import numpy as np
import matplotlib.pyplot as plt
import random
import csv
from cvxopt import matrix, spdiag, solvers, sparse
from vehicleModelCopy import Drivecycle, Vehicle
results = []
#distances = []
#energies = []
distanceVsEnergy = {}
nissanLeaf = Vehicle(1705.0,29.92,0.076,0.02195,0.86035,32.0)
nissanLeaf.load += 75.0
#accessoryLoad = 0.2 # kW
cars = []
predicted = []
actual = []
predictions = {'01':[],'02':[],'03':[],'04':[],'05':[],'06':[],'07':[],'08':[],
'09':[],'10':[],'11':[],'12':[]}
actuals = {'01':[],'02':[],'03':[],'04':[],'05':[],'06':[],'07':[],'08':[],
'09':[],'10':[],'11':[],'12':[]}
titles = {'01':'Jan','02':'Feb','03':'Mar','04':'Apr','05':'May','06':'Jun',
'07':'Jul','08':'Aug','09':'Sep','10':'Oct','11':'Nov','12':'Dec'}
trainingData = {'01':[],'02':[],'03':[],'04':[],'05':[],'06':[],'07':[],'08':[],
'09':[],'10':[],'11':[],'12':[]}
def __init__(self,
month,
fleetSize,
car=None,
region=None,
average=True,
regionType=None,
smoothTimes=False,
model='full'):
# month: string of integer 1-12 symbolising month
# vehicle: vehicle object
# regionType (opt): string filtering for a specific region type
# region (opt): string filtering for a specific region
if car == None or car == 'nissanLeafS':
car = Vehicle(1647.7, 29.97, 0.0713, 0.02206, 0.84, 24.0)
elif car == 'nissanLeafSL':
car = Vehicle(1647.7, 0, 29.61, 0.0738, 0.02195, 0.86, 30.0)
elif car == 'nissanLeafSV':
car = Vehicle(1704.5, 29.92, 0.076, 0.02195, 0.847, 30.0)
elif car == 'bmwI3':
car = Vehicle(1420.4, 22.9, 0.346, 0.01626, 0.849, 18.8)
elif car == 'teslaS60D':
car = Vehicle(2272.7, 37.37, 0.1842, 0.01508, 0.969, 60.0)
elif car == 'teslaS60R':
car = Vehicle(2272.7, 40.35, 0.1324, 0.01557, 0.884, 60.0)
elif car == 'teslaS70D':
car = Vehicle(2272.7, 36.23, 0.1906, 0.01746, 0.865, 70.0)
elif car == 'teslaS75D':
car = Vehicle(2272.7, 37.37, 0.1842, 0.01508, 0.964, 75.0)
elif car == 'teslaS75R':
car = Vehicle(2272.7, 40.35, 0.1324, 0.01557, 0.943, 75.0)
elif car == 'teslaS85D':
car = Vehicle(2272.7, 36.23, 0.1906, 0.01746, 0.86, 85.0)
elif car == 'teslaS90D':
car = Vehicle(2272.7, 39.24, 0.1493, 0.01514, 0.952, 90.0)
elif car == 'teslaSP100D':
car = Vehicle(2386.4, 41.35, 0.267, 0.0137, 0.956, 100.0)
elif car == 'teslaP85D':
car = Vehicle(2386.4, 41.91, 0.1389, 0.0185, 0.812, 85.0)
elif car == 'teslaSP90D':
car = Vehicle(2386.4, 41.51, 0.2226, 0.01403, 0.939, 90.0)
elif car == 'teslaX60D':
car = Vehicle(2500.0, 37.68, 0.0486, 0.0214, 0.953, 60.0)
elif car == 'teslaX75D':
car = Vehicle(2500.0, 37.68, 0.0486, 0.0214, 0.957, 75.0)
elif car == 'teslaX90D':
car = Vehicle(2500.0, 37.68, 0.0486, 0.0214, 0.931, 90.0)
elif car == 'teslaXP10D':
car = Vehicle(2727.3, 45.71, -0.0555, 0.0216, 0.928, 100.0)
elif car == 'BYDe6':
car = Vehicle(2500, 69.473, 0.0697, 0.02814, 0.911, 61.0)
elif car == 'chevroletSpark':
car = Vehicle(1420.45, 21.96, 0.1688, 0.01806, 0.78, 19.0)
elif car == 'fiat500e':
car = Vehicle(1477.3, 24.91, 0.2365, 0.01816, 0.79, 22.0)
elif car == 'toyotaScionIQ':
car = Vehicle(1250, 15.993, 0.56499, 0.013095, 0.844, 12.0)
elif car == 'toyotaRAV4':
car = Vehicle(1931.8, 32.246, 0.27335, 0.022058, 0.721, 41.8)
elif car == 'VWeGolf':
car = Vehicle(1647.7, 39.36, 0.5083, 0.0125, 0.942, 24.2)
elif car == 'kiaSoul':
car = Vehicle(1647.7, 22.058, 0.25763, 0.022168, 0.881, 27.0)
elif car == 'coda':
car = Vehicle(1818.2, 39.18, 0.2549, 0.0199, 0.635, 31.0)
elif car == 'mercedesBclass':
car = Vehicle(1931.8, 31.7, 0.177, 0.019, 0.681, 36.0)
elif car == 'mercedesSmart':
car = Vehicle(1079.5, 32.869, -0.1639, 0.028583, 0.786, 17.6)
elif car == 'hondaFit':
car = Vehicle(1647.7, 19.06, 0.407, 0.01499, 0.813, 20.0)
elif car == 'mitsubishiMiEV':
car = Vehicle(1306.8, 19.484, 0.43515, 0.016133, 0.752, 16.0)
else:
raise Exception('i do not recongnise that vehicle')
self.month = month
self.fleetSize = fleetSize
self.car = car
self.region = region
self.regionType = regionType
self.chargingEfficiency = 0.9
self.nVehicles = 0
# first getting the region types
self.reg1 = {} # 1:urban, 2:rural, 3:scotland
if regionType is not None:
self.reg2 = {} # 1:uc, 2:ut, 3:rt, 4:rv, 5:scotland
if region is not None:
self.reg3 = {
} # 1:NE, 2:NW, 3:Y+H, 4:EM, 5:WM, 6:E, 7:L, 8:SE, 9:SW,
# 10: Wales, 11: Scotland
if average == False:
vehicleList = []
# setting up counters which will be used to scale predictions
self.nVehicles = 0
self.nHouseholds = 0
self.journeyLogs = {}
self.demand = [0.0] * 1440 * 7
with open(households, 'rU') as csvfile:
reader = csv.reader(csvfile, delimiter='\t')
next(reader)
for row in reader:
if month is not None:
if row[9] != month: # skip households from the wrong month
continue
if row[0] not in self.reg1:
self.reg1[row[0]] = row[148]
if regionType is not None:
self.reg2[row[0]] = row[149]
if region is not None:
self.reg3[row[0]] = row[28]
with open(trips, 'rU') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
if month is not None:
if row[6] != self.month:
continue
if self.regionType is not None:
if self.reg2[row[1]] != self.regionType:
continue
if self.region is not None:
if self.reg3[row[1]] != self.region:
continue
vehicle = row[2]
if vehicle == ' ': # skip trips where the vehicle is missing
continue
if vehicle not in self.journeyLogs:
self.journeyLogs[vehicle] = []
self.nVehicles += 1
if average == False:
vehicleList.append(vehicle)
day = int(row[5]) - 1
try:
passengers = int(row[13]) # find the # people in the car
except:
passengers = 1 # if missing assume only the driver
try:
tripEnd = int(row[9])
tripStart = int(row[8])
tripDistance = float(row[10]) * 1609.34 # miles -> m
purposeTo = int(row[12])
except:
continue # skip trips without a time or length
if smoothTimes == True:
shift = 30 * random.random()
tripEnd = int(30 * int(tripEnd / 30) + shift)
tripStart = int(30 * int(tripStart / 30) + shift)
if model == 'full':
# if the trip is really long, run the motorway artemis
if tripDistance > 30000:
cycle = Drivecycle(tripDistance, 'motorway')
# otherwise run the rural/urban depending on the location
elif self.reg1[row[1]] == '1':
cycle = Drivecycle(tripDistance, 'rural')
elif self.reg1[row[1]] == '2':
cycle = Drivecycle(tripDistance, 'urban')
else:
# not really sure what to do here..?
#continue
cycle = Drivecycle(tripDistance, 'urban')
accessoryLoad = {
'1': 1.5,
'2': 1.3,
'3': 0.8,
'4': 0.4,
'5': 0.1,
'6': 0.0,
'7': 0.0,
'8': 0.0,
'9': 0.0,
'10': 0.2,
'11': 0.7,
'12': 1.3
}
car.load = passengers * 75 # add appropriate load to vehicle
energyConsumption = car.getEnergyExpenditure(
cycle, accessoryLoad[row[6]])
car.load = 0
elif model == 'linear':
energyConsumption += tripDistance * 0.23 / 1609.34
if tripStart == tripEnd:
tripEnd += 30
if tripStart > tripEnd:
tripEnd += 1440
tripStart += day * 1440
tripEnd += day * 1440
self.journeyLogs[vehicle].append(
[tripStart, tripEnd, energyConsumption, purposeTo])
# now scale for fleetsize
if average == True or fleetSize > self.nVehicles:
self.sf = fleetSize / self.nVehicles
for vehicle in self.journeyLogs:
for journey in self.journeyLogs[vehicle]:
journey[2] = journey[2] * self.sf
else:
if fleetSize < self.nVehicles:
self.sf = 1
chosenVehicles = []
while len(chosenVehicles) < fleetSize:
ran = int(random.random() * len(vehicleList))
if vehicleList[ran] not in chosenVehicles:
chosenVehicles.append(vehicleList[ran])
newLogs = {}
for vehicle in chosenVehicles:
newLogs[vehicle] = self.journeyLogs[vehicle]
self.journeyLogs = newLogs
for vehicle in self.journeyLogs:
self.journeyLogs[vehicle] = sorted(self.journeyLogs[vehicle])
accessoryLoad = {
'1': 1.5,
'2': 1.3,
'3': 0.8,
'4': 0.4,
'5': 0.1,
'6': 0.0,
'7': 0.0,
'8': 0.0,
'9': 0.0,
'10': 0.2,
'11': 0.7,
'12': 1.3
}
tesla = Vehicle(2273.0, 37.37, 0.1842, 0.01508, 0.94957, 60.0)
tesla.load = 80.0
chargePower = [3.5, 7.0]
uCycle = Drivecycle(10000, 'urban')
mCycle = Drivecycle(10000, 'motorway')
uEnergy = tesla.getEnergyExpenditure(uCycle, 0.1) / 10000
mEnergy = tesla.getEnergyExpenditure(mCycle, 0.1) / 10000
with open('../../Documents/JLRCompanyCars/trips_useful.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
def __init__(self, month, fleetSize, car=None, region=None,average=True,
regionType=None, smoothTimes=False,model='full'):
# month: string of integer 1-12 symbolising month
# vehicle: vehicle object
# regionType (opt): string filtering for a specific region type
# region (opt): string filtering for a specific region
if car == None:
nissanLeaf = Vehicle(1521.0,29.92,0.076,0.02195,0.86035,24.0)
car = nissanLeaf
elif car == 'tesla':
car = Vehicle(2273.0,37.37,0.1842,0.01508,0.94957,60.0)
elif car == 'bmw':
car = Vehicle(1420.0,22.9,0.346,0.01626,0.87785,22.0)
self.month = month
self.fleetSize = fleetSize
self.car = car
self.region = region
self.regionType = regionType
self.nVehicles = 0
# first getting the region types
self.reg1 = {} # 1:urban, 2:rural, 3:scotland
if regionType is not None:
self.reg2 = {} # 1:uc, 2:ut, 3:rt, 4:rv, 5:scotland
if region is not None:
self.reg3 = {} # 1:NE, 2:NW, 3:Y+H, 4:EM, 5:WM, 6:E, 7:L, 8:SE, 9:SW,
# 10: Wales, 11: Scotland
if average == False:
vehicleList = []
# setting up counters which will be used to scale predictions
self.nVehicles = 0
self.nHouseholds = 0
self.profiles = {}
self.demand = [0.0]*1440*7
with open(households,'rU') as csvfile:
reader = csv.reader(csvfile,delimiter='\t')
next(reader)
for row in reader:
if month is not None:
if row[9] != month: # skip households from the wrong month
continue
if row[0] not in self.reg1:
self.reg1[row[0]] = row[148]
if regionType is not None:
self.reg2[row[0]] = row[149]
if region is not None:
self.reg3[row[0]] = row[28]
with open(trips,'rU') as csvfile:
reader = csv.reader(csvfile)
next(reader)
for row in reader:
if month is not None:
if row[6] != self.month:
continue
if self.regionType is not None:
if self.reg2[row[1]] != self.regionType:
continue
if self.region is not None:
if self.reg3[row[1]] != self.region:
continue
vehicle = row[2]
if vehicle == ' ': # skip trips where the vehicle is missing
continue
if vehicle not in self.profiles:
self.profiles[vehicle] = [0.0]*(1440*7)
self.nVehicles += 1
self.journeyLogs[vehicle] = []
if average == False:
vehicleList.append(vehicle)
day = int(row[5])-1
try:
passengers = int(row[13]) # find the # people in the car
except:
passengers = 1 # if missing assume only the driver
try:
tripEnd = int(row[9])
tripStart = int(row[8])
tripDistance = float(row[10])*1609.34 # miles -> m
except:
continue # skip trips without a time or length
if smoothTimes == True:
shift = 30*random.random()
tripEnd = int(30*int(tripEnd/30)+shift)
tripStart = int(30*int(tripStart/30)+shift)
if model == 'full':
# if the trip is really long, run the motorway artemis
if tripDistance > 30000:
cycle = Drivecycle(tripDistance,'motorway')
# otherwise run the rural/urban depending on the location
elif self.reg1[row[1]] == '1':
cycle = Drivecycle(tripDistance,'rural')
elif self.reg1[row[1]] == '2':
cycle = Drivecycle(tripDistance,'urban')
else:
# not really sure what to do here..?
#continue
cycle = Drivecycle(tripDistance,'urban')
accessoryLoad = {'1':1.5,'2':1.3,'3':0.8,'4':0.4,'5':0.1,
'6':0.0,'7':0.0,'8':0.0,'9':0.0,'10':0.2,
'11':0.7,'12':1.3}
car.load = passengers*75 # add appropriate load to vehicle
energyConsumption = car.getEnergyExpenditure(cycle,
accessoryLoad[row[6]])
car.load = 0
elif model == 'linear':
energyConsumption += tripDistance*0.23/1609.34
if tripStart == tripEnd:
tripEnd += 30
if tripStart > tripEnd:
tripEnd += 1440
tripLen = tripEnd-tripStart
enPerMin = energyConsumption/tripLen
for i in range(tripStart,tripEnd):
if day*1440+i < 1440*7:
self.profiles[vehicle][day*1440+i] += enPerMin
else:
self.profiles[vehicle][(day-7)*1440+i] += enPerMin
# now scale for fleetsize
if average == True or fleetSize > self.nVehicles:
self.sf = fleetSize/self.nVehicles
for vehicle in self.profiles:
for i in range(len(self.profiles[vehicle])):
self.profiles[vehicle][i] = self.profiles[vehicle][i]*self.sf
else:
if fleetSize < self.nVehicles:
self.sf = 1
chosenVehicles = []
while len(chosenVehicles) < fleetSize:
ran = int(random.random()*len(vehicleList))
if vehicleList[ran] not in chosenVehicles:
chosenVehicles.append(vehicleList[ran])
newProfiles = {}
for vehicle in chosenVehicles:
newProfiles[vehicle] = self.profiles[vehicle]
self.profiles = newProfiles
# packages
import matplotlib.pyplot as plt
# 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],
def __init__(self,
regionType,
month,
day,
population,
fleetCode,
f=1,
region='',
supressText=False):
# fleetCode is a code to determine the fleet composition
# 0 -> all nissanLeaf
# 2 -> all mitsuibishi (for ENWL comparison)
self.regionType = regionType
self.factor = f * population / 1000
population = 1000 / f
# let's work out how many agents and journeys we're going to want
journeysPerPerson = 0
carsPerPerson = 0
with open('nts-data/number.csv', 'rU') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if row['day'] == day:
if row['month'] == month:
if row['region'] == regionType:
journeysPerPerson = float(row['number'])
if journeysPerPerson == 0:
raise Error('data for that region type / day / month not found')
self.numberJourneys = int(journeysPerPerson * population / 2)
# added divisor as generating journeys in pairs
if region == '':
region = 'United Kingdom'
with open('nts-data/vehiclesPerHead.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if row[0] == region:
carsPerPerson = float(row[1])
if carsPerPerson == 0:
raise Error('are you sure that is a valid region?')
self.numberAgents = int(carsPerPerson * population)
nissanLeaf = Vehicle(1705.0, 29.92, 0.076, 0.02195, 0.86035, 32.0)
bmwI3 = Vehicle(1420.0, 22.9, 0.346, 0.01626, 0.87785, 22.0)
teslaS60D = Vehicle(2273.0, 37.37, 0.1842, 0.01508, 0.94957, 60.0)
fiat500e = Vehicle(1477.0, 24.91, 0.2365, 0.01816, 0.80955, 24.0)
mitsubishi = Vehicle(1307.0, 19.484, 0.43515, 0.016133, 0.77805, 16.0)
# First we need to generate our fleet of vehicles
self.fleet = Fleet()
for k in range(0, self.numberAgents):
if fleetCode == 0:
agent = Agent(str(k), nissanLeaf, regionType, month)
elif fleetCode == 1:
ran = random.random()
if ran < 0.391:
agent = Agent(str(k), fiat500e, regionType, month)
elif ran < 0.652:
agent = Agent(str(k), nissanLeaf, regionType, month)
elif ran < 0.745:
agent = Agent(str(k), bmwI3, regionType, month)
else:
agent = Agent(str(k), teslaS60D, regionType, month)
elif fleetCode == 2:
agent = Agent(str(k), mitsubishi, regionType, month)
else:
print fleetCode
raise Exception('please check the fleet code')
self.fleet.addAgent(agent)
if supressText == False:
print str(self.numberAgents
) + ' agents were initialised, each representing ',
print str(self.factor) + ' vehicles'
# Then we need to generate the pool of journeys
pool = JourneyPool(day, month, regionType)
for k in range(0, self.numberJourneys):
pool.addJourney()
if supressText == False:
print str(self.numberJourneys) + ' journeys were generated'
assignmentComplete = False
while assignmentComplete == False:
try:
pickedOutJourneys = []
if supressText == False:
print 'now assigning journeys'
print 'PROGRESS:',
# Now we need to assign the journeys to vehicles in the fleet
for k in range(0, self.numberJourneys):
if supressText == False:
if self.numberJourneys < 33:
print 'X',
elif k % (self.numberJourneys / 33) == 0:
print 'X',
journey = pool.pickOutJourney()
pickedOutJourneys.append(journey)
agent = self.fleet.pickAvaliableAgent(
journey[1], journey[2])
agent.addJourney(journey)
if supressText == False:
print ''
print 'All journeys assigned!'
assignmentComplete = True
except NoAvaliableAgents:
print 'Damn it! re running this one'
self.fleet.reset()
for trip in pickedOutJourneys:
pool.returnJourney(trip)
print 'returned ' + str(trip[0])
continue
# Sort the energy logs into chronological order
self.fleet.sortFleetEnergyLogs()
def __init__(self, regionType, month, population, fleetCode, region=''):
# fleetCode is a code to determine the fleet composition
# 0 -> all nissanLeaf
# 2 -> all mitsuibishi (for ENWL comparison)
self.regionType = regionType
self.factor = population / 1000
population = 1000
# let's work out how many agents and journeys we're going to want
# added divisor as generating journeys in pairs
if region == '':
region = 'United Kingdom'
carsPerPerson = 0
with open('vehiclesPerHead.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
if row[0] == region:
carsPerPerson = float(row[1])
if carsPerPerson == 0:
raise Error('are you sure that is a valid region?')
self.numberAgents = int(carsPerPerson * population)
nissanLeaf = Vehicle(1705, 29.92, 0.076, 0.02195, 0.86035, 32)
bmwI3 = Vehicle(1420, 22.9, 0.346, 0.01626, 0.87785, 22)
teslaS60D = Vehicle(2273, 37.37, 0.1842, 0.01508, 0.94957, 60)
fiat500e = Vehicle(1477, 24.91, 0.2365, 0.01816, 0.80955, 24)
mitsubishi = Vehicle(1307, 19.484, 0.43515, 0.016133, 0.77805, 16)
# First we need to generate our fleet of vehicles
self.fleet = Fleet()
for k in range(0, self.numberAgents):
if fleetCode == 0:
agent = WeekAgent(str(k), nissanLeaf, regionType)
elif fleetCode == 2:
agent = WeekAgent(str(k), mitsubishi, regionType)
else:
raise Exception('please check the fleet code')
self.fleet.addAgent(agent)
print str(self.numberAgents
) + ' agents were initialised, each representing ',
print str(self.factor) + ' vehicles'
days = [
'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',
'Saturday'
]
for i in range(0, 7):
day = days[i]
journeysPerPerson = 0
with open('number.csv', 'rU') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if row['day'] == day:
if row['month'] == month:
if row['region'] == regionType:
journeysPerPerson = float(row['number'])
if journeysPerPerson == 0:
raise Error(
'data for that region type / day / month not found')
numberJourneys = int(journeysPerPerson * population / 2)
# Then we need to generate the pool of journeys
pool = JourneyPool(day, month, regionType)
for k in range(0, numberJourneys):
pool.addJourney()
print 'on ' + str(day) + ' ' + str(
numberJourneys) + ' journeys were generated'
print 'now assigning journeys'
print 'PROGRESS:',
# Now we need to assign the journeys to vehicles in the fleet
for k in range(0, numberJourneys):
if k % (numberJourneys / 33) == 0:
print 'X',
journey = pool.pickOutJourney()
agent = self.fleet.pickAvaliableAgent(journey[1] + i * 24 * 60,
journey[2] + i * 24 * 60)
agent.addJourney(journey, i)
print ''
print 'All journeys assigned!'
# Sort the energy logs into chronological order
self.fleet.sortFleetEnergyLogs()