import numpy as np
import sys
sys.path.append('../NTS')
# my code
from NTSenergyPrediction import EnergyPrediction
profiles = []
for i in range(0, 1000):
profiles.append([0.0] * 1440)
i = 0
run = EnergyPrediction('3', '7', regionType='3')
vehicleProfiles = run.returnDumbChargingProfiles(1000, 3.5)
#vehicleProfilesPO = run.returnPsuedoOptimalChargingProfiles(1000,3.5)
#'''
with open('../../Documents/vehicle_demand_pool.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
for profile in vehicleProfiles:
writer.writerow(profile)
'''
with open('../../Documents/vehicle_demand_poolPO.csv','w') as csvfile:
writer = csv.writer(csvfile)
for profile in vehicleProfilesPO:
writer.writerow(profile)
'''
# packages
import matplotlib.pyplot as plt
import numpy as np
import csv
import random
# my code
from vehicleModelCopy import Drivecycle, Vehicle
from NTSenergyPrediction import EnergyPrediction, NationalEnergyPrediction
day = '3'
month = '2'
run = EnergyPrediction(day)
dumbProfile = run.getDumbChargingProfile(3.5,tmax=36*60,logOutofCharge=True,
highUseWorkCharging=False,
highUseHomeCharging=False,
highUseShopCharging=False)
print float(run.nOutOfCharge)*100/run.nVehicles,
print '% out of charge in scenario 1'
run = EnergyPrediction(day)
dumbProfile = run.getDumbChargingProfile(3.5,tmax=36*60,logOutofCharge=True,
highUseWorkCharging=False,
highUseHomeCharging=True,
highUseShopCharging=False)
print float(run.nOutOfCharge)*100/run.nVehicles,
print '% out of charge in scenario 2'
energy = [0] * 200
for region in regionBreakdown:
population = regionBreakdown[region] * totalPopulation
simulation = Simulation(region, month, day, population, 1)
for i in simulation.fleet.fleet:
total = 0
#print i
#print simulation.fleet.fleet[i]
for j in i.energyLog:
total += j[1]
energy[int(total)] += numbers[region]
run2 = EnergyPrediction('3', '1', model='linear')
energy3 = run2.plotEnergyConsumption(returnResults=True,
newFigure=False,
wait=True)
# normalise
nEnergy3 = []
for i in range(0, len(energy3)):
nEnergy3.append(float(energy3[i]) / sum(energy3))
# normalise
nEnergy = []
for i in range(0, len(energy)):
nEnergy.append(float(energy[i]) / sum(energy))
run = EnergyPrediction('3', '1')
# packages
import matplotlib.pyplot as plt
import numpy as np
import csv
import random
# my code
from vehicleModelCopy import Drivecycle, Vehicle
from NTSenergyPrediction import EnergyPrediction, NationalEnergyPrediction
nHours = 36
pointsPerHour = 10
run = EnergyPrediction('3', '2')
dumb = run.getDumbChargingProfile(3.5, nHours * 60)
baseLoad = []
for i in range(0, pointsPerHour * nHours):
baseLoad.append(random.random() * 6000)
smartProfiles = run.getOptimalChargingProfiles(7.0,
baseLoad,
pointsPerHour=pointsPerHour)
smart = [0.0] * nHours * pointsPerHour
for vehicle in smartProfiles:
for i in range(0, nHours * pointsPerHour):
smart[i] += smartProfiles[vehicle][i]
plt.figure(1)
plt.plot(dumb)
plt.plot(range(0, 36 * 60, 60 / pointsPerHour), smart)
import csv
import matplotlib.pyplot as plt
from NTSenergyPrediction import EnergyPrediction, NationalEnergyPrediction
run = EnergyPrediction('3', '5')
energy = run.plotEnergyConsumption(returnResults=True, wait=True)
plt.xlim(0, 60)
'''
run2 = EnergyPrediction('3','5',car='tesla')
energy2 = run2.plotEnergyConsumption(returnResults=True, wait=True)
'''
plt.figure(2)
plt.subplot(2, 1, 1)
plt.bar(range(0, 24), energy[:24], color='b')
plt.bar(range(24, len(energy)), energy[24:], color='r')
plt.xlim(-0.5, 80)
plt.text(60,
500,
str(float(int(float(sum(energy[24:]) * 10000) / sum(energy))) / 100) +
'%',
fontsize=15)
plt.title('Nissan Leaf')
'''
plt.subplot(2,1,2)
plt.bar(range(0,60),energy2[:60],color='b')
plt.bar(range(60,len(energy2)),energy[60:],color='r')
plt.xlim(-0.5,80)
plt.text(60,500,str(float(int(float(sum(energy2[60:])*10000)/sum(energy2)))/100)+'%',fontsize=15)
plt.title('Tesla 60S')
'''
plt.show()
# packages
import matplotlib.pyplot as plt
import numpy as np
import csv
# my code
from vehicleModelCopy import Drivecycle, Vehicle
from NTSenergyPrediction import EnergyPrediction, BaseLoad
day = '3'
month = '7'
run = EnergyPrediction(day, month, region='7')
runBase = BaseLoad(day, month, 36, unit='k')
base = runBase.getLoad(population=0.1 * run.nPeople)
profiles = run.getOptimalChargingProfiles(4, base, chargeAtWork=True)
profiles2 = run.getOptimalChargingProfiles(4, base, chargeAtWork=False)
summed = [0.0] * 36
summed2 = [0.0] * 36
for vehicle in profiles:
for i in range(0, 36):
summed[i] += profiles[vehicle][i]
for vehicle in profiles2:
for i in range(0, 36):
summed2[i] += profiles2[vehicle][i]
t = np.linspace(0, 36, num=36 * 60)
# my code
from vehicleModelCopy import Drivecycle, Vehicle
from NTSenergyPrediction import EnergyPrediction
nissanLeaf = Vehicle(1521.0, 29.92, 0.076, 0.02195, 0.86035, 24.0)
regionTypes = {
'1': 'Urban Conurbation',
'2': 'Urban City and Town',
'3': 'Rural Town',
'4': 'Rural Village'
}
for rt in regionTypes:
offset = float(int(rt) - 1) / 4
test = EnergyPrediction('3', '5', nissanLeaf, regionType=rt)
#test.plotMileage(wait=True)
plt.figure(1)
test.plotEnergyConsumption(newFigure=False,
wait=True,
label=regionTypes[rt],
normalise=True,
offset=offset,
width=0.25)
plt.figure(2)
test.plotEnergyConsumption(newFigure=False,
wait=True,
label=regionTypes[rt],
normalise=False,
offset=offset,
import csv
import matplotlib.pyplot as plt
import numpy as np
import math
from NTSenergyPrediction import EnergyPrediction
y = []
for r in range(1, 5):
run = EnergyPrediction('3', regionType=str(r))
energy = 0
n = 0
print len(run.energy)
for vehicle in run.energy:
energy += run.energy[vehicle]
n += 1
energy = float(energy) / n
y.append(energy)
plt.figure(1)
plt.rcParams["font.family"] = 'serif'
x_ticks = [
'Urban\n Conurbation', 'Urban City\n & Town', 'Rural\n Town &\n Fringe',
'Rural\n Village,\n Hamlet &\n Isolated Dwelling'
]
plt.bar(range(1, 5), y)
plt.xticks(range(1, 5), x_ticks)
plt.ylabel('average predicted \n consumption (kWh)')
# packages
import matplotlib.pyplot as plt
import numpy as np
import csv
# my code
from NTSenergyPrediction import EnergyPrediction, BaseLoad
day = '3'
month = '1'
run = EnergyPrediction(day,month,region='1')
base = BaseLoad(day,month,36,unit='k')
baseload = base.getLoad(population=int(150.0*65/28))
profiles = run.getPsuedoOptimalProfile(4,baseload,weighted=False,
returnIndividual=True,
allowOverCap=False,
deadline=12)
vehicles = []
for vehicle in profiles[1]:
vehicles.append(vehicle)
profiles2 = run.getDumbChargingProfile(3.5,36*60,individuals=vehicles,
highUseHomeCharging=False,
highUseWorkCharging=False,
highUseShopCharging=False)
profiles3 = run.getOptimalChargingProfiles(3.5,baseload,individuals=vehicles,
sampleScale=False,allowOverCap=False)
import numpy as np
# my code
from vehicleModelCopy import Drivecycle, Vehicle
from NTSenergyPrediction import EnergyPrediction
nissanLeaf = Vehicle(1521.0, 29.92, 0.076, 0.02195, 0.86035, 24.0)
days = {
'1': 'Monday'
} #,'2':'Tuesday','3':'Wednesday','4':'Thursday','5':'Friday',
#'6':'Saturday','7':'Sunday'}
t = np.linspace(0, 48, 48 * 60)
plt.figure(1)
for i in days:
test = EnergyPrediction(i, '5', nissanLeaf, regionType='2')
profile = test.getDumbChargingProfile(3.5, 48 * 60, scalePerVehicle=True)
plt.plot(t, profile, label=days[i])
x = np.linspace(4, 36, num=9)
plt.grid()
my_xticks = [
'04:00 \n Day 1', '08:00', '12:00', '16:00', '20:00', '0:00',
'04:00 \n Day 2', '08:00', '12:00'
]
plt.xticks(x, my_xticks)
plt.xlim(0, 40)
plt.xlabel('time')
plt.ylabel('power per household (kW)')
plt.title('Dumb Charging in May')
plt.legend()
import csv
import matplotlib.pyplot as plt
import numpy as np
import math
from NTSenergyPrediction import EnergyPrediction
y = []
for day in range(1, 8):
run = EnergyPrediction(str(day), month='2')
energy = 0
n = 0
for vehicle in run.energy:
energy += run.energy[vehicle]
n += 1
energy = float(energy) / n
y.append(energy)
plt.figure(1)
plt.rcParams["font.family"] = 'serif'
plt.subplot(2, 1, 1)
x_ticks = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
plt.bar(range(1, 8), y)
plt.xticks(range(1, 8), x_ticks)
plt.ylabel('average predicted \n consumption (kWh)')
y = []
for mo in range(1, 13):