individual technologies are defined. Only
# overall efficiency increase can be implemented
#--------------------------------------------
# --------------------------------------------
# high_temp_ process
#
# High temperature processing dominates energy consumption in the iron and steel,
# non-ferrous metal, bricks, cement, glass and potteries industries. This includes
# - coke ovens
# - blast furnaces and other furnaces
# - kilns and
# - glass tanks.
# High consumption in Chemicals, Non_metallic mineral products, paper, food_production
# Fuel use ratio - electric arc furnace over blast furnace steel making in cement sector
# BAT - iron & steel - continous/Ingot casting Sectoral share - continuous %
# --------------------------------------------
# Share of cold rolling in steel manufacturing
# *****************
self.p_cold_rolling_steel_by = 0.2 # Estimated based on https://aceroplatea.es/docs/EuropeanSteelFigures_2015.pdf
self.eff_cold_rolling_process = 1.8 # 80% more efficient than hot rolling Fruehan et al. (2002)
self.eff_hot_rolling_process = 1.0 # 100% assumed efficiency
# Steel production - Enduse: is_high_temp_process, Sector: basic_metals
# *****************
# With industry service switch, the future shares
# in 'basic_oxygen_furnace', 'electric_arc_furnace', and 'SNG_furnace'
# can be specified
#scrap-based production: electric arc furnace
#direct reduction process: natrual gas based, electric arc furnace
#BF-BOF (blast furnace - basix oxgen furnace)
# Example service switch:
# is_high_temp_process,SNG_furnace,1.0,2050,basic_metals
# Cement production - Enduse: is_high_temp_process, Sector: non_metallic_mineral_products
# *****************
# technologies: Dry kilns, semidry kilns
# CHEMICALs - Enduse: is_high_temp_process, Sector: CHEMICALS
# *****************
# technologies: Dry & wet kilns
# ----------------
# Efficiency of motors
# ----------------
#is_motors_eff_change = 0
# ----------------
# Efficiency of others
# ----------------
#is_others_eff_change = 0
# ----------------
# Efficiency of others
# ----------------
#is_refrigeration_eff_change = 0
# ----------------
# Efficiency of is_compressed_air
# ----------------
#is_compressed_air_eff_change =
# ----------------
# Efficiency of is_drying_separation
# ----------------
#is_drying_separation_eff_change =
# ----------------
# Efficiency of is_low_temp_process
# ----------------
#is_low_temp_process_eff_change =
# ============================================================
# Assumption related to heat pump technologies
# ============================================================
#
# Assumptions related to technologies
#
# gshp_fraction : list
# Fraction of installed gshp_fraction heat pumps in base year
# ASHP = 1 - gshp_fraction
# ------------------------------------------------------------
self.gshp_fraction = 0.1
self.technologies, self.tech_list = read_data.read_technologies(
paths['path_technologies'], fueltypes)
self.installed_heat_pump_by = tech_related.generate_ashp_gshp_split(
self.gshp_fraction)
# Add heat pumps to technologies
self.technologies, self.tech_list[
'heating_non_const'], self.heat_pumps = tech_related.generate_heat_pump_from_split(
self.technologies, self.installed_heat_pump_by, fueltypes)
# Collect all heating technologies
# TODO: MAYBE ADD IN TECH DOC ANOTHER LIST SPECIFYING ALL HEATING TECHs
self.heating_technologies = get_all_heating_techs(self.tech_list)
# ============================================================
# Enduse diffusion paramters
# ============================================================
#
# Assumptions related to general diffusion
#
# This parameters are used to specify e.g. diffusion of
# an enduse which is not specified by technologies
# or the diffusion of a policy of changing a parameter
# over time.
# ------------------------------------------------------------
self.enduse_overall_change = {}
self.enduse_overall_change['other_enduse_mode_info'] = {
'diff_method': 'linear',
'sigmoid': {
'sig_midpoint': 0,
'sig_steepness': 1
}
}
# ============================================================
# Fuel Stock Definition
# Provide for every fueltype of an enduse
# the share of fuel which is used by technologies for thebase year
# ============================================================
self.rs_fuel_tech_p_by, self.ss_fuel_tech_p_by, self.is_fuel_tech_p_by = assumptions_fuel_shares.assign_by_fuel_tech_p(
enduses, sectors, fueltypes, fueltypes_nr)
# ========================================
# Get technologies of an enduse
# ========================================
self.rs_specified_tech_enduse_by = helpers.get_def_techs(
self.rs_fuel_tech_p_by, sector_crit=False)
self.ss_specified_tech_enduse_by = helpers.get_def_techs(
self.ss_fuel_tech_p_by, sector_crit=True)
self.is_specified_tech_enduse_by = helpers.get_def_techs(
self.is_fuel_tech_p_by, sector_crit=True)
rs_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.rs_specified_tech_enduse_by,
'rs_space_heating')
self.rs_specified_tech_enduse_by = rs_specified_tech_enduse_by_new
ss_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.ss_specified_tech_enduse_by,
'ss_space_heating')
self.ss_specified_tech_enduse_by = ss_specified_tech_enduse_by_new
is_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.is_specified_tech_enduse_by,
'is_space_heating')
self.is_specified_tech_enduse_by = is_specified_tech_enduse_by_new
# ============================================================
# Read in switches
# ============================================================
# Read in scenaric fuel switches
self.rs_fuel_switches = read_data.read_fuel_switches(
paths['rs_path_fuel_switches'], enduses, fueltypes,
self.technologies)
self.ss_fuel_switches = read_data.read_fuel_switches(
paths['ss_path_fuel_switches'], enduses, fueltypes,
self.technologies)
self.is_fuel_switches = read_data.read_fuel_switches(
paths['is_path_fuel_switches'], enduses, fueltypes,
self.technologies)
# Read in scenaric service switches
self.rs_service_switches = read_data.service_switch(
paths['rs_path_service_switch'], self.technologies)
self.ss_service_switches = read_data.service_switch(
paths['ss_path_service_switch'], self.technologies)
self.is_service_switches = read_data.service_switch(
paths['is_path_industry_switch'], self.technologies)
# Read in scenaric capacity switches
self.rs_capacity_switches = read_data.read_capacity_switch(
paths['rs_path_capacity_installation'])
self.ss_capacity_switches = read_data.read_capacity_switch(
paths['ss_path_capacity_installation'])
self.is_capacity_switches = read_data.read_capacity_switch(
paths['is_path_capacity_installation'])
# Testing
self.crit_switch_happening = testing_functions.switch_testing(
fuel_switches=[
self.rs_fuel_switches, self.ss_fuel_switches,
self.is_fuel_switches
],
service_switches=[
self.rs_service_switches, self.ss_service_switches,
self.is_service_switches
],
capacity_switches=[
self.rs_capacity_switches, self.ss_capacity_switches,
self.is_capacity_switches
])
# ========================================
# General other assumptions
# ========================================
self.seasons = date_prop.get_season(year_to_model=base_yr)
self.model_yeardays_daytype, self.yeardays_month, self.yeardays_month_days = date_prop.get_model_yeardays_daytype(
year_to_model=base_yr)
# ========================================
# Helper functions
# ========================================
self.rs_fuel_tech_p_by, self.rs_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.rs_fuel_tech_p_by,
self.rs_specified_tech_enduse_by,
sector_crit=False)
self.ss_fuel_tech_p_by, self.ss_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.ss_fuel_tech_p_by,
self.ss_specified_tech_enduse_by,
sector_crit=True)
self.is_fuel_tech_p_by, self.is_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.is_fuel_tech_p_by,
self.is_specified_tech_enduse_by,
sector_crit=True)
# ========================================
# Calculations with assumptions
# ========================================
self.cdd_weekend_cfactors = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_cooling_weekend)
self.ss_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_weekend)
self.is_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_is_weekend)
# ========================================
# Testing
# ========================================
testing_functions.testing_fuel_tech_shares(self.rs_fuel_tech_p_by)
for enduse in self.ss_fuel_tech_p_by:
testing_functions.testing_fuel_tech_shares(
self.ss_fuel_tech_p_by[enduse])
for enduse in self.is_fuel_tech_p_by:
testing_functions.testing_fuel_tech_shares(
self.is_fuel_tech_p_by[enduse])
testing_functions.testing_tech_defined(
self.technologies, self.rs_specified_tech_enduse_by)
testing_functions.testing_tech_defined(
self.technologies, self.ss_specified_tech_enduse_by)
testing_functions.testing_tech_defined(
self.technologies, self.is_specified_tech_enduse_by)
def process_scenarios(path_to_scenarios, year_to_model=2015):
"""Iterate folder with scenario results and plot charts
Arguments
----------
path_to_scenarios : str
Path to folders with stored results
year_to_model : int, default=2015
Year of base year
"""
# -----------
# Charts to plot
# -----------
heat_pump_range_plot = False # Plot of changing scenario values stored in scenario name
plot_multiple_cross_charts = True # Compare cross charts of different scenario
comparison_year = 2050
year_to_plot = 2050
# Delete folder results if existing
path_result_folder = os.path.join(
path_to_scenarios, "__results_multiple_scenarios")
basic_functions.delete_folder(path_result_folder)
seasons = date_prop.get_season(
year_to_model=year_to_model)
model_yeardays_daytype, _, _ = date_prop.get_yeardays_daytype(
year_to_model=year_to_model)
lookups = lookup_tables.basic_lookups()
# Get all folders with scenario run results (name of folder is scenario)
scenarios = os.listdir(path_to_scenarios)
# Simulation information is read in from .ini file for results
path_fist_scenario = os.path.join(path_to_scenarios, scenarios[0])
enduses, assumptions, regions = data_loader.load_ini_param(
path_fist_scenario)
# -------------------------------
# Iterate folders and get results
# -------------------------------
scenario_data = {}
for scenario in scenarios:
scenario_data[scenario] = {}
all_stations = os.listdir(
os.path.join(path_to_scenarios, scenario))
_to_igore = [
'infoparam.txt',
'model_run_pop',
'PDF_validation', 'model_run_sim_param.ini']
for station in all_stations:
if station not in _to_igore:
path_to_result_files = os.path.join(
path_to_scenarios,
scenario,
station,
'model_run_results_txt')
scenario_data[scenario] = read_data.read_in_results(
path_result=path_to_result_files,
seasons=seasons,
model_yeardays_daytype=model_yeardays_daytype)
else:
pass
# Create result folder
basic_functions.create_folder(path_result_folder)
# -------------------------------
# Generate plot with heat pump ranges
# -------------------------------
if heat_pump_range_plot:
plotting_multiple_scenarios.plot_heat_pump_chart(
lookups,
regions,
scenario_data,
fig_name=os.path.join(path_result_folder, "comparison_hp_service_switch_and_lf.pdf"),
fueltype_str_input='electricity',
plotshow=True)
# -------------------------------
# Compare cross charts for different scenario
# Ideally only compare two scenario
# -------------------------------
if plot_multiple_cross_charts:
fig_cross_graphs.plot_cross_graphs_scenarios(
base_yr=2015,
comparison_year=comparison_year,
regions=regions,
scenario_data=scenario_data,
fueltype_int=lookups['fueltypes']['electricity'],
fueltype_str='electricity',
fig_name=os.path.join(path_result_folder, "cross_chart_electricity.pdf"),
label_points=False,
plotshow=False)
fig_cross_graphs.plot_cross_graphs_scenarios(
base_yr=2015,
comparison_year=comparison_year,
regions=regions,
scenario_data=scenario_data,
fueltype_int=lookups['fueltypes']['gas'],
fueltype_str='gas',
fig_name=os.path.join(path_result_folder, "cross_chart_gas.pdf"),
label_points=False,
plotshow=False)
# -------------------------------
# Plot total demand for every year in line plot
# -------------------------------
plotting_multiple_scenarios.plot_tot_fueltype_y_over_time(
scenario_data,
lookups['fueltypes'],
fueltypes_to_plot=['electricity', 'gas'],
fig_name=os.path.join(path_result_folder, "tot_y_multiple_fueltypes.pdf"),
txt_name=os.path.join(path_result_folder, "tot_y_multiple_fueltypes.txt"),
plotshow=False)
plotting_multiple_scenarios.plot_tot_y_over_time(
scenario_data,
fig_name=os.path.join(path_result_folder, "tot_y_multiple.pdf"),
plotshow=False)
# -------------------------------
# Plot for all regions demand for every year in line plot
# -------------------------------
plotting_multiple_scenarios.plot_reg_y_over_time(
scenario_data,
fig_name=os.path.join(path_result_folder, "reg_y_multiple.pdf"),
plotshow=False)
# -------------------------------
# Plot comparison of total demand for a year for all LADs (scatter plot)
# -------------------------------
plotting_multiple_scenarios.plot_LAD_comparison_scenarios(
scenario_data,
year_to_plot=year_to_plot,
fig_name=os.path.join(path_result_folder, "LAD_multiple.pdf"),
plotshow=False)
# -------------------------------
# Plot different profiles in radar plot (spider plot)
# -------------------------------
plotting_multiple_scenarios.plot_radar_plots_average_peak_day(
scenario_data,
fueltype_to_model='electricity',
fueltypes=lookups['fueltypes'],
year_to_plot=year_to_plot,
fig_name=os.path.join(path_result_folder))
plotting_multiple_scenarios.plot_radar_plots_average_peak_day(
scenario_data,
fueltype_to_model='gas',
fueltypes=lookups['fueltypes'],
year_to_plot=year_to_plot,
fig_name=os.path.join(path_result_folder))
# ----------------------
# Plot peak hour of all fueltypes for different scenario
# ----------------------
plotting_multiple_scenarios.plot_tot_y_peak_hour(
scenario_data,
fig_name=os.path.join(path_result_folder, "tot_y_peak_h_electricity.pdf"),
fueltype_str_input='electricity',
plotshow=False)
plotting_multiple_scenarios.plot_tot_y_peak_hour(
scenario_data,
fig_name=os.path.join(path_result_folder, "tot_y_peak_h_gas.pdf"),
fueltype_str_input='gas',
plotshow=False)
print("Finished processing multiple scenario")
return
def run_fig_spatial_distribution_of_peak(
scenarios,
path_to_folder_with_scenarios,
path_shapefile,
sim_yrs,
field_to_plot,
unit,
fig_path,
fueltype_str='electricity'
):
"""
"""
weather_yrs = []
calculated_yrs_paths = []
fueltype_int = tech_related.get_fueltype_int(fueltype_str)
for scenario in scenarios:
path_scenario = os.path.join(path_to_folder_with_scenarios, scenario)
all_result_folders = os.listdir(path_scenario)
for result_folder in all_result_folders:
try:
split_path_name = result_folder.split("__")
weather_yr = int(split_path_name[0])
weather_yrs.append(weather_yr)
tupyle_yr_path = (weather_yr, os.path.join(path_scenario))
calculated_yrs_paths.append(tupyle_yr_path)
except ValueError:
pass
for simulation_yr in sim_yrs:
container = {}
container['abs_demand_in_peak_h_pp'] = {}
container['abs_demand_in_peak_h'] = {}
container['p_demand_in_peak_h'] = {}
for weather_yr, path_data_ed in calculated_yrs_paths:
print("... prepare data {} {}".format(weather_yr, path_data_ed))
path_to_weather_yr = os.path.join(path_data_ed, "{}__{}".format(weather_yr, 'all_stations'))
data = {}
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['assumptions'], regions = data_loader.load_ini_param(os.path.join(path_data_ed))
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions']['yeardays_month'], data['assumptions']['yeardays_month_days'] = date_prop.get_yeardays_daytype(year_to_model=2015)
# Population
population_data = read_data.read_scenaric_population_data(os.path.join(path_data_ed, 'model_run_pop'))
results_container = read_data.read_in_results(
os.path.join(path_to_weather_yr, 'model_run_results_txt'),
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
# ---------------------------------------------------
# Calculate hour with national peak demand
# This may be different depending on the weather yr
# ---------------------------------------------------
ele_regions_8760 = results_container['ed_fueltype_regs_yh'][simulation_yr][fueltype_int]
sum_all_regs_fueltype_8760 = np.sum(ele_regions_8760, axis=0) # Sum for every hour
max_day = int(basic_functions.round_down((np.argmax(sum_all_regs_fueltype_8760) / 24), 1))
max_h = np.argmax(sum_all_regs_fueltype_8760)
max_demand = np.max(sum_all_regs_fueltype_8760)
# Calculate the national peak demand in GW
national_peak_GW = np.max(sum_all_regs_fueltype_8760)
# ------------------------------------------------------
# Calculate the contribution of the regional peak demand
# ------------------------------------------------------
demand_in_peak_h = ele_regions_8760[:, max_h]
if unit == 'GW':
container['abs_demand_in_peak_h'][weather_yr] = demand_in_peak_h
elif unit == 'kW':
container['abs_demand_in_peak_h'][weather_yr] = demand_in_peak_h * 1000000 # Convert to KWh
else:
# Use GW as default
container['abs_demand_in_peak_h'][weather_yr] = demand_in_peak_h
container['abs_demand_in_peak_h_pp'][weather_yr] = demand_in_peak_h / population_data[simulation_yr]
# Relative fraction of regional demand in relation to peak
container['p_demand_in_peak_h'][weather_yr] = (demand_in_peak_h / national_peak_GW ) * 100 # given as percent
print("=================================")
print("{} {} {} {}".format(
simulation_yr,
weather_yr,
np.sum(ele_regions_8760),
national_peak_GW))
# --------------
# Create dataframe with all weather yrs calculatiosn for every region
# region1, region2, region3
# weather yr1
# weather yr2
# --------------
# Convert regional data to dataframe
abs_demand_in_peak_h_pp = np.array(list(container['abs_demand_in_peak_h_pp'].values()))
abs_demand_peak_h = np.array(list(container['abs_demand_in_peak_h'].values()))
p_demand_peak_h = np.array(list(container['p_demand_in_peak_h'].values()))
# Absolute demand
df_abs_peak_demand = pd.DataFrame(
abs_demand_peak_h,
columns=regions,
index=list(container['abs_demand_in_peak_h'].keys()))
# Relative demand
df_p_peak_demand = pd.DataFrame(
p_demand_peak_h,
columns=regions,
index=list(container['p_demand_in_peak_h'].keys()))
df_abs_demand_in_peak_h_pp = pd.DataFrame(
abs_demand_in_peak_h_pp,
columns=regions,
index=list(container['abs_demand_in_peak_h_pp'].keys()))
# Absolute peak value - mean
max_peak_h_across_weather_yrs = df_abs_peak_demand.max()
average_across_weather_yrs = df_abs_peak_demand.mean()
diff_peak_h_minus_mean = max_peak_h_across_weather_yrs - average_across_weather_yrs
for index, row in df_p_peak_demand.iterrows():
print("Weather yr: {} Total p: {}".format(index, np.sum(row)))
assert round(np.sum(row), 4) == 100.0
# ----------------------------
# Calculate standard deviation
# ----------------------------
std_deviation_df_abs_demand_in_peak_h_pp = df_abs_demand_in_peak_h_pp.std()
std_deviation_abs_demand_peak_h = df_abs_peak_demand.std()
std_deviation_p_demand_peak_h = df_p_peak_demand.std()
print("=========")
print("National stats")
print("=========")
print("Sum of std of absolut peak demand: " + str(np.sum(std_deviation_abs_demand_peak_h)))
# --------------------
# Create map
# --------------------
regional_statistics_columns = [
'name',
'std_deviation_p_demand_peak_h',
'std_deviation_abs_demand_peak_h',
'std_deviation_df_abs_demand_in_peak_h_pp',
'diff_peak_h_minus_mean']
df_stats = pd.DataFrame(columns=regional_statistics_columns)
for region_name in regions:
# 'name', 'absolute_GW', 'p_GW_peak'
line_entry = [[
region_name,
std_deviation_p_demand_peak_h[region_name],
std_deviation_abs_demand_peak_h[region_name],
std_deviation_df_abs_demand_in_peak_h_pp[region_name],
diff_peak_h_minus_mean[region_name],
]]
line_df = pd.DataFrame(line_entry, columns=regional_statistics_columns)
df_stats = df_stats.append(line_df)
# Load uk shapefile
uk_shapefile = gpd.read_file(path_shapefile)
# Merge stats to geopanda
shp_gdp_merged = uk_shapefile.merge(
df_stats,
on='name')
# Assign projection
crs = {'init': 'epsg:27700'} #27700: OSGB_1936_British_National_Grid
uk_gdf = gpd.GeoDataFrame(shp_gdp_merged, crs=crs)
ax = uk_gdf.plot(
figsize=basic_plot_functions.cm2inch(25, 20))
nr_of_intervals = 6
bin_values = result_mapping.get_reasonable_bin_values_II(
data_to_plot=list(uk_gdf[field_to_plot]),
nr_of_intervals=nr_of_intervals)
# Maual bins
#bin_values = [0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03]
print(float(uk_gdf[field_to_plot].max()))
print("BINS " + str(bin_values))
uk_gdf, cmap_rgb_colors, color_zero, min_value, max_value = fig_p2_weather_val.user_defined_bin_classification(
uk_gdf,
field_to_plot,
bin_values=bin_values)
# plot with face color attribute
uk_gdf.plot(
ax=ax,
facecolor=uk_gdf['bin_color'],
edgecolor='black',
linewidth=0.5)
legend_handles = result_mapping.add_simple_legend(
bin_values,
cmap_rgb_colors,
color_zero)
plt.legend(
handles=legend_handles,
title="{} [{}]".format(field_to_plot, unit),
prop={'size': 8},
#loc='upper center', bbox_to_anchor=(0.5, -0.05),
loc='center left', bbox_to_anchor=(1, 0.5),
frameon=False)
# PLot bins on plot
'''plt.text(
-20,
-20,
bin_values[:-1], #leave away maximum value
fontsize=8)'''
plt.tight_layout()
fig_out_path = os.path.join(fig_path, str(field_to_plot) + "__" + str(simulation_yr) + ".pdf")
plt.savefig(fig_out_path)
def main(scenarios_path, path_shapefile_input, base_yr,
simulation_yrs_to_plot):
"""Read in all results and plot PDFs
Arguments
----------
scenarios_path : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("Start creating plots")
# -------------------
# Create result folder
# -------------------
result_path = os.path.join(scenarios_path, '_results_weather_plots')
basic_functions.del_previous_setup(result_path)
basic_functions.create_folder(result_path)
for simulation_yr_to_plot in simulation_yrs_to_plot:
print("-----------")
print("...simulation_yr_to_plot: " + str(simulation_yr_to_plot))
print("-----------")
data = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = [
'model_run_pop', 'PDF_validation', '_results_weather_plots'
]
endings_to_ignore = ['.pdf', '.txt', '.ini']
all_scenarios_incl_ignored = os.listdir(scenarios_path)
all_scenarios = []
for scenario in all_scenarios_incl_ignored:
if scenario not in to_ignores:
all_scenarios.append(scenario)
scenario_result_container = []
for scenario_nr, scenario_name in enumerate(all_scenarios):
print(" ")
print("Scenario: {}".format(scenario_name))
print(" ")
scenario_path = os.path.join(scenarios_path, scenario_name)
all_result_folders = os.listdir(scenario_path)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[
-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(scenario_path, result_folder))
fueltype_str_to_create_maps = ['electricity']
fueltype_str = 'electricity'
fueltype_int = tech_related.get_fueltype_int(fueltype_str)
####################################################################
# Collect regional simulation data for every realisation
####################################################################
total_regional_demand_electricity = pd.DataFrame()
peak_hour_demand = pd.DataFrame()
national_peak = pd.DataFrame()
regional_share_national_peak = pd.DataFrame()
national_electricity = pd.DataFrame()
national_gas = pd.DataFrame()
national_hydrogen = pd.DataFrame()
for path_result_folder in paths_folders_result:
data = {}
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(
os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(
os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder = os.path.join(path_result_folder,
'simulation_results')
path_result_folder_model_runs = os.path.join(
path_result_folder, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(
year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data[
'assumptions']['yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_weather_results.read_in_weather_results(
path_result_folder_model_runs,
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'],
fueltype_str='electricity')
# --Total demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame([
results_container['ed_reg_tot_y'][simulation_yr_to_plot]
[fueltype_int]
],
columns=data['regions'])
total_regional_demand_electricity = total_regional_demand_electricity.append(
realisation_data)
# National per fueltype electricity
fueltype_elec_int = tech_related.get_fueltype_int(
'electricity')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_electricity = national_electricity.append(
realisation_data)
# National per fueltype gas
fueltype_elec_int = tech_related.get_fueltype_int('gas')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_gas = national_gas.append(realisation_data)
# National per fueltype hydrogen
fueltype_elec_int = tech_related.get_fueltype_int('hydrogen')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_hydrogen = national_hydrogen.append(realisation_data)
# --Peak day demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame([
results_container['ed_reg_peakday_peak_hour']
[simulation_yr_to_plot][fueltype_int]
],
columns=data['regions'])
peak_hour_demand = peak_hour_demand.append(realisation_data)
# --National peak
simulation_yrs_result = [
results_container['national_peak'][year][fueltype_int]
for year in results_container['national_peak'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_peak = national_peak.append(realisation_data)
# --Regional percentage of national peak demand
realisation_data = pd.DataFrame([
results_container['regional_share_national_peak']
[simulation_yr_to_plot]
],
columns=data['regions'])
regional_share_national_peak = regional_share_national_peak.append(
realisation_data)
# Add to scenario container
scenario_result_container.append({
'scenario_name':
scenario_name,
'peak_hour_demand':
peak_hour_demand,
'national_peak':
national_peak,
'regional_share_national_peak':
regional_share_national_peak,
'total_regional_demand_electricity':
total_regional_demand_electricity,
'national_electricity':
national_electricity,
'national_gas':
national_gas,
'national_hydrogen':
national_hydrogen,
})
# ------------------------------
# Plot national sum over time per fueltype and scenario
# ------------------------------
print("... plotting national sum of fueltype over time ")
fig_3_plot_over_time.fueltypes_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="fueltypes_over_time__{}__{}.pdf".format(
simulation_yr_to_plot, fueltype_str),
fueltypes=['electricity', 'gas', 'hydrogen'],
result_path=result_path,
unit='TWh',
plot_points=True,
crit_smooth_line=True,
seperate_legend=False)
# ------------------------------
# Plot national peak change over time for each scenario including weather variability
# ------------------------------
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_peak_over_time__{}__{}.pdf".format(
simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=True,
seperate_legend=False)
# ------------------------------
# Plotting spatial results for electricity
# ------------------------------
for i in scenario_result_container:
scenario_name = i['scenario_name']
total_regional_demand_electricity = i[
'total_regional_demand_electricity']
peak_hour_demand = i['peak_hour_demand']
regional_share_national_peak = i['regional_share_national_peak']
print("... plot spatial map of total annual demand")
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
total_regional_demand_electricity,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__tot_demand__{}_{}_{}.pdf".format(
scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='GW',
seperate_legend=False)
print("... plot spatial map of peak hour demand")
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
peak_hour_demand,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__peak_h_demand_{}_{}_{}.pdf".format(
scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='GW',
seperate_legend=False)
print(
"... plot spatial map of percentage of regional peak hour demand"
)
field_to_plot = 'mean'
fig_3_weather_map.total_annual_demand(
regional_share_national_peak,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__regional_share_national_peak_{}_{}_{}.pdf".
format(scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='percentage',
seperate_legend=False,
bins=[0.000001, 0.25, 0.5, 0.75, 1, 1.25, 1.5])
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
regional_share_national_peak,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__regional_share_national_peak_{}_{}_{}.pdf".
format(scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='percentage',
seperate_legend=False)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
def process_scenarios(path_to_scenarios, year_to_model=2015):
"""Iterate folder with scenario results and plot charts
Arguments
----------
path_to_scenarios : str
Path to folders with stored results
year_to_model : int, default=2015
Year of base year
"""
# Delete folder results if existing
path_result_folder = os.path.join(
path_to_scenarios, "_results_multiple_scenarios")
basic_functions.delete_folder(path_result_folder)
seasons = date_prop.get_season(
year_to_model=year_to_model)
model_yeardays_daytype, _, _ = date_prop.get_model_yeardays_daytype(
year_to_model=year_to_model)
# Get all folders with scenario run results (name of folder is scenario)
scenarios = os.listdir(path_to_scenarios)
# -------------------------------
# Iterate folders and get results
# -------------------------------
scenario_data = {}
for scenario in scenarios:
# Add scenario name to folder
scenario_data[scenario] = {}
path_to_result_files = os.path.join(
path_to_scenarios,
scenario,
'_result_data',
'model_run_results_txt')
scenario_data[scenario] = read_data.read_in_results(
path_runs=path_to_result_files,
seasons=seasons,
model_yeardays_daytype=model_yeardays_daytype)
# -----------------------
# Generate result folder
# -----------------------
basic_functions.create_folder(path_result_folder)
# ------------
# Create plots
# ------------
# Plot total demand for every year in line plot
plotting_multiple_scenarios.plot_tot_y_over_time(
scenario_data,
fig_name=os.path.join(path_result_folder, "tot_y_multiple.pdf"),
plotshow=False)
# Plot for all regions demand for every year in line plot
plotting_multiple_scenarios.plot_reg_y_over_time(
scenario_data,
fig_name=os.path.join(path_result_folder, "reg_y_multiple.pdf"),
plotshow=False)
# Plot comparison of total demand for a year for all LADs (scatter plot)
plotting_multiple_scenarios.plot_LAD_comparison_scenarios(
scenario_data,
year_to_plot=2050,
fig_name=os.path.join(path_result_folder, "LAD_multiple.pdf"),
plotshow=False)
# Plot different profiels in radar plot
plotting_multiple_scenarios.plot_radar_plots_average_peak_day(
scenario_data,
year_to_plot=2050,
fig_name=os.path.join(path_result_folder),
plotshow=False)
logging.info("Finished processing multiple scenario")
return
def main(path_data_ed, path_shapefile_input, plot_crit_dict, base_yr,
comparison_year):
"""Read in all results and plot PDFs
Arguments
----------
path_data_ed : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("...Start creating plots")
data = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = ['model_run_pop', 'PDF_validation']
endings_to_ignore = ['.pdf', '.txt', '.ini']
all_result_folders = os.listdir(path_data_ed)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[
-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(path_data_ed, result_folder))
####################################################################
# Calculate results for every weather year
####################################################################
for path_result_folder in paths_folders_result:
print("-----------------------")
print("path_result_folder: " + str(path_result_folder))
print("-----------------------")
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(os.path.join(path_data_ed))
# ------------------
# Load necessary inputs for read in
# ------------------
data = {}
data['local_paths'] = data_loader.get_local_paths(path_result_folder)
data['result_paths'] = basic_functions.get_result_paths(
os.path.join(path_result_folder))
data['lookups'] = lookup_tables.basic_lookups()
# ---------------
# Folder cleaning
# ---------------
basic_functions.del_previous_setup(
data['result_paths']['data_results_PDF'])
basic_functions.del_previous_setup(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(data['result_paths']['data_results_PDF'])
basic_functions.create_folder(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(
data['result_paths']['individual_enduse_lp'])
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(os.path.join(path_data_ed))
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(
year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions'][
'yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
data['scenario_data'] = {}
data['scenario_data'][
'population'] = read_data.read_scenaric_population_data(
os.path.join(path_data_ed, 'model_run_pop'))
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_data.read_in_results(
data['result_paths']['data_results_model_run_results_txt'],
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
# ------------------------------
# Plotting other results
# ------------------------------
plotting_results.run_all_plot_functions(
results_container,
data['assumptions']['reg_nrs'],
data['regions'],
data['lookups'],
data['result_paths'],
data['assumptions'],
data['enduses'],
plot_crit=plot_crit_dict,
base_yr=base_yr,
comparison_year=comparison_year)
# ------------------------------
# Plotting spatial results
# ------------------------------
if plot_crit_dict['spatial_results']:
result_mapping.spatial_maps(
data,
results_container,
data['result_paths']['data_results_shapefiles'],
data['regions'],
data['lookups']['fueltypes_nr'],
data['lookups']['fueltypes'],
path_shapefile_input,
plot_crit_dict,
base_yr=base_yr)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
def main(
scenarios_path,
path_shapefile_input,
base_yr,
simulation_yrs_to_plot
):
"""Read in all results and plot PDFs
Arguments
----------
scenarios_path : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("Start creating plots")
# -------------------
# Create temperatuere figur plot
# -------------------
plot_weather_chart = False
if plot_weather_chart:
from energy_demand.plotting import fig3_weather_at_home_plot
path_weather_data = "//linux-filestore.ouce.ox.ac.uk/mistral/nismod/data/energy_demand/J-MARIUS_data/_weather_realisation"
fig3_weather_at_home_plot.plotting_weather_data(path_weather_data)
# -------------------
# Create result folder
# -------------------
result_path = os.path.join(scenarios_path, '_results_weather_plots')
basic_functions.del_previous_setup(result_path)
basic_functions.create_folder(result_path)
x_chart_yrs_storage = {}
for simulation_yr_to_plot in simulation_yrs_to_plot:
print("=================")
print("...simulation_yr_to_plot: " + str(simulation_yr_to_plot))
print("=================")
data = {}
x_chart_yrs_storage[simulation_yr_to_plot] = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = [
'model_run_pop',
'PDF_validation',
'_results_weather_plots']
endings_to_ignore = [
'.pdf',
'.txt',
'.ini']
all_scenarios_incl_ignored = os.listdir(scenarios_path)
all_scenarios = []
for scenario in all_scenarios_incl_ignored:
if scenario not in to_ignores:
all_scenarios.append(scenario)
scenario_result_container = []
for scenario_nr, scenario_name in enumerate(all_scenarios):
print(" ")
print("Scenario: {}".format(scenario_name))
print(" ")
scenario_path = os.path.join(scenarios_path, scenario_name)
all_result_folders = os.listdir(scenario_path)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(scenario_path, result_folder))
fueltype_str_to_create_maps = ['electricity']
fueltype_str ='electricity'
fueltype_elec_int = tech_related.get_fueltype_int('electricity')
####################################################################
# Collect regional simulation data for every realisation
####################################################################
total_regional_demand_electricity = pd.DataFrame()
peak_hour_demand = pd.DataFrame()
peak_hour_demand_per_person = pd.DataFrame()
national_peak = pd.DataFrame()
regional_share_national_peak = pd.DataFrame()
regional_share_national_peak_pp = pd.DataFrame()
national_electricity = pd.DataFrame()
national_gas = pd.DataFrame()
national_hydrogen = pd.DataFrame()
national_heating_peak = pd.DataFrame()
daily_mean_peak_day = pd.DataFrame()
for path_result_folder in paths_folders_result:
print("... path_result_folder: {}".format(path_result_folder))
data = {}
ed_national_heating_peak = {}
try:
# ================================
# Loading in only heating peak demands (seperate calculations)
# ================================
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['regions'] = data_loader.load_ini_param(os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder_heating = os.path.join(path_result_folder, 'simulation_results')
path_result_folder_model_runs = os.path.join(path_result_folder_heating, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Read in heating deamnds
path_heating_demands = os.path.join(path_result_folder_model_runs, 'enduse_specific_results')
all_files = os.listdir(path_heating_demands)
for file_name in all_files:
ending = file_name[-4:]
if ending == ".npy":
year = int(file_name.split("__")[2][:-4])
file_path = os.path.join(path_heating_demands, file_name)
heating_demand = np.load(file_path)
maximum_hour_of_peak_day = heating_demand[fueltype_elec_int].argmax() #get maxim hour of peak day
ed_national_heating_peak[year] = heating_demand[fueltype_elec_int][maximum_hour_of_peak_day]
simulation_yrs_result = [ed_national_heating_peak[year] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_heating_peak = national_heating_peak.append(realisation_data)
except:
raise Exception("... no heating peak data available " + str(path_result_folder))
try:
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['regions'] = data_loader.load_ini_param(os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder = os.path.join(path_result_folder, 'simulation_results')
path_result_folder_model_runs = os.path.join(path_result_folder, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions']['yeardays_month'], data['assumptions']['yeardays_month_days'] = date_prop.get_yeardays_daytype(year_to_model=2015)
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_weather_results.read_in_weather_results(
path_result_folder_model_runs,
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'],
pop_data,
fueltype_str='electricity')
# --Total demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_tot_y'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
total_regional_demand_electricity = total_regional_demand_electricity.append(realisation_data)
# National per fueltype electricity
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_electricity = national_electricity.append(realisation_data)
# National per fueltype gas
fueltype_gas_int = tech_related.get_fueltype_int('gas')
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_gas_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_gas = national_gas.append(realisation_data)
# National per fueltype hydrogen
fueltype_hydrogen_int = tech_related.get_fueltype_int('hydrogen')
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_hydrogen_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_hydrogen = national_hydrogen.append(realisation_data)
# --Peak hour demand per region (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_peakday_peak_hour'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
peak_hour_demand = peak_hour_demand.append(realisation_data)
# --Peak hour demand / pop per region (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_peakday_peak_hour_per_pop'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
peak_hour_demand_per_person = peak_hour_demand_per_person.append(realisation_data)
# --National peak
simulation_yrs_result = [results_container['national_peak'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_peak = national_peak.append(realisation_data)
# --Regional percentage of national peak demand
realisation_data = pd.DataFrame(
[results_container['regional_share_national_peak'][simulation_yr_to_plot]],
columns=data['regions'])
regional_share_national_peak = regional_share_national_peak.append(realisation_data)
# --Regional percentage of national peak demand per person
realisation_data = pd.DataFrame(
[results_container['regional_share_national_peak_pp'][simulation_yr_to_plot]],
columns=data['regions'])
regional_share_national_peak_pp = regional_share_national_peak_pp.append(realisation_data)
# Mean demand of peak day
simulation_yrs_result = [results_container['mean_peak_day_demand'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
daily_mean_peak_day = daily_mean_peak_day.append(realisation_data)
except:
raise Exception("The run '{}' is corrupted".format(path_result_folder))
# Add to scenario container
result_entry = {
'national_heating_peak': national_heating_peak,
'scenario_name': scenario_name,
'peak_hour_demand': peak_hour_demand,
'peak_hour_demand_per_person': peak_hour_demand_per_person,
'national_peak': national_peak,
'regional_share_national_peak': regional_share_national_peak,
'regional_share_national_peak_pp': regional_share_national_peak_pp,
'total_regional_demand_electricity': total_regional_demand_electricity,
'national_electricity': national_electricity,
'national_gas': national_gas,
'national_hydrogen': national_hydrogen,
'daily_mean_peak_day': daily_mean_peak_day}
scenario_result_container.append(result_entry)
# ---------------------------------------------------------------
# TEST PLOT X-axis: Contribution to peak y-axis: Std: deviation
# ---------------------------------------------------------------
x_chart_yrs_storage[simulation_yr_to_plot][scenario_name] = result_entry
# ------------------------------
# Plot national sum over time per fueltype and scenario
# ------------------------------
crit_smooth_line = False
seperate_legend = True
try:
print("... plotting national sum of fueltype over time ")
fig_3_plot_over_time.fueltypes_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="fueltypes_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
fueltypes=['electricity', 'gas', 'hydrogen'],
result_path=result_path,
unit='TWh',
plot_points=True,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILS national sum")
# ------------------------------
# Plot national peak change over time for each scenario including weather variability
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='national_peak',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_peak_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
# ------------------------------
# Plot heating peak change over time for each scenario including weather variability
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='national_heating_peak',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_heating_peak_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
# ------------------------------
# plot PEAK DAY mean
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='daily_mean_peak_day',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="mean_demand_of_peak_day{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
## ------------------------------
## Plotting x-chart
## ------------------------------
fig_3_plot_over_time.plot_std_dev_vs_contribution(
scenario_result_container=x_chart_yrs_storage,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="_scenarios_4_chart_absolute.pdf",
fueltypes=['electricity'],
result_path=result_path,
path_shapefile_input=path_shapefile_input,
unit='TWh',
plot_points=True)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
