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Fix: nb of occupants #40

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c0nb4 merged 1 commit into from
Jan 21, 2025
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Fix: nb of occupants #40

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84 changes: 29 additions & 55 deletions districtgenerator/classes/users.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,62 +128,34 @@ def generate_number_occupants(self,area):
-------
None.
"""
self.nb_occ = []
building_probs = {
"SFH": (0.245114, 0.402323, 0.154148, 0.148869, 0.049623),
"TH": (0.236817, 0.400092, 0.157261, 0.154371, 0.051457),
"MFH": (0.490622, 0.307419, 0.101949, 0.074417, 0.024805),
"AB": (0.490622, 0.307419, 0.101949, 0.074417, 0.024805),
}
probabilities = building_probs.get(self.building, building_probs["MFH"])

# Build cumulative distribution
import random
cdf = []
cumul_sum = 0.0
for p in probabilities:
cumul_sum += p
cdf.append(cumul_sum)

# Single pass for all flats
for _ in range(self.nb_flats):
r = random.random()
occupant = 1
# Find the index where r fits into the cumulative distribution
for idx, threshold in enumerate(cdf):
if r < threshold:
occupant = idx + 1 # occupant is 1-based
break
self.nb_occ.append(occupant)

if self.building == "SFH":
# choose random number of occupants (1-5) for single family houses (assumption)
probabilities = (0.245114, 0.402323, 0.154148, 0.148869, 0.049623) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a single-family house, assuming a maximum of 5 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
# loop over all flats of current single family house
for k in range(self.nb_flats):
random_nb = rd.random() # picking random number in [0,1)
j = 1 # staring with one occupant
# the random number decides how many occupants are chosen (1-5)
while j <= 5 :
if random_nb < sum(probabilities[:j]) :
self.nb_occ.append(j) # minimum is 1 occupant
break
j += 1

elif self.building == "TH":
# choose random number of occupants (1-5) for terraced houses (assumption)
probabilities = (0.236817, 0.400092, 0.157261, 0.154371, 0.051457) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a terraced house, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
# loop over all flats of current terraced house
for k in range(self.nb_flats):
random_nb = rd.random() # picking random number in [0,1)
j = 1 # staring with one occupant
# the random number decides how many occupants are chosen (1-5)
while j <= 5 :
if random_nb < sum(probabilities[:j]) :
self.nb_occ.append(j) # minimum is 1 occupant
break
j += 1

elif self.building == "MFH":
# choose random number of occupants (1-5) for each flat in the multi family house (assumption)
probabilities = (0.490622, 0.307419, 0.101949, 0.074417, 0.024805) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a flat, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
# loop over all flats of current multi family house
for k in range(self.nb_flats):
random_nb = rd.random() # picking random number in [0,1)
j = 1 # staring with one occupant
# the random number decides how many occupants are chosen (1-5)
while j <= 5 :
if random_nb < sum(probabilities[:j]) :
self.nb_occ.append(j) # minimum is 1 occupant
break
j += 1

elif self.building == "AB":
# choose random number of occupants (1-5) for each flat in the apartment block (assumption)
probabilities = (0.490622, 0.307419, 0.101949, 0.074417, 0.024805) # Probabilities of having 1, 2, 3, 4 or 5 occupants in a flat, assuming a maximum of 4 occupants. Sources: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-mietwohnungen.html and https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Wohnen/Tabellen/tabelle-wo2-eigentuemerwohnungen.html
# loop over all flats of current apartment block
for k in range(self.nb_flats):
random_nb = rd.random() # picking random number in [0,1)
j = 1 # staring with one occupant
# the random number decides how many occupants are chosen (1-5)
while j <= 5 :
if random_nb < sum(probabilities[:j]) :
self.nb_occ.append(j) # minimum is 1 occupant
break
j += 1


def generate_annual_el_consumption(self):
Expand Down Expand Up @@ -254,6 +226,8 @@ def generate_annual_el_consumption(self):
# A random integer is selected as the current demand, which must lie between the two values determined by the first random number
break
i += 1


def generate_lighting_index(self, area):
"""
Choose a random lighting index between 0 and 99 for the residential buildings.
Expand Down
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