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Use_Cases.py
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Use_Cases.py
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import Plots
import Utility
import pandas as pd
import numpy as np
def uc1_public_fast(
fuel_stations, boundaries,
amenities, traffic_data,
region, region_key, radius):
uc_id = 'Use_Case_1_Public_Fast'
# radius = 900 # radius around fuel station for traffic acquisition
# get fuelstations in region
anz_fuel_stations = len(fuel_stations)
fuel_in_region_bool = pd.Series(fuel_stations.geometry.within(boundaries.geometry[region_key]), name='Bool')
fuel_stations = fuel_stations.join(fuel_in_region_bool)
# add empty column for Traffic
data = np.zeros(anz_fuel_stations)
traffic = pd.Series(data, name='traffic')
fuel_stations = fuel_stations.join(traffic)
# locate fuelstations in region
fs = fuel_stations.loc[fuel_stations['Bool'] == 1]
x = np.arange(0, len(fs))
fs = fs.assign(INDEX=x)
fs.set_index('INDEX', inplace=True)
# Calculating distribution Weights for Fuel Stations
circles = fs.buffer(radius)
anz_fs = len(fs)
# Loop for calculating weight by using the appr. daily Traffic around Fuel Station
i = 0
while i < anz_fs:
# Trafficdata inside a radius of 900 around Fuel Station
traffic_around_fs = pd.Series(traffic_data.geometry.within(circles.geometry.iloc[i]), name='Bool')
traffic_bool = traffic_data.join(traffic_around_fs)
traffic_around_fs_true = traffic_data.loc[traffic_bool['Bool'] == 1]
traffic_sum = traffic_around_fs_true['dtv'].sum()
fs.iloc[i, 4] = traffic_sum
i += 1
anz_fs = len(fs)
# Distribution of Energy
if anz_fs > 0:
data = np.zeros(anz_fs,)
energy_sum_per_fs = pd.Series(data, name='energysum')
fs = fs.join(energy_sum_per_fs)
load_power = amenities.loc[:, 'sum UC home']
load_power.name = 'loadpower_hpc'
load_power = pd.to_numeric(load_power)
energy_sum = load_power*15/60 # Ladeleistung in Energie umwandeln
energy_sum_overall = energy_sum.sum()
print(energy_sum_overall, 'kWh got fastcharged in region ', region_key)
# sort descending by traffic
fs = fs.sort_values(by=['traffic'], ascending=False)
x = np.arange(0, len(fs))
fs = fs.assign(INDEX=x)
fs.set_index('INDEX', inplace=True)
fs['energysum'] = Utility.apportion(fs['traffic'], energy_sum_overall)
fs['conversionfactor'] = fs['energysum'] / energy_sum_overall
else:
print('No fast charging possible, because no fuel station in the area!')
if anz_fs != 0:
Plots.plot_uc1(fs, region,
traffic_data, circles)
col_select = ['geometry', 'traffic', 'energysum', 'conversionfactor']
Utility.save(fs, uc_id, col_select, region_key)
return fs
def uc2_public_slow(
public, boundaries,
amenities, poi,
region, region_key):
print('UC2')
uc_id = 'Use_Case_2_Public_Slow'
# get poi's in region
public_in_region_bool = pd.Series(public.geometry.within(boundaries.geometry[region_key]), name='Bool')
public_in_region = public.join(public_in_region_bool)
pir = public_in_region.loc[public_in_region['Bool'] == 1] # pir = public in region
anz_pir = len(pir)
data = np.zeros(anz_pir, )
es = pd.Series(data, name='energysum')
pir = pir.join(es)
load_power = amenities.loc[:, 'sum UC leisure']
load_power.name = 'chargepower_public'
load_power = pd.to_numeric(load_power)
energy_sum = load_power * 15 / 60 # Ladeleistung in Energie umwandeln
energy_sum_overall = energy_sum.sum()
print(energy_sum_overall, 'kWh got charged at public space in region', region_key)
# distribution of energysum based on weight of poi
anz_pir = len(pir)
pir['newindex'] = np.arange(anz_pir)
pir.set_index('newindex', inplace=True)
data = np.zeros(anz_pir)
pir['weight'] = pd.Series(data)
a = pir['amenity']
le = pir['leisure']
s = pir['shop']
t = pir['tourism']
# extract pois by Key
poia = poi.loc[poi['OSM-Key'] == 'amenity']
poil = poi.loc[poi['OSM-Key'] == 'leisure']
pois = poi.loc[poi['OSM-Key'] == 'shop']
# combining POI-data and geopackage
i = 0
while i <= anz_pir - 1:
if a.iloc[i] is not None and a.iloc[i] in poia['OSM-Value'].values:
data = poia.loc[poi['OSM-Value'] == a.iloc[i], "weight"]
pir.iloc[i, 8] = data
elif le.iloc[i] is not None and le.iloc[i] in poil['OSM-Value'].values:
data = poil.loc[poi['OSM-Value'] == le.iloc[i], "weight"]
pir.iloc[i, 8] = data
elif s.iloc[i] is not None and s.iloc[i] in pois['OSM-Value'].values:
data = pois.loc[poi['OSM-Value'] == s.iloc[i], "weight"]
pir.iloc[i, 8] = data
elif t.iloc[i] is not None and t.iloc[i] in poi['OSM-Value'].values:
pir.iloc[i, 8] = 0
else:
pir.iloc[i, 8] = 0
print('Missing OSM Key in Geopackage-Data for UC2')
i += 1
pir['weight'] = pd.to_numeric(pir['weight'], errors='coerce')
x = np.arange(0, len(pir))
pir = pir.assign(INDEX=x)
pir.set_index('INDEX', inplace=True)
pir['energysum'] = Utility.apportion(pir['weight'], energy_sum_overall)
pir['conversionfactor'] = pir['energysum'] / energy_sum_overall
Plots.plot_uc2(pir, region)
col_select = ['name', 'amenity', 'leisure', 'shop', 'tourism',
'geometry', 'energysum', 'weight', 'conversionfactor']
Utility.save(pir, uc_id, col_select, region_key)
def uc3_private_home(
zensus, boundaries,
amenities, region,
region_key):
print('UC3')
uc_id = 'Use_Case_3_Private_Home'
# getting zenusdata in region
home_in_region_bool = pd.Series(zensus.geometry.within(boundaries.geometry[region_key]), name='Bool')
home_in_region = zensus.join(home_in_region_bool)
hir = home_in_region.loc[home_in_region['Bool'] == 1] # hir = home in region
anz_hir = len(hir)
data = np.zeros(anz_hir, )
es = pd.Series(data, name='energysum')
hir = hir.join(es)
hir['energysum'] = np.nan
load_power = amenities.loc[:, 'sum UC home']
load_power.name = 'chargepower_home'
load_power = pd.to_numeric(load_power)
energy_sum = load_power * 15 / 60 # Ladeleistung in Energie umwandeln
energy_sum_overall = energy_sum.sum()
print(energy_sum_overall, 'kWh got charged at home in region', region_key)
# distribution of energysum based on population in 100x100 area
pop_in_area = sum(hir['population'])
hir = hir.sort_values(by=['population'], ascending=False)
hir['conversionfactor'] = home_in_region['population'] / pop_in_area
x = np.arange(0, len(hir))
hir = hir.assign(INDEX=x)
hir.set_index('INDEX', inplace=True)
hir['energysum'] = Utility.apportion(hir['conversionfactor'], energy_sum_overall)
Plots.plot_uc3(hir, region)
col_select = ['population', 'geom_point', 'geometry', 'energysum', 'conversionfactor']
Utility.save(hir, uc_id, col_select, region_key)
return zensus
def uc4_private_work(work, boundaries,
amenities, region,
region_key, weight_retail,
weight_commercial, weight_industrial):
print('UC4')
uc_id = 'Use_Case_4_Private_Work'
# getting pois of area
work_in_region_bool = pd.Series(work.geometry.within(boundaries.geometry[region_key]), name='Bool')
work_in_region = work.join(work_in_region_bool)
wir = work_in_region.loc[work_in_region['Bool'] == 1] # wir = work in region
anz_wir = len(wir)
data = np.zeros(anz_wir, )
es = pd.Series(data, name='energysum')
wir = wir.join(es)
wir['energysum'] = np.nan
load_power = amenities.loc[:, 'sum UC work']
load_power.name = 'chargepower_work'
load_power = pd.to_numeric(load_power)
energy_sum = load_power * 15 / 60 # convert power to energy
energy_sum_overall = energy_sum.sum()
print(energy_sum_overall, 'kWh got charged at work in region', region_key)
# distribution of energysum based on area of Polygon
anz_wir = len(wir)
wir['newindex'] = np.arange(anz_wir)
wir.set_index('newindex', inplace=True)
data = np.zeros(anz_wir)
wir['weight'] = pd.Series(data)
# calculating the area of polygons
area = wir['geometry'].area / 10 ** 6
sum_area = sum(area)
# calculation of weight for type of use
i = 0
while i <= anz_wir - 1:
if 'retail' in wir.iloc[i, 0]:
wir.iloc[i, 4] = weight_retail * area[i] / sum_area # Weight for retail
elif 'commercial' in wir.iloc[i, 0]:
wir.iloc[i, 4] = weight_commercial * area[i] / sum_area # Weight for commercial
elif 'industrial' in wir.iloc[i, 0]:
wir.iloc[i, 4] = weight_industrial * area[i] / sum_area # Weight for industrial
else:
print('no specification')
i += 1
x = np.arange(0, len(wir))
wir = wir.assign(INDEX=x)
wir.set_index('INDEX', inplace=True)
wir['energysum'] = Utility.apportion(wir['weight'], energy_sum_overall)
wir['conversionfactor'] = wir['energysum']/energy_sum_overall
wir['center_geo'] = wir.centroid
Plots.plot_uc4(wir, region)
col_select = ['landuse', 'geometry', 'center_geo', 'energysum', 'weight', 'conversionfactor']
Utility.save(wir, uc_id, col_select, region_key)