def test0(): # test shifting
testing_ = 1
threshold = 6
optigoal = "Bill"
shifting_ = 0
c_load = 0
meter = 1
min_interval = 60
csv_directory = "C:\Users\Bat Cave\Dropbox\My Files\Raw Data\house2"
csv = "consumption.csv"
csvpath = str(csv_directory) + "\\" + str(csv)
housedata = ida.aggregatedatacsv(csvpath, min_interval)
demand_ = housedata[1]
occupants = [1, 1, 1, 1, 1, 1]
routine_ = rb.routine_builder(demand_, min_interval, occupants)
bill_type = 2
bill_number = 1
if optigoal == "Bill" and bill_type == 2: # if optimising for bill and eval at ToU bill
shifting_ = 1 # enable appliance shifting
all_billing = be.bill_eval(demand_, bill_type, bill_number, min_interval, c_load, meter, 0)
cost_ = all_billing[1]
expected_ = []
appliancedemand = ida.deviceimport(housedata, min_interval, csv_directory, "No", "Yes", "No", "No", "No", "No",
"No", "No", "No", "No", "No", "No", "No", "No")
device_demand = appliancedemand[0][1]
variation = clothesdryer(device_demand, routine_, min_interval, cost_, threshold, shifting_, testing_)
print " Variation: " + str(variation)
if variation == expected_:
print "Pass test 0: Testing device optimisation using minimisation\n"
else:
print " Expected: " + str(expected_)
print "***** Fail ***** test 0: Testing device optimisation using minimisation\n"
def test0(): # test shifting
testing_ = 1
threshold = 6
optigoal = "Bill"
shifting_ = 0
c_load = 0
meter = 1
min_interval = 15
csv_directory = "C:\Users\Bat Cave\Dropbox\My Files\Raw Data\house2"
csv = "consumption.csv"
csvpath = str(csv_directory) + "\\" + str(csv)
housedata = ida.aggregatedatacsv(csvpath, min_interval)
demand_ = housedata[1]
occupants = [1, 1, 1, 1, 1, 1]
routine_ = rb.routine_builder(demand_, min_interval, occupants)
bill_type = 2
bill_number = 1
if optigoal == "Bill" and bill_type == 2: # if optimising for bill and eval at ToU bill
shifting_ = 1 # enable appliance shifting
all_billing = be.bill_eval(demand_, bill_type, bill_number, min_interval, c_load, meter, 0)
cost_ = all_billing[1]
expected_ = []
devicedemand = ida.deviceimport(housedata, min_interval, csv_directory, "No", "No", "No", "No", "No", "No",
"Yes", "No", "No", "No", "No", "No", "No", "No")
device_demand = devicedemand[0][6]
variation = secondfridge(device_demand, routine_, cost_, threshold, testing_)
print "\n Variation: " + str(variation)
if variation == expected_:
print "Pass test 0: Testing device optimisation using minimisation\n"
else:
print " Expected: " + str(expected_)
print "***** Fail ***** test 0: Testing device optimisation using minimisation\n"
def test1():
optigoal = "Bill"
dataid = 4922
meter = 1
shifting_ = 1
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Uni Work\Capstone\PeaconStreet\dataid_" + str(
dataid) + "_localdate_07012013_AND_09302013.csv"
min_interval = 15
df = ida.peacon_street(csvpath, min_interval)
houselevel_data = []
for row in df[1][0]:
houselevel_data.append(row)
houselevel_cum_sum = np.cumsum(houselevel_data)
plt.plot(houselevel_cum_sum, label='Unoptimised Household Demand')
print "Total house level consumption = " + str(
np.around(houselevel_cum_sum[-1], 2)) + " kWh"
c_load = [0 for zero in range(0, len(houselevel_data))
] # zero value matrix for controlled demand
occupants = [2, 0, 0, 0, 0, 0]
routine_ = rb.routine_builder(houselevel_data, min_interval, occupants)
bill_type = 2
cost_number = 54
billing_info = be.bill_eval(houselevel_data, bill_type, cost_number,
min_interval, c_load, meter, 0)
house_cost = be.billing_calc(houselevel_data, c_load, bill_type,
billing_info[0], billing_info[1],
billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(house_cost), 2))
device_data = []
for row in df[1][1]:
device_data.append(row)
skiplist = []
count = 0
for x in np.arange(0, len(device_data)):
if not x in skiplist:
if device_data[x] > 0:
count += 1
for z in np.arange(x, len(device_data)):
skiplist.append(z)
if device_data[z] == 0:
break
original_cum_sum = np.cumsum(device_data)
plt.plot(original_cum_sum, label='Unoptimised Device Demand')
device_per = np.around(
np.divide(original_cum_sum[-1], houselevel_cum_sum[-1]) * 100, 2)
device_cost = be.billing_calc(device_data, c_load, bill_type, 0,
billing_info[1], billing_info[2],
billing_info[3])
print "Device accounts for " + str(
device_per) + "% of household consumption"
print "A total of " + str(np.around(original_cum_sum[-1], 2)) + " kWh"
print "The device costings a total of $ " + str(
np.around(sum(device_cost), 2))
print "Occuring in " + str(count) + " blocks"
for i in np.arange(7, 8):
hh_threshold = i
if hh_threshold >= 6:
print "\nThreshold number = " + str(hh_threshold)
vari = airconditioner(device_data, routine_, min_interval,
billing_info[1], hh_threshold, shifting_, 0)
print(np.sum(vari))
var_cum_sum = np.cumsum(vari)
optimised_cum_sum = original_cum_sum + var_cum_sum
if hh_threshold >= 1000:
plt.plot(optimised_cum_sum, label='Motivation Level = %i' % i)
per_saved = np.around(
100 - np.divide(np.max(optimised_cum_sum),
np.max(original_cum_sum)) * 100, 2)
print "Percentage Saved = " + str(
per_saved) + "% & kWh saved = " + str(
np.around((original_cum_sum[-1] - optimised_cum_sum[-1]),
2)) + " kWh"
device_per = np.around(
np.divide(optimised_cum_sum[-1],
houselevel_cum_sum[-1] + var_cum_sum[-1]) * 100, 2)
optimised_device_data = [a + b for a, b in zip(device_data, vari)]
opti_device_cost = be.billing_calc(optimised_device_data, c_load,
bill_type, 0, billing_info[1],
billing_info[2],
billing_info[3])
print "Saving a total of $ " + str(
np.around(sum(device_cost) - sum(opti_device_cost), 2))
print "Device accounts for " + str(
device_per) + "% of household consumption"
house_per_save = np.around(
100 - np.divide(houselevel_cum_sum[-1] + var_cum_sum[-1],
houselevel_cum_sum[-1]) * 100, 2)
print "Comparitive house level saving " + str(house_per_save) + "%"
print "Optimised house level consumption " + str(
np.around(houselevel_cum_sum[-1] + var_cum_sum[-1],
2)) + " kWh"
optimised_house_level = [
a + b for a, b in zip(houselevel_data, vari)
]
houselevel_cost = be.billing_calc(optimised_house_level, c_load,
bill_type, 0, billing_info[1],
billing_info[2], billing_info[3])
print "Costing a total of $ " + str(
np.around(sum(houselevel_cost), 2))
def test11(): # real kettle data treated as a cycle
optigoal = "Bill"
testing_ = 1
shifting_ = 0
min_interval = 60
threshold = 1
meal_appliance = 0
c_load = 0
meter = 1
device_type = "Cycle"
elimthres = 7
minithres = 6
minisaving = 20
minicyclemax = 1
minitime = 3600
shiftthres = 5
shifttime = 180
subthres = 4
subtype = "Cycle"
subtime = 45
subdemand = 1000
subsaving = 15
csv = "sound-system.csv"
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Raw Data\house" + "\\" + str(
csv)
data = ida.aggregatedatacsv(csvpath, min_interval)
demand_ = data[1]
occupants = [1, 1, 1, 1, 1, 1]
routine_ = rb.routine_builder(demand_, min_interval, occupants)
bill_type = 2
bill_number = 1
if optigoal == "Bill" and bill_type == 2: # if optimising for bill and eval at ToU bill
shifting_ = 1 # enable appliance shifting
all_billing = be.bill_eval(demand_, bill_type, bill_number, min_interval,
0, c_load, meter)
cost_ = all_billing[1]
expected_ = [[]]
expected_saving = [[]]
print "** Test 11 ** " + str(csv)
variation = []
for q in np.arange(0, 1):
variation.append(
testsettings(demand_, routine_, min_interval, cost_, threshold,
shifting_, testing_, meal_appliance, device_type,
elimthres, minithres, minisaving, minicyclemax,
minitime, shiftthres, shifttime, subthres, subtype,
subtime, subdemand, subsaving))
vary_calc = [] # define variation calculation as a list
max_saving = [] # define maximum savings as a list
for m in np.arange(0, len(variation)): # for all the different variations
vary_calc.append(
sum(variation[m]
)) # sum all the optimisation changes and add to a list
min_variation_index = [
i for i, x in enumerate(vary_calc) if x == min(vary_calc)
] # find index of greatest saving
for y in np.arange(
0, len(min_variation_index
)): # for all scenerios where the greatest saving is made
max_saving.append(
variation[min_variation_index[y]]) # add the variations to a list
print " Intervals optimised " + str(np.count_nonzero(max_saving[0]))
print " Variations: " + str(variation)
print " Saving Variation: " + str(
max_saving) # print the max saving variation pattern list
if variation == expected_ and max_saving == expected_saving:
print "Pass test 11: real data \n"
else:
print " Expected: " + str(expected_)
print " Expected saving: " + str(expected_saving)
print "***** Fail ***** test 11: real data\n"
def test10(): # real kettle data treated as a cycle
optigoal = "Bill"
testing_ = 1
shifting_ = 0
min_interval = 180
c_load = 0
meter = 1
threshold = 6
csv = "74-kettle-101-consumption.csv"
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Raw Data\h1_150120\h1" + "\\" + "141229_150104_50_h1" + "\\" \
+ str(csv)
kettledata = ida.aggregatedatacsv(csvpath, min_interval)
demand_ = kettledata[1]
occupants = [1, 1, 1, 1, 1, 1]
routine_ = rb.routine_builder(demand_, min_interval, occupants)
bill_type = 1
bill_number = 1
if optigoal == "Bill" and bill_type == 3: # if optimising for bill and eval at ToU bill
shifting_ = 1 # enable appliance shifting
cost_ = be.bill_eval(demand_, bill_type, bill_number, min_interval, 0,
c_load, meter)
expected_ = [[
0.0, 0.0, 0.0, -68021.64893168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-74762.03088144002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -96389.143949120044,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -160431.63805943992, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -75922.154274079978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, -103159.85328527998, -68490.919669680006, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, -58786.738587680011, 0.0, 0.0, 0.0, 0.0, 0.0
]]
expected_saving = [[
0.0, 0.0, 0.0, -68021.64893168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-74762.03088144002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -96389.143949120044,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -160431.63805943992, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -75922.154274079978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, -103159.85328527998, -68490.919669680006, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, -58786.738587680011, 0.0, 0.0, 0.0, 0.0, 0.0
]]
meal_appliance = 0
device_type = "Cycle"
elimthres = 7
minithres = 6
minisaving = 20
minicyclemax = 1
minitime = 3600
shiftthres = 5
shifttime = 60
subthres = 4
subtype = "Cycle"
subtime = 45
subdemand = 1000
subsaving = 15
variation = []
print "** Test 10 **"
for q in np.arange(0, 1):
variation.append(
testsettings(demand_, routine_, min_interval, cost_, threshold,
shifting_, testing_, meal_appliance, device_type,
elimthres, minithres, minisaving, minicyclemax,
minitime, shiftthres, shifttime, subthres, subtype,
subtime, subdemand, subsaving))
print " Variations: " + str(variation)
vary_calc = [] # define variation calculation as a list
max_saving = [] # define maximum savings as a list
for m in np.arange(0, len(variation)): # for all the different variations
vary_calc.append(
sum(variation[m]
)) # sum all the optimisation changes and add to a list
min_variation_index = [
i for i, x in enumerate(vary_calc) if x == min(vary_calc)
] # find index of greatest saving
for y in np.arange(
0, len(min_variation_index
)): # for all scenerios where the greatest saving is made
max_saving.append(
variation[min_variation_index[y]]) # add the variations to a list
print " Saving Variation: " + str(
max_saving) # print the max saving variation pattern list
print " Intervals optimised " + str(np.count_nonzero(max_saving[0]))
if variation == expected_ and max_saving == expected_saving:
print "Pass test 10: real data \n"
else:
print " Expected: " + str(expected_)
print " Expected saving: " + str(expected_saving)
print "***** Fail ***** test 10: real data\n"
def test1():
optigoal = "Bill"
dataid = 4922
meter = 1
shifting_ = 1
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Uni Work\Capstone\PeaconStreet\dataid_" + str(
dataid) + "_localdate_07012013_AND_09302013.csv"
min_interval = 15
df = ida.peacon_street(csvpath, min_interval)
houselevel_data = []
for row in df[1][0]:
houselevel_data.append(row)
houselevel_cum_sum = np.cumsum(houselevel_data)
print "Total house level consumption = " + str(
np.around(houselevel_cum_sum[-1], 2)) + " kWh"
c_load = [0 for zero in range(0, len(houselevel_data))
] # zero value matrix for controlled demand
occupants = [2, 0, 0, 0, 0, 0]
routine_ = rb.routine_builder(houselevel_data, min_interval, occupants)
bill_type = 2
cost_number = 54
billing_info = be.bill_eval(houselevel_data, bill_type, cost_number,
min_interval, c_load, meter, 0)
house_cost = be.billing_calc(houselevel_data, c_load, bill_type,
billing_info[0], billing_info[1],
billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(house_cost), 2))
device_data = []
for row in df[1][7]: # <<<<----- Edit device number here
device_data.append(row)
skiplist = []
count = 0
for x in np.arange(0, len(device_data)):
if not x in skiplist:
if device_data[x] > 0:
count += 1
for z in np.arange(x, len(device_data)):
skiplist.append(z)
if device_data[z] == 0:
break
original_cum_sum = np.cumsum(device_data)
plt.plot(original_cum_sum, label='Unoptimised Device Demand')
device_per = np.around(
np.divide(original_cum_sum[-1], houselevel_cum_sum[-1]) * 100, 2)
device_cost = be.billing_calc(device_data, c_load, bill_type, 0,
billing_info[1], billing_info[2],
billing_info[3])
print "Device accounts for " + str(
device_per) + "% of household consumption"
print "A total of " + str(np.around(original_cum_sum[-1], 2)) + " kWh"
print "The device costings a total of $ " + str(
np.around(sum(device_cost), 2))
print "Occuring in " + str(count) + " blocks \n"
for i in np.arange(7, 8):
hh_threshold = i
if hh_threshold >= 7:
print "\nThreshold number = " + str(hh_threshold)
vari = clotheswasher(device_data, routine_, min_interval,
billing_info[1], hh_threshold, shifting_, 0)
var_cum_sum = np.cumsum(vari)
optimised_cum_sum = original_cum_sum + var_cum_sum
if hh_threshold >= 7:
plt.plot(optimised_cum_sum, label='Motivation Level = %i' % i)
per_saved = np.around(
100 - np.divide(np.max(optimised_cum_sum),
np.max(original_cum_sum)) * 100, 2)
print "Percentage Saved = " + str(
per_saved) + "% & kWh saved = " + str(
np.around((original_cum_sum[-1] - optimised_cum_sum[-1]),
2)) + " kWh"
device_per = np.around(
np.divide(optimised_cum_sum[-1],
houselevel_cum_sum[-1] + var_cum_sum[-1]) * 100, 2)
optimised_device_data = [a + b for a, b in zip(device_data, vari)]
opti_device_cost = be.billing_calc(optimised_device_data, c_load,
bill_type, 0, billing_info[1],
billing_info[2],
billing_info[3])
print "Saving a total of $ " + str(
np.around(sum(device_cost) - sum(opti_device_cost), 2))
print "Device accounts for " + str(
device_per) + "% of household consumption"
house_per_save = np.around(
100 - np.divide(houselevel_cum_sum[-1] + var_cum_sum[-1],
houselevel_cum_sum[-1]) * 100, 2)
print "Comparitive house level saving " + str(house_per_save) + "%"
print "Optimised house level consumption " + str(
np.around(houselevel_cum_sum[-1] + var_cum_sum[-1],
2)) + " kWh"
optimised_house_level = [
a + b for a, b in zip(houselevel_data, vari)
]
houselevel_cost = be.billing_calc(optimised_house_level, c_load,
bill_type, 0, billing_info[1],
billing_info[2], billing_info[3])
print "Costing a total of $ " + str(
np.around(sum(houselevel_cost), 2))
plt.legend(loc="best", fontsize='medium')
w = len(device_data)
plt.xlabel('Time Intervals (From 01/07/2013 [0] - 01/09/2013 [' + str(w) +
'])')
plt.ylabel('Kilowatt hours of Electricity (kWh)')
plt.title('Impact of Behavioural Change on Clothes Washer Use (Data ID:' +
str(dataid) + ")")
plt.show()
)
# End of demand aggregator
# Start of routine builder
if household_makeup == "Custom" or household_makeup == "custom": # if exact situation needs to be specified
occupant = [0 for i in range(0, 6)]
occupant[0] = 2 # number of occupants with full time jobs
occupant[1] = 0 # number of occupants with part time jobs
occupant[2] = 0 # number of occupants with casual time jobs
occupant[3] = 0 # number of occupants that are unemployed
occupant[4] = 0 # number of occupants that are retired
occupant[5] = 0 # number of children at school
for z in np.arange(0, len(occupant)): # loop through occupant list
if occupant[z] > 1: # if more then one occupant has the same job type
occupant[z] = 1 # just enter 1, system treats it the same
routinelist.append(rb.routine_builder(house_demand, mininterval, occupant))
# at to list of routines to be evaluated
else:
routinelist = rb.routineselect(house_demand, mininterval, household_makeup)
# returns list of all routines that match criteria
routineloop = 0 # counts how many routines that are evaluated
for routine in routinelist: # looping through all applicable routines
routineloop += 1 # add one to routine counter
# End of routine builder
# Start household type
householdcount = 0 # counts how many household loops are completed
for xyz in np.arange(min_loop, max_loop): # for each value between range
if household_eval == "loop" or household_eval == "specific":
threshold = xyz
else:
threshold = ht.householdthreshold(
def test1():
optigoal = "Bill"
dataid = 4922
meter = 1
shifting_ = 1
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Uni Work\Capstone\PeaconStreet\dataid_" + str(dataid) + "_localdate_07012013_AND_09302013.csv"
min_interval = 15
weather_forecast = weather(min_interval) # import weather for the period being evaluated
df = ida.peacon_street(csvpath, min_interval)
houselevel_data = []
for row in df[1][0]:
houselevel_data.append(row)
houselevel_cum_sum = np.cumsum(houselevel_data)
# plt.plot(houselevel_cum_sum, label='Unoptimised House Level Demand') # <<< --- House level demand graph
print "Total house level consumption = " + str(np.around(houselevel_cum_sum[-1], 2)) + " kWh"
c_load = [0 for zero in range(0, len(houselevel_data))] # zero value matrix for controlled demand
occupants = [2, 0, 0, 0, 0, 0]
routine_ = rb.routine_builder(houselevel_data, min_interval, occupants)
bill_type = 2
cost_number = 54
billing_info = be.bill_eval(houselevel_data, bill_type, cost_number, min_interval, c_load, meter, 0)
house_cost = be.billing_calc(houselevel_data, c_load, bill_type, billing_info[0], billing_info[1], billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(house_cost), 2))
device_data = []
for row in df[1][11]: # <<<<----- Edit device number here
device_data.append(row)
skiplist = []
count = 0
for x in np.arange(0, len(device_data)):
if not x in skiplist:
if device_data[x] > 0:
count += 1
for z in np.arange(x, len(device_data)):
skiplist.append(z)
if device_data[z] == 0:
break
original_cum_sum = np.cumsum(device_data)
plt.plot(original_cum_sum, label='Unoptimised Device Demand')
device_per = np.around(np.divide(original_cum_sum[-1], houselevel_cum_sum[-1])*100, 2)
device_cost = be.billing_calc(device_data, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Device accounts for " + str(device_per) + "% of household consumption"
print "A total of " + str(np.around(original_cum_sum[-1], 2)) + " kWh"
print "The device costings a total of $ " + str(np.around(sum(device_cost), 2))
print "Occuring in " + str(count) + " blocks"
for i in np.arange(7, 8):
hh_threshold = i
if hh_threshold >= 7:
print "\nThreshold number = " + str(hh_threshold)
vari = clothesdryer(device_data, routine_, min_interval, billing_info[1], hh_threshold, shifting_, weather_forecast, 0)
var_cum_sum = np.cumsum(vari)
optimised_cum_sum = original_cum_sum + var_cum_sum
if hh_threshold >= 7:
plt.plot(optimised_cum_sum, label='Motivation Level = %i' % i)
per_saved = np.around(100 - np.divide(np.max(optimised_cum_sum), np.max(original_cum_sum))*100, 2)
print "Percentage Saved = " + str(per_saved) + "% & kWh saved = " + str(np.around((original_cum_sum[-1]-optimised_cum_sum[-1]), 2)) + " kWh"
device_per = np.around(np.divide(optimised_cum_sum[-1], houselevel_cum_sum[-1]+var_cum_sum[-1])*100, 2)
optimised_device_data = [a + b for a, b in zip(device_data, vari)]
opti_device_cost = be.billing_calc(optimised_device_data, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Saving a total of $ " + str(np.around(sum(device_cost)-sum(opti_device_cost), 2))
print "Device accounts for " + str(device_per) + "% of household consumption"
house_per_save = np.around(100 - np.divide(houselevel_cum_sum[-1]+var_cum_sum[-1], houselevel_cum_sum[-1])*100, 2)
print "Comparitive house level saving " + str(house_per_save) + "%"
print "Optimised house level consumption " + str(np.around(houselevel_cum_sum[-1]+var_cum_sum[-1], 2)) + " kWh"
optimised_house_level = [a + b for a, b in zip(houselevel_data, vari)]
houselevel_cost = be.billing_calc(optimised_house_level, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(houselevel_cost), 2))
plt.legend(loc="best", fontsize='medium')
w = len(device_data)
plt.xlabel('Time Intervals (From 01/07/2013 [0] - 01/09/2013 [' + str(w) + '])')
plt.ylabel('Kilowatt hours of Electricity (kWh)')
plt.title('Impact of Behavioural Change on Clothes Dryer Use (Data ID:' + str(dataid) + ")")
plt.show()
sys.exit("Data Source Error 1: Please select a valid data source as either 'own' or 'pecan'")
# End of demand aggregator
# Start of routine builder
if household_makeup == "Custom" or household_makeup == "custom": # if exact situation needs to be specified
occupant = [0 for i in range(0, 6)]
occupant[0] = 2 # number of occupants with full time jobs
occupant[1] = 0 # number of occupants with part time jobs
occupant[2] = 0 # number of occupants with casual time jobs
occupant[3] = 0 # number of occupants that are unemployed
occupant[4] = 0 # number of occupants that are retired
occupant[5] = 0 # number of children at school
for z in np.arange(0, len(occupant)): # loop through occupant list
if occupant[z] > 1: # if more then one occupant has the same job type
occupant[z] = 1 # just enter 1, system treats it the same
routinelist.append(rb.routine_builder(house_demand, mininterval, occupant))
# at to list of routines to be evaluated
else:
routinelist = rb.routineselect(house_demand, mininterval, household_makeup)
# returns list of all routines that match criteria
routineloop = 0 # counts how many routines that are evaluated
for routine in routinelist: # looping through all applicable routines
routineloop += 1 # add one to routine counter
# End of routine builder
# Start household type
householdcount = 0 # counts how many household loops are completed
for xyz in np.arange(min_loop, max_loop): # for each value between range
if household_eval == "loop" or household_eval == "specific":
threshold = xyz
else:
threshold = ht.householdthreshold(xyz, household_type, optigoal) # return the threshold value
def test1():
optigoal = "Bill"
dataid = 4922
meter = 1
shifting_ = 1
csvpath = "C:\Users\Bat Cave\Dropbox\My Files\Uni Work\Capstone\PeaconStreet\dataid_" + str(dataid) + "_localdate_07012013_AND_09302013.csv"
min_interval = 15
df = ida.peacon_street(csvpath, min_interval)
houselevel_data = []
for row in df[1][0]:
houselevel_data.append(row)
houselevel_cum_sum = np.cumsum(houselevel_data)
plt.plot(houselevel_cum_sum, label='Unoptimised Household Demand')
print "Total house level consumption = " + str(np.around(houselevel_cum_sum[-1], 2)) + " kWh"
c_load = [0 for zero in range(0, len(houselevel_data))] # zero value matrix for controlled demand
occupants = [2, 0, 0, 0, 0, 0]
routine_ = rb.routine_builder(houselevel_data, min_interval, occupants)
bill_type = 2
cost_number = 54
billing_info = be.bill_eval(houselevel_data, bill_type, cost_number, min_interval, c_load, meter, 0)
house_cost = be.billing_calc(houselevel_data, c_load, bill_type, billing_info[0], billing_info[1], billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(house_cost), 2))
device_data = []
for row in df[1][1]:
device_data.append(row)
skiplist = []
count = 0
for x in np.arange(0, len(device_data)):
if not x in skiplist:
if device_data[x] > 0:
count += 1
for z in np.arange(x, len(device_data)):
skiplist.append(z)
if device_data[z] == 0:
break
original_cum_sum = np.cumsum(device_data)
plt.plot(original_cum_sum, label='Unoptimised Device Demand')
device_per = np.around(np.divide(original_cum_sum[-1], houselevel_cum_sum[-1])*100, 2)
device_cost = be.billing_calc(device_data, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Device accounts for " + str(device_per) + "% of household consumption"
print "A total of " + str(np.around(original_cum_sum[-1], 2)) + " kWh"
print "The device costings a total of $ " + str(np.around(sum(device_cost), 2))
print "Occuring in " + str(count) + " blocks"
for i in np.arange(7, 8):
hh_threshold = i
if hh_threshold >= 6:
print "\nThreshold number = " + str(hh_threshold)
vari = airconditioner(device_data, routine_, min_interval, billing_info[1], hh_threshold, shifting_, 0)
print(np.sum(vari))
var_cum_sum = np.cumsum(vari)
optimised_cum_sum = original_cum_sum + var_cum_sum
if hh_threshold >= 1000:
plt.plot(optimised_cum_sum, label='Motivation Level = %i' % i)
per_saved = np.around(100 - np.divide(np.max(optimised_cum_sum), np.max(original_cum_sum))*100, 2)
print "Percentage Saved = " + str(per_saved) + "% & kWh saved = " + str(np.around((original_cum_sum[-1]-optimised_cum_sum[-1]), 2)) + " kWh"
device_per = np.around(np.divide(optimised_cum_sum[-1], houselevel_cum_sum[-1]+var_cum_sum[-1])*100, 2)
optimised_device_data = [a + b for a, b in zip(device_data, vari)]
opti_device_cost = be.billing_calc(optimised_device_data, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Saving a total of $ " + str(np.around(sum(device_cost)-sum(opti_device_cost), 2))
print "Device accounts for " + str(device_per) + "% of household consumption"
house_per_save = np.around(100 - np.divide(houselevel_cum_sum[-1]+var_cum_sum[-1], houselevel_cum_sum[-1])*100, 2)
print "Comparitive house level saving " + str(house_per_save) + "%"
print "Optimised house level consumption " + str(np.around(houselevel_cum_sum[-1]+var_cum_sum[-1], 2)) + " kWh"
optimised_house_level = [a + b for a, b in zip(houselevel_data, vari)]
houselevel_cost = be.billing_calc(optimised_house_level, c_load, bill_type, 0, billing_info[1], billing_info[2], billing_info[3])
print "Costing a total of $ " + str(np.around(sum(houselevel_cost), 2))