def __init__(self, age): self.generate(age) self.WorkdayArrival_Avg = self.WorkdayLeave_Avg + profilegentools.gaussMinMax(7*60, 30) if(age < 16): self.WorkdayBedTime_Avg = profilegentools.gaussMinMax((23-(16-age)*0.25)*60, 30) self.WeekendBedTime_Avg = profilegentools.gaussMinMax((23-(16-age)*0.25)*60, 30) self.WorkdaySport_Avg = profilegentools.gaussMinMax(19*60, 1*60)
def addPV(self, area): self.hasPV = True self.pvArea = area self.pvEfficiency = random.randint(config.PVEfficiencyMin, config.PVEfficiencyMax) self.pvAzimuth = profilegentools.gaussMinMax(config.PVAzimuthMean, config.PVAzimuthSigma) if(self.pvAzimuth < 0): self.pvAzimuth = self.pvAzimuth + 360 self.pvElevation = profilegentools.gaussMinMax(config.PVAngleMean, config.PVAngleSigma)
def __init__(self, age):
self.generate(age)
self.generateWorkdays(0)
self.WeekendWakeUp_Avg = profilegentools.gaussMinMax(8.5 * 60, 1 * 60)
self.WeekendActivities = 0.7 - (0.03 * (age - 65))
self.WorkdayBedTime_Avg = profilegentools.gaussMinMax(
(23 - (age - 65) * 0.15) * 60, 30)
self.WeekendBedTime_Avg = profilegentools.gaussMinMax(
(23 - (age - 65) * 0.15) * 60, 30)
def __init__(self, age):
self.generate(age)
self.WorkdayArrival_Avg = self.WorkdayLeave_Avg + profilegentools.gaussMinMax(
7 * 60, 30)
if (age < 16):
self.WorkdayBedTime_Avg = profilegentools.gaussMinMax(
(23 - (16 - age) * 0.25) * 60, 30)
self.WeekendBedTime_Avg = profilegentools.gaussMinMax(
(23 - (16 - age) * 0.25) * 60, 30)
self.WorkdaySport_Avg = profilegentools.gaussMinMax(19 * 60, 1 * 60)
def generate(self, consumption):
self.Runtime = profilegentools.gaussMinMax(15, 5)
self.Offtime = profilegentools.gaussMinMax(40, 10)
self.Consumption = consumption
self.State = 0
self.RuntimeCycle = self.Runtime
self.OfftimeCycle = self.Offtime
self.CycleProgress = random.randint(0, self.Runtime + self.Offtime)
if (self.CycleProgress < (self.Runtime + self.Offtime)):
self.State = 1
def addPV(self, area):
self.hasPV = True
self.pvArea = area
self.pvEfficiency = random.randint(config.PVEfficiencyMin,
config.PVEfficiencyMax)
self.pvAzimuth = profilegentools.gaussMinMax(config.PVAzimuthMean,
config.PVAzimuthSigma)
if (self.pvAzimuth < 0):
self.pvAzimuth = self.pvAzimuth + 360
self.pvElevation = profilegentools.gaussMinMax(config.PVAngleMean,
config.PVAngleSigma)
def __init__(self, age): self.generate(age) self.generateWorkdays(0) self.WeekendWakeUp_Avg = profilegentools.gaussMinMax(8.5*60, 1*60) self.WeekendActivities = 0.7 - (0.03*(age-65)) self.WorkdayBedTime_Avg = profilegentools.gaussMinMax((23-(age-65)*0.15)*60, 30) self.WeekendBedTime_Avg = profilegentools.gaussMinMax((23-(age-65)*0.15)*60, 30)
def generate(self, consumption): self.Runtime = profilegentools.gaussMinMax(15, 5) self.Offtime = profilegentools.gaussMinMax(40, 10) self.Consumption = consumption self.State = 0 self.RuntimeCycle = self.Runtime self.OfftimeCycle = self.Offtime self.CycleProgress = random.randint(0,self.Runtime+self.Offtime) if(self.CycleProgress < (self.Runtime+self.Offtime)): self.State = 1
def __init__(self):
self.generate()
self.ConsumptionYearly = profilegentools.gaussMinMax(
2010, 400
) * config.consumptionFactor #kWh http://www.nibud.nl/uitgaven/huishouden/gas-elektriciteit-en-water.html
age = random.triangular(65, 85, 70)
self.Persons = [persons.PersonRetired(age)]
if (random.randint(1, 2) == 1):
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeBigMin,
config.ConsumptionFridgeBigMax))
]
else:
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax)),
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax))
]
self.hasDishwasher = random.randint(0, 5) < 3 #40%
#Determine washing days
self.generateWashingdays(random.randint(2, 3))
#Dermine Dishwasher times
if self.hasDishwasher:
self.generateDishwashdays(3)
def __init__(self, parttime=False, jobless=False):
self.generate()
numKids = round(
max(min(4, random.gauss(1.7, 0.4)), 1)
) # http://www.cbs.nl/nl-NL/menu/themas/bevolking/faq/specifiek/faq-hoeveel-kinderen.htm
self.ConsumptionYearly = profilegentools.gaussMinMax(
2010 + (700 * numKids), 500 + (numKids * 100)
) * config.consumptionFactor #kWh http://www.nibud.nl/uitgaven/huishouden/gas-elektriciteit-en-water.html
ageParents = random.randint(40, 55)
if parttime == True:
self.Persons = [
persons.PersonWorker(ageParents),
persons.PersonParttimeWorker(ageParents)
]
elif jobless == True:
self.Persons = [
persons.PersonWorker(ageParents),
persons.PersonJobless(ageParents)
]
else:
self.Persons = [persons.PersonWorker(ageParents)]
self.Persons.append(
copy.deepcopy(self.Persons[0])
) #Make a copy, we expect a household to be rather synchronized!
#To make life easy, only one persons.Person will use the electric vehicle, so only the main persons.Person will receive a driving distance
self.Persons[0].setDistanceToWork(
round(
max(
0,
random.gauss(config.commuteDistanceMean,
config.commuteDistanceSigma))))
#now add the kids
for i in range(0, numKids):
self.Persons.append(
persons.PersonStudent(
random.randint(ageParents - 3, ageParents + 3) - 30))
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax)),
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax))
]
self.hasDishwasher = random.randint(0, 5) < 4 #60%
#Determine washing days
self.generateWashingdays(min(5 + numKids, 7))
#Dermine Dishwasher times
if self.hasDishwasher:
self.generateDishwashdays(min(5 + numKids, 7))
def __init__(self, parttime=False, jobless=False):
self.generate()
self.ConsumptionYearly = profilegentools.gaussMinMax(
3360, 700
) * config.consumptionFactor #kWh http://www.nibud.nl/uitgaven/huishouden/gas-elektriciteit-en-water.html
assert (parttime == False
or jobless == False) # ONLY one van be active
age = random.randint(26, 65)
if parttime == True:
self.Persons = [
persons.PersonWorker(age),
persons.PersonParttimeWorker(age)
]
elif jobless == True:
self.Persons = [
persons.PersonWorker(age),
persons.PersonJobless(age)
]
else:
self.Persons = [
persons.PersonWorker(age),
persons.PersonWorker(age)
]
#To make life easy, only one persons.Person will use the electric vehicle, so only the main persons.Person will receive a driving distance
self.Persons[0].setDistanceToWork(
round(
max(
0,
random.gauss(config.commuteDistanceMean,
config.commuteDistanceSigma))))
if (random.randint(1, 2) == 1):
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeBigMin,
config.ConsumptionFridgeBigMax))
]
else:
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax)),
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax))
]
self.hasDishwasher = random.randint(0, 5) < 2 #40%
#Determine washing days
self.generateWashingdays(random.randint(3, 4))
#Dermine Dishwasher times
if self.hasDishwasher:
self.generateDishwashdays(3)
def __init__(self):
self.generate()
self.ConsumptionYearly = profilegentools.gaussMinMax(
2010, 400
) * config.consumptionFactor #kWh http://www.nibud.nl/uitgaven/huishouden/gas-elektriciteit-en-water.html
self.Persons = [persons.PersonWorker(random.randint(26, 65))]
#For information about commuters, see this:
#http://www.kimnet.nl/publicatie/mobiliteitsbalans-2013
#Approx 50% uses the car to commute, which is our interrest as this part can be replaced by (PH)EV
#http://www.cbs.nl/NR/rdonlyres/13516DAC-36E1-47B7-9918-2364EF7D0B48/0/2011k2v4p34art.pdf
#3 out of 5 working people don't work in town. Fair to assume that these people commute by car
#Average commute distance is not too clear from multiple sources, such as the once above. Something around 20km seems to be average in most sources
#However 30 is also mentioned: http://www.nederlandheeftwerk.nl/index.php/cms_categorie/58707/bb/1/id/58707
#Depends also on the region and work in vicinity.
self.Persons[0].setDistanceToWork(
round(
max(
0,
random.gauss(config.commuteDistanceMean,
config.commuteDistanceSigma))))
if (random.randint(1, 2) == 1):
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeBigMin,
config.ConsumptionFridgeBigMax))
]
else:
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax)),
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax))
]
#We synchronize the dishwasher and dryer based on the annual consumption. Furthermore, this also influences the number of washes.
self.hasDishwasher = random.randint(0, 5) == 0 #20%
#Determine washing days
self.generateWashingdays(random.randint(2, 3))
#Dermine Dishwasher times
if self.hasDishwasher:
self.generateDishwashdays(3)
def __init__(self):
self.generate()
self.ConsumptionYearly = profilegentools.gaussMinMax(
2010, 400
) * config.consumptionFactor #kWh http://www.nibud.nl/uitgaven/huishouden/gas-elektriciteit-en-water.html
age = random.triangular(65, 85, 70)
self.Persons = [persons.PersonRetired(age)]
#To make life easy, only one persons.Person will use the electric vehicle, so only the main persons.Person will receive a driving distance
self.Persons[0].setDistanceToWork(
round(
max(
0,
random.gauss(config.commuteDistanceMean,
config.commuteDistanceSigma))))
if (random.randint(1, 2) == 1):
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeBigMin,
config.ConsumptionFridgeBigMax))
]
else:
self.Fridges = [
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax)),
devices.DeviceFridge(
random.randint(config.ConsumptionFridgeSmallMin,
config.ConsumptionFridgeSmallMax))
]
self.hasDishwasher = random.randint(0, 5) < 3 #40%
#Determine washing days
self.generateWashingdays(random.randint(2, 3))
#Dermine Dishwasher times
if self.hasDishwasher:
self.generateDishwashdays(3)
def generate(self, age): #Variates could also use a gauss distribution, some persons are more predictable than others ;) self.Age = age self.WorkdayWakeUp_Avg = profilegentools.gaussMinMax(7*60, 1.5*60) self.WorkdayWakeUp_Variate = 10 self.WorkdayLeave_Avg = self.WorkdayWakeUp_Avg + profilegentools.gaussMinMax(45, 15) self.WorkdayLeave_Variate = 10 self.WorkdayArrival_Avg = self.WorkdayLeave_Avg + profilegentools.gaussMinMax(8.5*60, 30) self.WorkdayArrival_Variate = 15 self.WorkdaySport_Avg = self.WorkdayArrival_Avg + profilegentools.gaussMinMax(2.5*60, 2*60) self.WorkdaySport_Variate = 15 self.WorkdaySportDuration_Avg = profilegentools.gaussMinMax(1.5*60, 30) self.WorkdaySportDuration_Variate = 10 self.WorkdayBedTime_Avg = self.WorkdayWakeUp_Avg + profilegentools.gaussMinMax(15.5*60,30) self.WorkdayBedTime_Variate = 15 self.WorkdayActivities = random.randint(config.personWeekdayActivityChanceMin, config.personWeekdayActivityChanceMax) / 100 #Chance to conduct random activities self.WeekendWakeUp_Avg = profilegentools.gaussMinMax(9*60, 2*60) self.WeekendWakeUp_Variate = 20 self.WeekendSport_Avg = profilegentools.gaussMinMax(14*60, 5*60) self.WeekendSport_Variate = 60 self.WeekendSportDuration_Avg = profilegentools.gaussMinMax(1.5*60, 30) self.WeekendSportDuration_Variate = 30 self.WeekendBedTime_Avg = profilegentools.gaussMinMax(23*60, 30) self.WeekendBedTime_Variate = 10 self.WeekendActivities = random.randint(config.personWeekendActivityChanceMin, config.personWeekendActivityChanceMax) / 100 #For a new deepcopy, the following values should be regenerated self.WorkdaySportday = 1 + random.randint(1,5) self.WeekendSportday = 6*random.randint(0,1) self.Workdays = range(1,6) self.DistanceToWork = 0 self.CookingTime = self.WorkdayArrival_Avg + 30 self.showerMorning = True self.showerDays = list(range(0, 7)) self.showerDuration = profilegentools.gaussMinMax(8, 3) # Generate Heat parameters self.generateHeatParams()
def generate(self, age):
#Variates could also use a gauss distribution, some persons are more predictable than others ;)
self.Age = age
self.WorkdayWakeUp_Avg = profilegentools.gaussMinMax(7 * 60, 1.5 * 60)
self.WorkdayWakeUp_Variate = 10
self.WorkdayLeave_Avg = self.WorkdayWakeUp_Avg + profilegentools.gaussMinMax(
45, 15)
self.WorkdayLeave_Variate = 10
self.WorkdayArrival_Avg = self.WorkdayLeave_Avg + profilegentools.gaussMinMax(
8.5 * 60, 30)
self.WorkdayArrival_Variate = 15
self.WorkdaySport_Avg = self.WorkdayArrival_Avg + profilegentools.gaussMinMax(
2.5 * 60, 2 * 60)
self.WorkdaySport_Variate = 15
self.WorkdaySportDuration_Avg = profilegentools.gaussMinMax(
1.5 * 60, 30)
self.WorkdaySportDuration_Variate = 10
self.WorkdayBedTime_Avg = self.WorkdayWakeUp_Avg + profilegentools.gaussMinMax(
15.5 * 60, 30)
self.WorkdayBedTime_Variate = 15
self.WorkdayActivities = random.randint(
config.personWeekdayActivityChanceMin,
config.personWeekdayActivityChanceMax
) / 100 #Chance to conduct random activities
self.WeekendWakeUp_Avg = profilegentools.gaussMinMax(9 * 60, 2 * 60)
self.WeekendWakeUp_Variate = 20
self.WeekendSport_Avg = profilegentools.gaussMinMax(14 * 60, 5 * 60)
self.WeekendSport_Variate = 60
self.WeekendSportDuration_Avg = profilegentools.gaussMinMax(
1.5 * 60, 30)
self.WeekendSportDuration_Variate = 30
self.WeekendBedTime_Avg = profilegentools.gaussMinMax(23 * 60, 30)
self.WeekendBedTime_Variate = 10
self.WeekendActivities = random.randint(
config.personWeekendActivityChanceMin,
config.personWeekendActivityChanceMax) / 100
#For a new deepcopy, the following values should be regenerated
self.WorkdaySportday = 1 + random.randint(1, 5)
self.WeekendSportday = 6 * random.randint(0, 1)
self.Workdays = range(1, 6)
self.DistanceToWork = 0
self.CookingTime = self.WorkdayArrival_Avg + 30
self.showerMorning = True
self.showerDays = list(range(0, 7))
self.showerDuration = profilegentools.gaussMinMax(8, 3)
# Generate Heat parameters
self.generateHeatParams()
def generate(self):
#The Yearly consumption is the normal consumption of domestic appliances as found for years in households. This excludes:
# (PH)EV, Heat Pump, PV
self.ConsumptionYearly = profilegentools.gaussMinMax(3500, 500) #kWh
#According to http://www.energie-nederland.nl/wp-content/uploads/2013/04/EnergieTrends2014.pdf, this is the distribution for devices we are interested in:
self.ConsumptionShare = { "Electronics" : profilegentools.gaussMinMax(17,3), \
"Lighting" : profilegentools.gaussMinMax(6,2), \
"Standby" : profilegentools.gaussMinMax(35,6) }
self.Persons = []
self.Consumption = { "Total" : [], \
"Other" : [], \
"Inductive" : [], \
"Fridges" : [], \
"Electronics" : [], \
"Lighting" : [], \
"Standby" : [] }
self.consumptionFactor = { "Other" : [], \
"Inductive" : [], \
"Fridges" : [], \
"Electronics" : [], \
"Lighting" : [], \
"Standby" : [] }
self.HeatGain = { "PersonGain" : [], \
"DeviceGain" : [], \
"SolarGain" : [], \
"VentFlow" : [], \
"Total" : []}
self.HeatDemand = { "DHWDemand" : [], \
"Total" : []}
# Heat gain factors from devices
self.HeatGainShare = { "Other" : 0.3, \
"Fridges" : 0.5, \
"Electronics" : 1.0, \
"Lighting" : 0.8, \
"Standby" : 1.0 }
self.ReactiveConsumption = {"Total" : [], \
"Other" : [], \
"Inductive" : [], \
"Fridges" : [], \
"Electronics" : [], \
"Lighting" : [], \
"Standby" : [] }
self.ReactiveFactor = { "Other" : 1, \
"Inductive" : (random.randint(70,90)/100), \
"Fridges" : (random.randint(50,65)/100), \
"Electronics" : -(random.randint(99,100)/100), \
"Lighting" : -(random.randint(99,100)/100), \
"Standby" : -(random.randint(75,85)/100) }
self.PVProfile = []
self.Occupancy = []
self.hasDishwasher = False
self.hasInductionCooking = random.randint(1, 10) < 4
self.hasEV = False
self.hasHP = False
self.hasCHP = False
self.hasFloorHeating = True
self.numOfWashes = 0 #weekly
self.numOfDishwashes = 0 #Weekly
self.WashingDays = []
self.washingMoment = [-1] * 7
self.DishwashDays = []
self.DishwashMoment = [-1] * 7
#devices
self.Fridges = []
self.Devices = { "Kettle": devices.DeviceKettle(config.ConsumptionKettle),\
"Lighting": devices.DeviceLighting(),\
"Electronics": devices.DeviceElectronics(),\
"Cooking": devices.DeviceCooking(),\
"Ventilation": devices.DeviceVentilation(config.ConsumptionHouseVentilation),\
"Ironing": devices.DeviceIroning(config.ConsumptionIron),\
"Vacuumcleaner": devices.DeviceVacuumcleaner(config.ConsumptionVacuumcleaner),\
"WashingMachine": devices.DeviceWashingMachine(),\
"DishwashMachine": devices.DeviceDishwasher(),\
"ElectricalVehicle": devices.DeviceElectricalVehicle(),\
"PVPanel" : devices.DeviceSolarPanel()}
self.HeatingDevices = { "PersonGain": heatdemand.PersonGain(), \
"Thermostat": heatdemand.Thermostat(), \
"VentFlow": heatdemand.Ventilation(), \
"DHWDemand": heatdemand.DHWDemand() }
self.familyActivites = random.randint(
config.familyOutingChanceMin, config.familyOutingChanceMax) / 100
def generate(self, age):
self.Age = age
# --edit-- shift the activity schedule
self.WorkdayWakeUp_Avg = profilegentools.gaussMinMax(5 * 60, 0.5 * 60)
self.WorkdayWakeUp_Variate = 10
self.WorkdayLeave_Avg = self.WorkdayWakeUp_Avg + \
profilegentools.gaussMinMax(2.5*60, 15)
self.WorkdayLeave_Variate = 10
self.WorkdayArrival_Avg = self.WorkdayLeave_Avg + \
profilegentools.gaussMinMax(8.5*60, 30)
self.WorkdayArrival_Variate = 15
self.WorkdaySport_Avg = self.WorkdayArrival_Avg + \
profilegentools.gaussMinMax(2.5*60, 2*60)
self.WorkdaySport_Variate = 15
self.WorkdaySportDuration_Avg = profilegentools.gaussMinMax(
1.5 * 60, 30)
self.WorkdaySportDuration_Variate = 10
self.WorkdayBedTime_Avg = self.WorkdayWakeUp_Avg + \
profilegentools.gaussMinMax(16*60, 30)
self.WorkdayBedTime_Variate = 15
self.WorkdayActivities = random.randint(
config.personWeekdayActivityChanceMin,
config.personWeekdayActivityChanceMax
) / 100 # Chance to conduct random activities
self.WeekendWakeUp_Avg = profilegentools.gaussMinMax(8 * 60, 2 * 60)
self.WeekendWakeUp_Variate = 20
self.WeekendSport_Avg = profilegentools.gaussMinMax(14 * 60, 5 * 60)
self.WeekendSport_Variate = 60
self.WeekendSportDuration_Avg = profilegentools.gaussMinMax(
1.5 * 60, 30)
self.WeekendSportDuration_Variate = 30
self.WeekendBedTime_Avg = profilegentools.gaussMinMax(22 * 60, 30)
self.WeekendBedTime_Variate = 10
self.WeekendActivities = random.randint(
config.personWeekendActivityChanceMin,
config.personWeekendActivityChanceMax) / 100
# For a new deepcopy, the following values should be regenerated
self.WorkdaySportday = 1 + random.randint(1, 5)
self.WeekendSportday = 6 * random.randint(0, 1)
self.Workdays = range(1, 6)
self.DistanceToWork = 0
self.CookingTime = self.WorkdayArrival_Avg + 30
self.showerMorning = True
self.showerDays = list(range(0, 7))
self.showerDuration = profilegentools.gaussMinMax(8, 3)
# Generate Heat parameters
self.generateHeatParams()
