def data_electrical(request):
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
electricalData = electricalGSP.objects.filter(GSP=supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in electricalGSP._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 3])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 4):
process[i, (j - 1)] = change[j]
g = np.array(process) / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': g[0:, 2]})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
S = S.reset_index()
columnsTitles = ["Time", "Data"]
proData = S.reindex(columns=columnsTitles)
finalData = proData.iloc[0:(24 * d)]
response = HttpResponse(content_type='text/csv')
if m == 13:
file = gspName.name + 'GSP_0104_3103.csv'
elif m < 10:
file = gspName.name + 'GSP_0' + str(int(m))
else:
file = gspName.name + 'GSP_' + str(int(m))
response['Content-Disposition'] = 'attachment; filename="%s"' % file
writer = csv.writer(response)
with open('heat_demand.csv', 'w') as csvfile:
writer.writerow(['', 'Electrical Demand (MWh)'])
for i in range(0, len(finalData)):
writer.writerow(finalData.iloc[i, 0:])
return response
def gspDemand(supplyPoint, m, d):
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
return elec
def sizing_storage(request):
supplyPoint=request.GET['supplyPoint']
m=int(request.GET['month'])
d=request.GET['days']
gspName=GSP.objects.get(idx=supplyPoint)
ap=1 -(int(request.GET['demandReduction'])/100)
if d =='':
d=int(0)
else:
d = int(d)
if m == 13:
d=366
electricalData = electricalGSP.objects.filter(GSP = supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(vlqs, names=[f.name for f in electricalGSP._meta.fields])
l=np.array(r)
process = np.zeros([len(l),3])
for i in range(0,len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1,4):
process[i,(j-1)] = change[j]
g=np.array(process)/2
dS=pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData=pd.DataFrame({'Time':dS[0:8784], 'Data':g[0:,2]})
data=proData.set_index('Time')
d1, d2, S = month(m, data)
elec=S['Data']
capacityProxy=np.zeros([24, d])
apProxy=np.zeros([24, d])
for i in range(1,(d+1)):
model, sol, capacity, ap = runSolutionCapacity((elec.iloc[(24*i)-24:(24*i)]),ap)
capacityProxy[0:,(i-1)]=capacity
apProxy[0:,(i-1)]=ap
capacityFinal=capacityProxy.reshape((d*24), order='F')
apFinal=apProxy.reshape((d*24),order='F')
capacityMax = np.amax(capacityFinal)
reductionMax = np.amax(apFinal)*100
return render(request,'energyStorage/sizing_details.html',{'capacityMax':capacityMax, 'reductionMax': reductionMax})
def data_charge_demand(request):
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspNameProxy = GSP.objects.get(idx=supplyPoint)
gspName = GSP.objects.filter(idx=supplyPoint)
na0 = ([p.name for p in gspName])
gspAuthority = gspLocalAuthority.objects.filter(gsp=na0[0])
na1 = ([p.localAuthority for p in gspAuthority])
mediumEV = request.GET['mediumEV']
smallEV = request.GET['smallEV']
geographicArea = int(request.GET['geographicArea'])
if geographicArea == 1:
gA = 'Urban'
else:
gA = 'Rural'
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
if mediumEV == '':
mediumEV = int(0)
else:
mediumEV = int(mediumEV)
if smallEV == '':
smallEV = int(0)
else:
smallEV = int(smallEV)
#electricalData = process_data((electricalGSP.objects.filter(GSP = supplyPoint)), electricalGSP, 3)
jobElectricalData = process_data.delay(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = jobElectricalData.perform()
#chargeDataSmall = process_data((Journey.objects.filter(localAuthority=na1[0], Area=gA, typeEV='Economy')), Journey,7)
jobChargeDataSmall = process_data.delay((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Economy')), Journey, 7)
chargeDataSmall = jobChargeDataSmall.perform()
#chargeDataMedium = process_data((Journey.objects.filter(localAuthority=na1[0], Area=gA, typeEV='Midsize')), Journey, 7)
jobChargeDataMedium = process_data.delay((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Midsize')), Journey, 7)
chargeDataMedium = jobChargeDataMedium.perform()
# good to here
electricalData = electricalData[0:, 2] / 2
chargeDataSmall = chargeDataSmall[0:, 0:4]
chargeDataMedium = chargeDataMedium[0:, 0:4]
#profileSmall=formatChargeDemand(smallEV, chargeDataSmall, 'Economy',d)
jobProfileSmall = formatChargeDemand.delay(smallEV, chargeDataSmall,
'Economy', d)
profileSmall = jobProfileSmall.perform()
#profileMedium=formatChargeDemand(mediumEV, chargeDataMedium, 'Midsize',d)
jobProfileMedium = formatChargeDemand.delay(mediumEV, chargeDataMedium,
'Midsize', d)
profileMedium = jobProfileMedium.perform()
profileTotal = np.zeros([len(profileSmall), 1])
profileTotal[0:, 0] = (profileSmall['Charge'].as_matrix() +
profileMedium['Charge'].as_matrix()) / 1000
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
S = S.reset_index()
columnsTitles = ["Time", "Data"]
proData = S.reindex(columns=columnsTitles)
base = np.zeros([(24 * d), 1])
base[0:, 0] = proData.iloc[0:(24 * d), 1]
newData = base + profileTotal
inter = np.append(profileTotal, newData, 1)
finalData = np.append(proData.iloc[0:(24 * d)], inter, 1)
####
if geographicArea == 1:
file = gspNameProxy.name + '_' + str(
mediumEV) + "_MidsizeEV" + '_' + str(
smallEV) + "_SmallEV_Urban.csv"
else:
file = gspNameProxy.name + '_' + str(
mediumEV) + "_MidsizeEV" + '_' + str(
smallEV) + "_SmallEV_Rural.csv"
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="%s"' % file
writer = csv.writer(response)
with open('charge_demand.csv', 'w') as csvfile:
writer.writerow([
'', 'Base GSP Demand (MWh)', 'EV Charging Demand (MWh)',
'Future GSP Demand (MWh)'
])
#writer.writerow([''])
for i in range(0, len(finalData)):
writer.writerow(finalData[i, 0:])
return response
def plot_ev_demand_compare(request):
supplyPoint = request.GET['supplyPoint']
supplyPoint2 = request.GET['supplyPoint2']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.filter(idx=supplyPoint)
na0 = ([p.name for p in gspName])
gspAuthority = gspLocalAuthority.objects.filter(gsp=na0[0])
na1 = ([p.localAuthority for p in gspAuthority])
gspName2 = GSP.objects.filter(idx=supplyPoint2)
na0_2 = ([p.name for p in gspName2])
gspAuthority2 = gspLocalAuthority.objects.filter(gsp=na0_2[0])
na1_2 = ([p.localAuthority for p in gspAuthority2])
mediumEV = request.GET['mediumEV']
smallEV = request.GET['smallEV']
geographicArea = int(request.GET['geographicArea'])
if geographicArea == 1:
gA = 'Urban'
else:
gA = 'Rural'
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
if mediumEV == '':
mediumEV = int(0)
else:
mediumEV = int(mediumEV)
if smallEV == '':
smallEV = int(0)
else:
smallEV = int(smallEV)
electricalData = process_data(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
chargeDataSmall = process_data((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Economy')), Journey, 7)
chargeDataMedium = process_data((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Midsize')), Journey, 7)
electricalData2 = process_data(
(electricalGSP.objects.filter(GSP=supplyPoint2)), electricalGSP, 3)
chargeDataSmall2 = process_data((Journey.objects.filter(
localAuthority=na1_2[0], Area=gA, typeEV='Economy')), Journey, 7)
chargeDataMedium2 = process_data((Journey.objects.filter(
localAuthority=na1_2[0], Area=gA, typeEV='Midsize')), Journey, 7)
###
electricalData = electricalData[0:, 2] / 2
chargeDataSmall = chargeDataSmall[0:, 0:4]
chargeDataMedium = chargeDataMedium[0:, 0:4]
profileSmall = formatChargeDemand(smallEV, chargeDataSmall, 'Economy', d)
profileMedium = formatChargeDemand(mediumEV, chargeDataMedium, 'Midsize',
d)
profileTotal = np.zeros([len(profileSmall), 1])
profileTotal[0:, 0] = (profileSmall['Charge'].as_matrix() +
profileMedium['Charge'].as_matrix())
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
pC = max(profileTotal)[0]
peakCharge = round(pC, 2)
###
electricalData2 = electricalData2[0:, 2] / 2
chargeDataSmall2 = chargeDataSmall2[0:, 0:4]
chargeDataMedium2 = chargeDataMedium2[0:, 0:4]
profileSmall2 = formatChargeDemand(smallEV, chargeDataSmall2, 'Economy', d)
profileMedium2 = formatChargeDemand(mediumEV, chargeDataMedium2, 'Midsize',
d)
profileTotal2 = np.zeros([len(profileSmall), 1])
profileTotal2[0:, 0] = (profileSmall2['Charge'].as_matrix() +
profileMedium2['Charge'].as_matrix())
pC2 = max(profileTotal2)[0]
peakCharge2 = round(pC2, 2)
###
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m == 13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates = dates[0:(24 * d)]
fig, ax = plt.subplots()
ax.plot_date(finalDates,
profileTotal,
linestyle='-',
marker='None',
label=na0[0])
ax.plot_date(finalDates,
profileTotal2,
linestyle='-',
marker='None',
label=na0_2[0])
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
plt.ylabel('Charging Demand (kWh)')
plt.grid(True)
plt.legend()
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
##
return render(
request, 'transport/show_plot_ev_compare.html', {
'graphic': graphic,
'name': na0[0],
'name2': na0_2[0],
'peakCharge': peakCharge,
'peakCharge2': peakCharge2
})
def plot_electrical(request):
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
electricalData = electricalGSP.objects.filter(GSP=supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in electricalGSP._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 3])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 4):
process[i, (j - 1)] = change[j]
g = np.array(process)
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': g[0:, 2]})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
finalData = elec.iloc[0:(24 * d)] / 2
if max(finalData) > abs(min(finalData)):
maxGSP_proxy = max(finalData)
else:
maxGSP_proxy = min(finalData)
maxGSP = round(maxGSP_proxy, 2)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
rating = statHP[0, 1]
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m == 13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates = dates[0:(24 * d)]
fig, ax = plt.subplots()
ax.plot_date(finalDates,
finalData,
linestyle='-',
marker='None',
label='Demand')
## if (finalData<0).any() == True and (finalData>0).any() == False:
## ax.plot_date(finalDates,ratingNeg, linestyle='-', marker='None', color='r', label='GSP Rating')
## elif (finalData<0).any() == False and (finalData>0).any() == True:
## ax.plot_date(finalDates,rating, linestyle='-', marker='None', color='r', label='GSP Rating')
## elif (finalData<0).any() == True and (finalData>0).any() == True:
## ax.plot_date(finalDates,ratingNeg, linestyle='-', marker='None', color='r', label='GSP Rating')
## ax.plot_date(finalDates,rating, linestyle='-', marker='None', color='r')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
plt.ylabel('Electrical Demand (MWh)')
plt.title('Electrical Demand Plot for ' + gspName.name + ' GSP')
plt.grid(True)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
##
return render(request, 'electrical/show_plot.html', {
'graphic': graphic,
'maxGSP': maxGSP,
'rating': rating
})
def calculateIndustrialHeatPumpDemand(supplyPoint, d, m, temp, manufacturingHP,
commercialHP, entertainmentHP,
educationHP, indicator):
#heatData = industrialHeat.objects.filter(GSP = supplyPoint)
heatData = industrialBreakdown.objects.filter(GSP=supplyPoint)
vlqs = heatData.values_list()
#r = np.core.records.fromrecords(vlqs, names=[f.name for f in industrialHeat._meta.fields])
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in industrialBreakdown._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 6])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 7):
process[i, (j - 1)] = change[j]
g = np.array(process)
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData1 = pd.DataFrame({
'Time': dS[0:8784],
'Manufacturing': g[0:, 2],
'Commercial': g[0:, 3],
'Entertainment': g[0:, 4],
'Education': g[0:, 5]
})
data1 = proData1.set_index('Time')
d1, d2, S1 = month(m, data1)
manufacturing = S1['Manufacturing']
totalDemandManufacturing = manufacturing.iloc[0:(24 * d)]
commercial = S1['Commercial']
totalDemandCommercial = commercial.iloc[0:(24 * d)]
entertainment = S1['Entertainment']
totalDemandEntertainment = entertainment.iloc[0:(24 * d)]
education = S1['Education']
totalDemandEducation = education.iloc[0:(24 * d)]
demandIndustrialHP = np.zeros(d * 24)
if indicator == 1:
numbs = industrialNumbers.objects.filter(GSP=supplyPoint)
interNumbs = np.core.records.fromrecords(
numbs.values_list(),
names=[f.name for f in industrialNumbers._meta.fields])
totalNumbs = np.array(interNumbs)
manufacturingHP = int(totalNumbs[0][2] * (manufacturingHP / 100))
commercialHP = int(totalNumbs[0][3] * (commercialHP / 100))
entertainmentHP = int(totalNumbs[0][4] * (entertainmentHP / 100))
educationHP = int(totalNumbs[0][5] * (educationHP / 100))
for i in range(1, (d + 1)):
demMan = (totalDemandManufacturing.iloc[(
(24 * i) - 24):(24 * i)].as_matrix()) * manufacturingHP
demCom = (totalDemandCommercial.iloc[(
(24 * i) - 24):(24 * i)].as_matrix()) * commercialHP
demEnt = (totalDemandEntertainment.iloc[(
(24 * i) - 24):(24 * i)].as_matrix()) * entertainmentHP
demEdu = (totalDemandEducation.iloc[(
(24 * i) - 24):(24 * i)].as_matrix()) * educationHP
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demIndustrialB = np.zeros(24)
for j in range(0, 24):
demIndustrialB[j] = (demMan[j] /
(tempB[j] * 0.04762 + 3.03283)) + (
demCom[j] /
(tempB[j] * 0.04762 + 3.03283)) + (
demEnt[j] /
(tempB[j] * 0.04762 + 3.03283)) + (
demEdu[j] /
(tempB[j] * 0.04762 + 3.03283))
demandIndustrialHP[((24 * i) - 24):(24 * i)] = demIndustrialB
totalHeatPumpIndustrial = demandIndustrialHP
return totalHeatPumpIndustrial
def calculateHeatPumpDemand(supplyPoint, tmy_data, d, m, smallHP, mediumHP,
largeHP):
h40HP = smallHP / 2
h60HP = smallHP / 2
h100HP = mediumHP / 2
h140HP = mediumHP / 2
h160HP = largeHP
if isinstance(h40HP, float):
h40HP = h40HP + 0.5
h60HP = h60HP - 0.5
if isinstance(h100HP, float):
h100HP = h100HP + 0.5
h140HP = h140HP - 0.5
numSmallHP = h40HP + h60HP
numMediumHP = h100HP + h140HP
numLargeHP = h160HP
#change data type
rawData = processData(supplyPoint)
hourSmallHP, totalDemandSmallHP = format_inputs(rawData, h40HP, h60HP, 0,
0, 0)
hourMediumHP, totalDemandMediumHP = format_inputs(rawData, 0, 0, h100HP,
h140HP, 0)
hourLargeHP, totalDemandLargeHP = format_inputs(rawData, 0, 0, 0, 0,
h160HP)
## latitude=statHP[0,2]
## longitude=statHP[0,3]
## tmy_data, altitude = extract_weather(d,m, latitude, longitude)
temp = tmy_data['DryBulb']
# add time element to data
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
##Small Houses with Heat Pumps
proDataSmallHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmallHP / 1000)
})
dataSmallHP = proDataSmallHP.set_index('Time')
d1, d2, smallSHP = month(m, dataSmallHP)
reSmallHP = pd.DataFrame(smallSHP['Data'], index=smallSHP.index)
reSmallHP.columns = ['Demand']
finalDataSmallHP = reSmallHP.iloc[0:(24 * d)]
indSmallHP = finalDataSmallHP / numSmallHP
demandSmallHP = np.zeros(d * 24)
if numSmallHP != 0:
for i in range(1, (d + 1)):
demB = indSmallHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demSmallB = np.zeros(24)
for j in range(0, 24):
demSmallB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandSmallHP[((24 * i) - 24):(24 * i)] = demSmallB
smallHPTotal = demandSmallHP * numSmallHP
else:
smallHPTotal = np.zeros(d * 24)
##Medium houses with heat pumps
proDataMediumHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMediumHP / 1000)
})
dataMediumHP = proDataMediumHP.set_index('Time')
d1, d2, mediumSHP = month(m, dataMediumHP)
reMediumHP = pd.DataFrame(mediumSHP['Data'], index=mediumSHP.index)
reMediumHP.columns = ['Demand']
finalDataMediumHP = reMediumHP.iloc[0:(24 * d)]
indMediumHP = finalDataMediumHP / numMediumHP
demandMediumHP = np.zeros(d * 24)
if numMediumHP != 0:
for i in range(1, (d + 1)):
demB = indMediumHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demMediumB = np.zeros(24)
for j in range(0, 24):
demMediumB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandMediumHP[((24 * i) - 24):(24 * i)] = demMediumB
mediumHPTotal = demandMediumHP * numMediumHP
else:
mediumHPTotal = np.zeros(d * 24)
##Large houses with heat pumps
proDataLargeHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLargeHP / 1000)
})
dataLargeHP = proDataLargeHP.set_index('Time')
d1, d2, largeSHP = month(m, dataLargeHP)
reLargeHP = pd.DataFrame(largeSHP['Data'], index=largeSHP.index)
reLargeHP.columns = ['Demand']
finalDataLargeHP = reLargeHP.iloc[0:(24 * d)]
indLargeHP = finalDataLargeHP / numLargeHP
demandLargeHP = np.zeros(d * 24)
if numLargeHP != 0:
for i in range(1, (d + 1)):
demB = indLargeHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demLargeB = np.zeros(24)
for j in range(0, 24):
demLargeB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandLargeHP[((24 * i) - 24):(24 * i)] = demLargeB
largeHPTotal = demandLargeHP * numLargeHP
else:
largeHPTotal = np.zeros(d * 24)
totHPCharge = (smallHPTotal[0:] / 1000) + (mediumHPTotal[0:] / 1000) + (
largeHPTotal[0:] / 1000)
return totHPCharge
def plot_gsp_storage(request):
supplyPoint=request.GET['supplyPoint']
m=int(request.GET['month'])
d=request.GET['days']
gspName=GSP.objects.get(idx=supplyPoint)
esCap=int(request.GET['esCap'])
if d =='':
d=int(0)
else:
d = int(d)
if m == 13:
d=366
electricalData = electricalGSP.objects.filter(GSP = supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(vlqs, names=[f.name for f in electricalGSP._meta.fields])
l=np.array(r)
process = np.zeros([len(l),3])
for i in range(0,len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1,4):
process[i,(j-1)] = change[j]
g=np.array(process)/2
dS=pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData=pd.DataFrame({'Time':dS[0:8784], 'Data':g[0:,2]})
data=proData.set_index('Time')
d1, d2, S = month(m, data)
elec=S['Data']
fProxy=np.zeros([24, d])
cProxy=np.zeros([24, d])
disProxy=np.zeros([24, d])
newDemandProxy=np.zeros([24, d])
for i in range(1,(d+1)):
model, sol, flo, cha, dis, nD = runSolutionBalance(esCap, (elec.iloc[(24*i)-24:(24*i)]))
fProxy[0:,(i-1)]=flo
cProxy[0:,(i-1)]=cha
disProxy[0:,(i-1)]=dis
newDemandProxy[0:,(i-1)]=nD
f=fProxy.reshape((d*24), order='F')
c=cProxy.reshape((d*24),order='F')
dis=disProxy.reshape((d*24),order='F')
newDemand=newDemandProxy.reshape((d*24),order='F')
check=all(i > 0 for i in f)
if np.absolute(np.amax(f)) > np.absolute(np.amin(f)):
fMax = np.amax(f)
newDemandMax = np.amax(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((fMax - newDemandMax), 2)
else:
fMax = np.amin(f)
newDemandMax = np.amin(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((newDemandMax- fMax), 2)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)), gspStats, 16)
rating=statHP[0,1]
####Plotting####
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m==13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates=dates[0:(24*d)]
fig, ax =plt.subplots()
ax.plot_date(finalDates, f, linestyle='-', marker='None', label='Base Case (no BES)')
ax.plot(finalDates, newDemand, linestyle='-', marker='None', label='With BES')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
ax.legend()
plt.ylabel('Electrical Demand (MWh)')
plt.title('Electrical Demand Plot for '+ gspName.name + ' GSP' + ' with ' + str(esCap)+ ' MWh BES')
plt.grid(True)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(), canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
return render(request, 'energyStorage/show_plot.html', {'graphic':graphic, 'fMax':fMax, 'newDemandMax': newDemandMax, 'peakReduction':peakReduction,'esCap':esCap, 'headroom':headroom, 'rating':rating})
def data_gsp_storage(request):
supplyPoint=request.GET['supplyPoint']
m=int(request.GET['month'])
d=request.GET['days']
gspName=GSP.objects.get(idx=supplyPoint)
esCap=int(request.GET['esCap'])
if d =='':
d=int(0)
else:
d = int(d)
if m == 13:
d=366
electricalData = electricalGSP.objects.filter(GSP = supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(vlqs, names=[f.name for f in electricalGSP._meta.fields])
l=np.array(r)
process = np.zeros([len(l),3])
for i in range(0,len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1,4):
process[i,(j-1)] = change[j]
g=np.array(process)/2
dS=pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData=pd.DataFrame({'Time':dS[0:8784], 'Data':g[0:,2]})
data=proData.set_index('Time')
d1, d2, S = month(m, data)
elec=S['Data']
fProxy=np.zeros([24, d])
cProxy=np.zeros([24, d])
disProxy=np.zeros([24, d])
newDemandProxy=np.zeros([24, d])
for i in range(1,(d+1)):
model, sol, flo, cha, dis, nD = runSolutionBalance(esCap, (elec.iloc[(24*i)-24:(24*i)]))
fProxy[0:,(i-1)]=flo
cProxy[0:,(i-1)]=cha
disProxy[0:,(i-1)]=dis
newDemandProxy[0:,(i-1)]=nD
f=fProxy.reshape((d*24), order='F')
c=cProxy.reshape((d*24),order='F')
dis=disProxy.reshape((d*24),order='F')
newDemand=newDemandProxy.reshape((d*24),order='F')
check=all(i > 0 for i in f)
if np.absolute(np.amax(f)) > np.absolute(np.amin(f)):
fMax = np.amax(f)
newDemandMax = np.amax(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((fMax - newDemandMax), 2)
else:
fMax = np.amin(f)
newDemandMax = np.amin(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((newDemandMax- fMax), 2)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates=elec[0:(24*d)].index
powerData=np.zeros([len(f),3])
powerData[0:,0]=f
powerData[0:,1]=newDemand
powerData[0:,2]=c+dis
finalDataProxy=pd.DataFrame(powerData, index=finalDates)
finalData=finalDataProxy.reset_index()
response = HttpResponse(content_type='text/csv')
file =gspName.name +'GSP_'+str(esCap)+'MWh_energy_storage.csv'
response['Content-Disposition'] = 'attachment; filename="%s"' % file
writer = csv.writer(response)
with open('pv_generation.csv','w') as csvfile:
writer.writerow(['', 'Base Demand(MWh)', 'New Demand(MWh)', 'Energy Storage Flow (MWh)'])
for i in range(0,len(finalData)):
writer.writerow(finalData.iloc[i,0:])
return response
def industrial_electric_data(request):
percent = int(request.GET['percent'])
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if d == '':
d = int(0)
else:
d = int(d)
if m == 3 and d > 30:
d = 30
if m == 13:
d = 365
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
firm = statHP[0, 1]
latitude = statHP[0, 2]
longitude = statHP[0, 3]
tmy_data, altitude = extract_weather(d, m, latitude, longitude)
temp = tmy_data['DryBulb']
heatData = industrialHeat.objects.filter(GSP=supplyPoint)
vlqs = heatData.values_list()
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in industrialHeat._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 3])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 4):
process[i, (j - 1)] = change[j]
g = np.array(process)
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': g[0:, 2]})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
heat = S['Data']
totalDemand = heat.iloc[0:(24 * d)]
demandIndustrialHP = np.zeros(d * 24)
for i in range(1, (d + 1)):
demB = (totalDemand.iloc[(
(24 * i) - 24):(24 * i)].as_matrix() * 1e6) * (percent / 100)
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demIndustrialB = np.zeros(24)
for j in range(0, 24):
demIndustrialB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandIndustrialHP[((24 * i) - 24):(24 * i)] = demIndustrialB
totalHeatPumpIndustrial = demandIndustrialHP / 1e6
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
elecChange = pd.DataFrame((elec + totalHeatPumpIndustrial),
index=elec.index)
dataFinal = np.zeros([(24 * d), 2])
dataFinal[0:, 0] = totalHeatPumpIndustrial
dataFinal[0:, 1] = elec.as_matrix() + totalHeatPumpIndustrial
chargeOut = pd.DataFrame(dataFinal, index=elec.index)
finalData = chargeOut.reset_index()
response = HttpResponse(content_type='text/csv')
file = gspName.name + 'GSP_'
response['Content-Disposition'] = 'attachment; filename="%s"' % file
writer = csv.writer(response)
with open('pv_generation.csv', 'w') as csvfile:
writer.writerow(['', 'Electric Heat Demand(MWh)'])
for i in range(0, len(finalData)):
writer.writerow(finalData.iloc[i, 0:])
return response
def industrial_electric_plot(request):
percent = int(request.GET['percent'])
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if d == '':
d = int(0)
else:
d = int(d)
if m == 3 and d > 30:
d = 30
if m == 13:
d = 365
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
firm = statHP[0, 1]
latitude = statHP[0, 2]
longitude = statHP[0, 3]
tmy_data, altitude = extract_weather(d, m, latitude, longitude)
temp = tmy_data['DryBulb']
heatData = industrialHeat.objects.filter(GSP=supplyPoint)
vlqs = heatData.values_list()
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in industrialHeat._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 3])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 4):
process[i, (j - 1)] = change[j]
g = np.array(process)
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': g[0:, 2]})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
heat = S['Data']
totalDemand = heat.iloc[0:(24 * d)]
demandIndustrialHP = np.zeros(d * 24)
for i in range(1, (d + 1)):
demB = (totalDemand.iloc[(
(24 * i) - 24):(24 * i)].as_matrix() * 1e6) * (percent / 100)
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demIndustrialB = np.zeros(24)
for j in range(0, 24):
demIndustrialB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandIndustrialHP[((24 * i) - 24):(24 * i)] = demIndustrialB
totalHeatPumpIndustrial = demandIndustrialHP / 1e6
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
elecChange = pd.DataFrame((elec + totalHeatPumpIndustrial),
index=elec.index)
peakChargeProxy = max(totalHeatPumpIndustrial)
peakCharge = round(peakChargeProxy, 2)
if max(elec.as_matrix()) > abs(min(elec.as_matrix())):
maxGSP_proxy = max(elec.as_matrix())
else:
maxGSP_proxy = min(elec.as_matrix())
maxGSP = round(maxGSP_proxy, 2)
if max((elec + totalHeatPumpIndustrial).as_matrix()) > abs(
min((elec + totalHeatPumpIndustrial).as_matrix())):
maxGSPNew_proxy = max((elec + totalHeatPumpIndustrial).as_matrix())
else:
maxGSPNew_proxy = min((elec + totalHeatPumpIndustrial).as_matrix())
maxGSPNew = round(maxGSPNew_proxy, 2)
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=5)
elif d > 31:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates = dates[0:(24 * d)]
fig, ax = plt.subplots()
ax.plot_date(finalDates,
totalHeatPumpIndustrial,
linestyle='-',
marker='None',
label=gspName.name)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
ax.legend()
plt.grid(True)
plt.ylabel('Heat Demand (MWh)')
plt.title('Industrial Heat Demand Plot for ' + gspName.name + ' GSP')
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
##
fig2, ax2 = plt.subplots()
ax2.plot_date(elec.index, (elec.iloc[0:].as_matrix()),
linestyle='-',
marker='None',
label='Base Demand')
ax2.plot_date(elecChange.index, (elecChange.iloc[0:, 0].as_matrix()),
linestyle='-',
marker='None',
label='New Demand')
ax2.xaxis.set_major_formatter(formatter)
ax2.xaxis.set_major_locator(loc)
ax2.legend()
plt.grid(True)
plt.ylabel('Electrical Demand (MWh)')
plt.title(gspName.name + ' GSP Demand')
buffer2 = BytesIO()
canvas2 = pylab.get_current_fig_manager().canvas
canvas2.draw()
pilImage2 = PIL.Image.frombytes("RGB", canvas2.get_width_height(),
canvas2.tostring_rgb())
pilImage2.save(buffer2, "PNG")
pylab.close()
image_png2 = buffer2.getvalue()
graphic2 = base64.b64encode(image_png2)
graphic2 = graphic2.decode('utf-8')
return render(
request, 'electricHeat/show_plot_industrial.html', {
'graphic': graphic,
'graphic2': graphic2,
'firm': firm,
'peakCharge': peakCharge,
'maxGSP': maxGSP,
'maxGSPNew': maxGSPNew
})
def storage_heater_plot(request):
#get user input data from browser
small = request.GET['small']
medium = request.GET['medium']
large = request.GET['large']
smallHP = request.GET['smallHP']
mediumHP = request.GET['mediumHP']
largeHP = request.GET['largeHP']
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if small == '':
small = int(0)
else:
small = int(small)
if smallHP == '':
smallHP = int(0)
else:
smallHP = int(smallHP)
if medium == '':
medium = int(0)
else:
medium = int(medium)
if mediumHP == '':
mediumHP = int(0)
else:
mediumHP = int(mediumHP)
if large == '':
large = int(0)
else:
large = int(large)
if largeHP == '':
largeHP = int(0)
else:
largeHP = int(largeHP)
if d == '':
d = int(0)
else:
d = int(d)
if m == 3 and d > 30:
d = 30
if m == 13:
d = 365
##
h40 = small / 2
h60 = small / 2
h100 = medium / 2
h140 = medium / 2
h160 = large
if isinstance(h40, float):
h40 = h40 + 0.5
h60 = h60 - 0.5
if isinstance(h100, float):
h100 = h100 + 0.5
h140 = h140 - 0.5
numSmall = h40 + h60
numMedium = h100 + h140
numLarge = h160
##
h40HP = smallHP / 2
h60HP = smallHP / 2
h100HP = mediumHP / 2
h140HP = mediumHP / 2
h160HP = largeHP
if isinstance(h40HP, float):
h40HP = h40HP + 0.5
h60HP = h60HP - 0.5
if isinstance(h100HP, float):
h100HP = h100HP + 0.5
h140HP = h140HP - 0.5
numSmallHP = h40HP + h60HP
numMediumHP = h100HP + h140HP
numLargeHP = h160HP
#change data type
rawData = processData(supplyPoint)
hourSmall, totalDemandSmall = format_inputs(rawData, h40, h60, 0, 0,
0) # output in Watts
hourMedium, totalDemandMedium = format_inputs(rawData, 0, 0, h100, h140, 0)
hourLarge, totalDemandLarge = format_inputs(rawData, 0, 0, 0, 0, h160)
# add time element to data
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
##small sized property##
proDataSmall = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmall / 1000)
})
dataSmall = proDataSmall.set_index('Time')
d1, d2, smallS = month(m, dataSmall)
reSmall = pd.DataFrame(smallS['Data'], index=smallS.index)
reSmall.columns = ['Demand']
finalDataSmall = reSmall.iloc[0:(24 * d)]
chargeSmall = np.zeros(d * 24)
indSmall = finalDataSmall / numSmall
if numSmall != 0:
for i in range(1, (d + 1)):
intraSmall = pd.DataFrame(indSmall.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * 0.2))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * gone[0]))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
smallChargeTotal = chargeSmall * numSmall
else:
smallChargeTotal = np.zeros(d * 24)
##medium sized property##
proDataMedium = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMedium / 1000)
})
dataMedium = proDataMedium.set_index('Time')
d1, d2, mediumS = month(m, dataMedium)
reMedium = pd.DataFrame(mediumS['Data'], index=mediumS.index)
reMedium.columns = ['Demand']
finalDataMedium = reMedium.iloc[0:(24 * d)]
chargeMedium = np.zeros(d * 24)
indMedium = finalDataMedium / numMedium
if numMedium != 0:
for i in range(1, (d + 1)):
intraMedium = pd.DataFrame(indMedium.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * 0.2))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * gone[0]))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
mediumChargeTotal = chargeMedium * numMedium
else:
mediumChargeTotal = np.zeros(d * 24)
##Large sized property##
proDataLarge = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLarge / 1000)
})
dataLarge = proDataLarge.set_index('Time')
d1, d2, largeS = month(m, dataLarge)
reLarge = pd.DataFrame(largeS['Data'], index=largeS.index)
reLarge.columns = ['Demand']
finalDataLarge = reLarge.iloc[0:(24 * d)]
chargeLarge = np.zeros(d * 24)
indLarge = finalDataLarge / numLarge
if numLarge != 0:
for i in range(1, (d + 1)):
intraLarge = pd.DataFrame(indLarge.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * 0.2))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * gone[0]))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
largeChargeTotal = chargeLarge * numLarge
else:
largeChargeTotal = np.zeros(d * 24)
###########################################################################################################
##heat pumps
hourSmallHP, totalDemandSmallHP = format_inputs(rawData, h40HP, h60HP, 0,
0, 0)
hourMediumHP, totalDemandMediumHP = format_inputs(rawData, 0, 0, h100HP,
h140HP, 0)
hourLargeHP, totalDemandLargeHP = format_inputs(rawData, 0, 0, 0, 0,
h160HP)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
firm = statHP[0, 1]
latitude = statHP[0, 2]
longitude = statHP[0, 3]
tmy_data, altitude = extract_weather(d, m, latitude, longitude)
temp = tmy_data['DryBulb']
##Small Houses with Heat Pumps
proDataSmallHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmallHP / 1000)
})
dataSmallHP = proDataSmallHP.set_index('Time')
d1, d2, smallSHP = month(m, dataSmallHP)
reSmallHP = pd.DataFrame(smallSHP['Data'], index=smallSHP.index)
reSmallHP.columns = ['Demand']
finalDataSmallHP = reSmallHP.iloc[0:(24 * d)]
indSmallHP = finalDataSmallHP / numSmallHP
demandSmallHP = np.zeros(d * 24)
if numSmallHP != 0:
for i in range(1, (d + 1)):
demB = indSmallHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demSmallB = np.zeros(24)
for j in range(0, 24):
demSmallB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandSmallHP[((24 * i) - 24):(24 * i)] = demSmallB
smallHPTotal = demandSmallHP * numSmallHP
else:
smallHPTotal = np.zeros(d * 24)
##Medium houses with heat pumps
proDataMediumHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMediumHP / 1000)
})
dataMediumHP = proDataMediumHP.set_index('Time')
d1, d2, mediumSHP = month(m, dataMediumHP)
reMediumHP = pd.DataFrame(mediumSHP['Data'], index=mediumSHP.index)
reMediumHP.columns = ['Demand']
finalDataMediumHP = reMediumHP.iloc[0:(24 * d)]
indMediumHP = finalDataMediumHP / numMediumHP
demandMediumHP = np.zeros(d * 24)
if numMediumHP != 0:
for i in range(1, (d + 1)):
demB = indMediumHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demMediumB = np.zeros(24)
for j in range(0, 24):
demMediumB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandMediumHP[((24 * i) - 24):(24 * i)] = demMediumB
mediumHPTotal = demandMediumHP * numMediumHP
else:
mediumHPTotal = np.zeros(d * 24)
##Large houses with heat pumps
proDataLargeHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLargeHP / 1000)
})
dataLargeHP = proDataLargeHP.set_index('Time')
d1, d2, largeSHP = month(m, dataLargeHP)
reLargeHP = pd.DataFrame(largeSHP['Data'], index=largeSHP.index)
reLargeHP.columns = ['Demand']
finalDataLargeHP = reLargeHP.iloc[0:(24 * d)]
indLargeHP = finalDataLargeHP / numLargeHP
demandLargeHP = np.zeros(d * 24)
if numLargeHP != 0:
for i in range(1, (d + 1)):
demB = indLargeHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demLargeB = np.zeros(24)
for j in range(0, 24):
demLargeB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandLargeHP[((24 * i) - 24):(24 * i)] = demLargeB
largeHPTotal = demandLargeHP * numLargeHP
else:
largeHPTotal = np.zeros(d * 24)
#############################################################################################################
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
totCharge = (smallChargeTotal[0:] /
1000) + (mediumChargeTotal[0:] / 1000) + (
largeChargeTotal[0:] / 1000 + smallHPTotal[0:] / 1000 +
mediumHPTotal[0:] / 1000 + largeHPTotal[0:] / 1000)
## if m==13:
## totChargeNew=np.zeros(366*24)
## totChargeNew[0:(365*24)]=totCharge
## totChargeNew[(365*24):(366*24)]=totCharge[(364*24):(365*24)]
elecChange = pd.DataFrame((elec + totCharge), index=elec.index)
rating = np.zeros(d * 24)
rating[0:] = firm
peakChargeProxy = max(totCharge) * 1000
peakCharge = round(peakChargeProxy)
if max(elec.as_matrix()) > abs(min(elec.as_matrix())):
maxGSP_proxy = max(elec.as_matrix())
else:
maxGSP_proxy = min(elec.as_matrix())
maxGSP = round(maxGSP_proxy, 2)
if max((elec + totCharge).as_matrix()) > abs(
min((elec + totCharge).as_matrix())):
maxGSPNew_proxy = max((elec + totCharge).as_matrix())
else:
maxGSPNew_proxy = min((elec + totCharge).as_matrix())
maxGSPNew = round(maxGSPNew_proxy, 2)
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m == 13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
fig, ax = plt.subplots()
title = str(int(numSmall + numMedium +
numLarge)) + ' Homes with Storage Heaters \n' + str(
int(numSmallHP + numMediumHP +
numLargeHP)) + ' Homes with Heat Pumps'
if max(totCharge > 1):
ax.plot_date(elec.index, totCharge, linestyle='-', marker='None')
plt.ylabel('Electrical Demand (MWh)')
else:
ax.plot_date(elec.index,
totCharge * (1000),
linestyle='-',
marker='None')
plt.ylabel('Electrical Demand (kWh)')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
plt.grid(True)
plt.title(title)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
fig2, ax2 = plt.subplots()
ax2.plot_date(elec.index, (elec.iloc[0:].as_matrix()),
linestyle='-',
marker='None',
label='Base Demand')
ax2.plot_date(elecChange.index, (elecChange.iloc[0:, 0].as_matrix()),
linestyle='-',
marker='None',
label='New Demand')
ax2.xaxis.set_major_formatter(formatter)
ax2.xaxis.set_major_locator(loc)
ax2.legend()
plt.grid(True)
plt.ylabel('Electrical Demand (MWh)')
plt.title(gspName.name + ' GSP Demand')
buffer2 = BytesIO()
canvas2 = pylab.get_current_fig_manager().canvas
canvas2.draw()
pilImage2 = PIL.Image.frombytes("RGB", canvas2.get_width_height(),
canvas2.tostring_rgb())
pilImage2.save(buffer2, "PNG")
pylab.close()
image_png2 = buffer2.getvalue()
graphic2 = base64.b64encode(image_png2)
graphic2 = graphic2.decode('utf-8')
return render(
request, 'electricHeat/show_plot.html', {
'graphic': graphic,
'graphic2': graphic2,
'firm': firm,
'peakCharge': peakCharge,
'maxGSP': maxGSP,
'maxGSPNew': maxGSPNew
})
def storage_heater_data(request):
#get user input data from browser
small = request.GET['small']
medium = request.GET['medium']
large = request.GET['large']
smallHP = request.GET['smallHP']
mediumHP = request.GET['mediumHP']
largeHP = request.GET['largeHP']
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if small == '':
small = int(0)
else:
small = int(small)
if smallHP == '':
smallHP = int(0)
else:
smallHP = int(smallHP)
if medium == '':
medium = int(0)
else:
medium = int(medium)
if mediumHP == '':
mediumHP = int(0)
else:
mediumHP = int(mediumHP)
if large == '':
large = int(0)
else:
large = int(large)
if largeHP == '':
largeHP = int(0)
else:
largeHP = int(largeHP)
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
##
h40 = small / 2
h60 = small / 2
h100 = medium / 2
h140 = medium / 2
h160 = large
if isinstance(h40, float):
h40 = h40 + 0.5
h60 = h60 - 0.5
if isinstance(h100, float):
h100 = h100 + 0.5
h140 = h140 - 0.5
numSmall = h40 + h60
numMedium = h100 + h140
numLarge = h160
##
h40HP = smallHP / 2
h60HP = smallHP / 2
h100HP = mediumHP / 2
h140HP = mediumHP / 2
h160HP = largeHP
if isinstance(h40HP, float):
h40HP = h40HP + 0.5
h60HP = h60HP - 0.5
if isinstance(h100HP, float):
h100HP = h100HP + 0.5
h140HP = h140HP - 0.5
numSmallHP = h40HP + h60HP
numMediumHP = h100HP + h140HP
numLargeHP = h160HP
#change data type
rawData = processData(supplyPoint)
hourSmall, totalDemandSmall = format_inputs(rawData, h40, h60, 0, 0,
0) # output in Watts
hourMedium, totalDemandMedium = format_inputs(rawData, 0, 0, h100, h140, 0)
hourLarge, totalDemandLarge = format_inputs(rawData, 0, 0, 0, 0, h160)
# add time element to data
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
##small sized property##
proDataSmall = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmall / 1000)
})
dataSmall = proDataSmall.set_index('Time')
d1, d2, smallS = month(m, dataSmall)
reSmall = pd.DataFrame(smallS['Data'], index=smallS.index)
reSmall.columns = ['Demand']
finalDataSmall = reSmall.iloc[0:(24 * d)]
chargeSmall = np.zeros(d * 24)
indSmall = finalDataSmall / numSmall
if numSmall != 0:
for i in range(1, (d + 1)):
intraSmall = pd.DataFrame(indSmall.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * 0.2))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * gone[0]))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
smallChargeTotal = chargeSmall * numSmall
else:
smallChargeTotal = np.zeros(d * 24)
##medium sized property##
proDataMedium = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMedium / 1000)
})
dataMedium = proDataMedium.set_index('Time')
d1, d2, mediumS = month(m, dataMedium)
reMedium = pd.DataFrame(mediumS['Data'], index=mediumS.index)
reMedium.columns = ['Demand']
finalDataMedium = reMedium.iloc[0:(24 * d)]
chargeMedium = np.zeros(d * 24)
indMedium = finalDataMedium / numMedium
if numMedium != 0:
for i in range(1, (d + 1)):
intraMedium = pd.DataFrame(indMedium.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * 0.2))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * gone[0]))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
mediumChargeTotal = chargeMedium * numMedium
else:
mediumChargeTotal = np.zeros(d * 24)
##Large sized property##
proDataLarge = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLarge / 1000)
})
dataLarge = proDataLarge.set_index('Time')
d1, d2, largeS = month(m, dataLarge)
reLarge = pd.DataFrame(largeS['Data'], index=largeS.index)
reLarge.columns = ['Demand']
finalDataLarge = reLarge.iloc[0:(24 * d)]
chargeLarge = np.zeros(d * 24)
indLarge = finalDataLarge / numLarge
if numLarge != 0:
for i in range(1, (d + 1)):
intraLarge = pd.DataFrame(indLarge.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * 0.2))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * gone[0]))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
largeChargeTotal = chargeLarge * numLarge
else:
largeChargeTotal = np.zeros(d * 24)
###########################################################################################################
##heat pumps
hourSmallHP, totalDemandSmallHP = format_inputs(rawData, h40HP, h60HP, 0,
0, 0)
hourMediumHP, totalDemandMediumHP = format_inputs(rawData, 0, 0, h100HP,
h140HP, 0)
hourLargeHP, totalDemandLargeHP = format_inputs(rawData, 0, 0, 0, 0,
h160HP)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
latitude = statHP[0, 2]
longitude = statHP[0, 3]
tmy_data, altitude = extract_weather(d, m, latitude, longitude)
temp = tmy_data['DryBulb']
##Small Houses with Heat Pumps
proDataSmallHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmallHP / 1000)
})
dataSmallHP = proDataSmallHP.set_index('Time')
d1, d2, smallSHP = month(m, dataSmallHP)
reSmallHP = pd.DataFrame(smallSHP['Data'], index=smallSHP.index)
reSmallHP.columns = ['Demand']
finalDataSmallHP = reSmallHP.iloc[0:(24 * d)]
indSmallHP = finalDataSmallHP / numSmallHP
demandSmallHP = np.zeros(d * 24)
if numSmallHP != 0:
for i in range(1, (d + 1)):
demB = indSmallHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demSmallB = np.zeros(24)
for j in range(0, 24):
demSmallB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandSmallHP[((24 * i) - 24):(24 * i)] = demSmallB
smallHPTotal = demandSmallHP * numSmallHP
else:
smallHPTotal = np.zeros(d * 24)
##Medium houses with heat pumps
proDataMediumHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMediumHP / 1000)
})
dataMediumHP = proDataMediumHP.set_index('Time')
d1, d2, mediumSHP = month(m, dataMediumHP)
reMediumHP = pd.DataFrame(mediumSHP['Data'], index=mediumSHP.index)
reMediumHP.columns = ['Demand']
finalDataMediumHP = reMediumHP.iloc[0:(24 * d)]
indMediumHP = finalDataMediumHP / numMediumHP
demandMediumHP = np.zeros(d * 24)
if numMediumHP != 0:
for i in range(1, (d + 1)):
demB = indMediumHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demMediumB = np.zeros(24)
for j in range(0, 24):
demMediumB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandMediumHP[((24 * i) - 24):(24 * i)] = demMediumB
mediumHPTotal = demandMediumHP * numMediumHP
else:
mediumHPTotal = np.zeros(d * 24)
##Large houses with heat pumps
proDataLargeHP = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLargeHP / 1000)
})
dataLargeHP = proDataLargeHP.set_index('Time')
d1, d2, largeSHP = month(m, dataLargeHP)
reLargeHP = pd.DataFrame(largeSHP['Data'], index=largeSHP.index)
reLargeHP.columns = ['Demand']
finalDataLargeHP = reLargeHP.iloc[0:(24 * d)]
indLargeHP = finalDataLargeHP / numLargeHP
demandLargeHP = np.zeros(d * 24)
if numLargeHP != 0:
for i in range(1, (d + 1)):
demB = indLargeHP.iloc[((24 * i) - 24):(24 * i)].as_matrix()
tempB = temp.iloc[((24 * i) - 24):(24 * i)].as_matrix()
demLargeB = np.zeros(24)
for j in range(0, 24):
demLargeB[j] = demB[j] / (tempB[j] * 0.04762 + 3.03283)
demandLargeHP[((24 * i) - 24):(24 * i)] = demLargeB
largeHPTotal = demandLargeHP * numLargeHP
else:
largeHPTotal = np.zeros(d * 24)
#############################################################################################################
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
totCharge = (smallChargeTotal[0:] /
1000) + (mediumChargeTotal[0:] / 1000) + (
largeChargeTotal[0:] / 1000 + smallHPTotal[0:] / 1000 +
mediumHPTotal[0:] / 1000 + largeHPTotal[0:] / 1000)
elecChange = pd.DataFrame((elec + totCharge), index=elec.index)
chargeOut = pd.DataFrame(totCharge, index=elec.index)
finalData = chargeOut.reset_index()
response = HttpResponse(content_type='text/csv')
file = gspName.name + 'GSP_' + str(
int(numSmall + numMedium + numLarge)) + '_storage_heaters_' + str(
int(numSmallHP + numMediumHP + numLargeHP)) + '_heat_pumps.csv'
response['Content-Disposition'] = 'attachment; filename="%s"' % file
writer = csv.writer(response)
with open('pv_generation.csv', 'w') as csvfile:
writer.writerow(['', 'Electric Heat Demand(MWh)'])
for i in range(0, len(finalData)):
writer.writerow(finalData.iloc[i, 0:])
return response
def calculateStorageHeaterDemand(supplyPoint, d, m, small, medium, large):
h40 = small / 2
h60 = small / 2
h100 = medium / 2
h140 = medium / 2
h160 = large
if isinstance(h40, float):
h40 = h40 + 0.5
h60 = h60 - 0.5
if isinstance(h100, float):
h100 = h100 + 0.5
h140 = h140 - 0.5
numSmall = h40 + h60
numMedium = h100 + h140
numLarge = h160
rawData = processData(supplyPoint)
hourSmall, totalDemandSmall = format_inputs(rawData, h40, h60, 0, 0,
0) # output in Watts
hourMedium, totalDemandMedium = format_inputs(rawData, 0, 0, h100, h140, 0)
hourLarge, totalDemandLarge = format_inputs(rawData, 0, 0, 0, 0, h160)
# add time element to data
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
##small sized property##
proDataSmall = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandSmall / 1000)
})
dataSmall = proDataSmall.set_index('Time')
d1, d2, smallS = month(m, dataSmall)
reSmall = pd.DataFrame(smallS['Data'], index=smallS.index)
reSmall.columns = ['Demand']
finalDataSmall = reSmall.iloc[0:(24 * d)]
chargeSmall = np.zeros(d * 24)
indSmall = finalDataSmall / numSmall
if numSmall != 0:
for i in range(1, (d + 1)):
intraSmall = pd.DataFrame(indSmall.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * 0.2))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraSmall, 'Small',
(19.3 * gone[0]))
chargeSmall[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
smallChargeTotal = chargeSmall * numSmall
else:
smallChargeTotal = np.zeros(d * 24)
##medium sized property##
proDataMedium = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandMedium / 1000)
})
dataMedium = proDataMedium.set_index('Time')
d1, d2, mediumS = month(m, dataMedium)
reMedium = pd.DataFrame(mediumS['Data'], index=mediumS.index)
reMedium.columns = ['Demand']
finalDataMedium = reMedium.iloc[0:(24 * d)]
chargeMedium = np.zeros(d * 24)
indMedium = finalDataMedium / numMedium
if numMedium != 0:
for i in range(1, (d + 1)):
intraMedium = pd.DataFrame(indMedium.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * 0.2))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraMedium, 'Medium',
(19.3 * gone[0]))
chargeMedium[((24 * i) -
24):(24 * i)] = chT.iloc[0:, 0].as_matrix()
mediumChargeTotal = chargeMedium * numMedium
else:
mediumChargeTotal = np.zeros(d * 24)
##Large sized property##
proDataLarge = pd.DataFrame({
'Time': dS[0:8784],
'Data': (totalDemandLarge / 1000)
})
dataLarge = proDataLarge.set_index('Time')
d1, d2, largeS = month(m, dataLarge)
reLarge = pd.DataFrame(largeS['Data'], index=largeS.index)
reLarge.columns = ['Demand']
finalDataLarge = reLarge.iloc[0:(24 * d)]
chargeLarge = np.zeros(d * 24)
indLarge = finalDataLarge / numLarge
if numLarge != 0:
for i in range(1, (d + 1)):
intraLarge = pd.DataFrame(indLarge.iloc[((24 * i) - 24):(24 * i),
0].as_matrix(),
index=list(range(0, 24)),
columns=['Demand'])
if i == 1:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * 0.2))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
else:
chg, chT, gone = storageHeater(intraLarge, 'Large',
(23.1 * gone[0]))
chargeLarge[((24 * i) - 24):(24 *
i)] = chT.iloc[0:, 0].as_matrix()
largeChargeTotal = chargeLarge * numLarge
else:
largeChargeTotal = np.zeros(d * 24)
totCharge = (smallChargeTotal[0:] / 1000) + (
mediumChargeTotal[0:] / 1000) + (largeChargeTotal[0:] / 1000)
return totCharge