def forecastWork(modelDir, ind):
''' Run the model in its directory.'''
(cellCapacity, dischargeRate, chargeRate, cellQuantity, cellCost) = \
[float(ind[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'cellCost')]
demandCharge = float(ind['demandCharge'])
retailCost = float(ind['retailCost'])
battEff = float(ind.get("batteryEfficiency")) / 100.0
dodFactor = float(ind.get('dodFactor')) / 100.0
projYears = int(ind.get('projYears'))
batteryCycleLife = int(ind.get('batteryCycleLife'))
battCapacity = cellQuantity * float(ind['cellCapacity']) * dodFactor
o = {}
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['histCurve'].replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except:
raise Exception("CSV file is incorrect format.")
confidence = float(ind['confidence']) / 100
# ---------------------- MAKE PREDICTIONS ------------------------------- #
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
predictions = fc.neural_net_predictions(all_X, all_y)
dailyLoadPredictions = [
predictions[i:i + 24] for i in range(0, len(predictions), 24)
]
weather = df['tempc'][-8760:]
dailyWeatherPredictions = [
weather[i:i + 24] for i in range(0, len(weather), 24)
]
month = df['month'][-8760:]
dispatched = [False] * 365
# decide to implement VBAT every day for a year
VB_power, VB_energy = [], []
for i, (load24, temp24, m) in enumerate(
zip(dailyLoadPredictions, dailyWeatherPredictions, month)):
peak = max(load24)
if fc.shouldDispatchPS(peak, m, df, confidence):
dispatched[i] = True
vbp, vbe = fc.pulp24hrBattery(load24, dischargeRate * cellQuantity,
cellCapacity * cellQuantity, battEff)
VB_power.extend(vbp)
VB_energy.extend(vbe)
else:
VB_power.extend([0] * 24)
VB_energy.extend([0] * 24)
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = predictions
o['trainAccuracy'] = 0 #round(model.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = 0 #round(model.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False] * 365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([
b
for b in [i and j for (i, j) in zip(dispatched, shouldHaveDispatched)]
if b
])
falsePositive = len([
b for b in
[i and (not j) for (i, j) in zip(dispatched, shouldHaveDispatched)]
if b
])
falseNegative = len([
b for b in [(not i) and j
for (i, j) in zip(dispatched, shouldHaveDispatched)] if b
])
o['precision'] = round(
truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(
truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched if i])
o['MAE'] = round(
sum([
abs(l - m) / m * 100
for l, m in zip(predictions, list(all_y[-8760:]))
]) / 8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f + 1) for (s, f) in zip(start, finish)]
demand = list(all_y[-8760:])
peakDemand = [max(demand[s:f]) for s, f in monthHours]
demandAdj = [d + p for d, p in zip(demand, VB_power)]
peakDemandAdj = [max(demandAdj[s:f]) for s, f in monthHours]
discharges = [f if f < 0 else 0 for f in VB_power]
# Monthly Cost Comparison Table
o['monthlyDemand'] = peakDemand
o['monthlyDemandRed'] = peakDemandAdj
o['ps'] = [p - s for p, s in zip(peakDemand, peakDemandAdj)]
o['benefitMonthly'] = [x * demandCharge for x in o['ps']]
# Demand Before and After Storage Graph
o['demand'] = demand
o['demandAfterBattery'] = demandAdj
o['batteryDischargekW'] = VB_power
o['batteryDischargekWMax'] = max(VB_power)
batteryCycleLife = float(ind['batteryCycleLife'])
# Battery State of Charge Graph
# Turn dc's SoC into a percentage, with dodFactor considered.
o['batterySoc'] = SoC = [100 - (e / battCapacity * 100) for e in VB_energy]
# Estimate number of cyles the battery went through. Sums the percent of SoC.
cycleEquivalents = sum([
SoC[i] - SoC[i + 1]
for i, x in enumerate(SoC[:-1]) if SoC[i + 1] < SoC[i]
]) / 100.0
o['cycleEquivalents'] = cycleEquivalents
o['batteryLife'] = batteryCycleLife / cycleEquivalents
# Cash Flow Graph
# inserting battery efficiency only into the cashflow calculation
# cashFlowCurve is $ in from peak shaving minus the cost to recharge the battery every day of the year
cashFlowCurve = [sum(o['ps']) * demandCharge for year in range(projYears)]
cashFlowCurve.insert(0, -1 * cellCost *
cellQuantity) # insert initial investment
# simplePayback is also affected by the cost to recharge the battery every day of the year
o['SPP'] = (cellCost * cellQuantity) / (sum(o['ps']) * demandCharge)
o['netCashflow'] = cashFlowCurve
o['cumulativeCashflow'] = [
sum(cashFlowCurve[:i + 1]) for i, d in enumerate(cashFlowCurve)
]
o['NPV'] = npv(float(ind['discountRate']), cashFlowCurve)
battCostPerCycle = cellQuantity * cellCost / batteryCycleLife
lcoeTotCost = cycleEquivalents * retailCost + battCostPerCycle * cycleEquivalents
o['LCOE'] = lcoeTotCost / (cycleEquivalents * battCapacity)
# Other
o['startDate'] = '2011-01-01' # dc[0]['datetime'].isoformat()
o['stderr'] = ''
# Seemingly unimportant. Ask permission to delete.
o['stdout'] = 'Success'
o['months'] = [
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct",
"Nov", "Dec"
]
return o
def workForecast(modelDir, ind):
''' Run the model in its directory.'''
o = {}
# Grab data from CSV,
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['histCurve'].replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except:
raise Exception("CSV file is incorrect format.")
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
X_train, y_train = all_X[:-8760], all_y[:-8760]
clf = linear_model.SGDRegressor(max_iter=10000, tol=1e-4)
clf.fit(X_train, y_train)
# ---------------------- MAKE PREDICTIONS ------------------------------- #
X_test, y_test = all_X[-8760:], all_y[-8760:]
predictions = clf.predict(X_test)
dailyLoadPredictions = [
predictions[i:i + 24] for i in range(0, len(predictions), 24)
]
P_lower, P_upper, E_UL = vbat24hr(ind, df['tempc'][-8760:])
dailyPl = [P_lower[i:i + 24] for i in range(0, len(P_lower), 24)]
dailyPu = [P_upper[i:i + 24] for i in range(0, len(P_upper), 24)]
dailyEu = [E_UL[i:i + 24] for i in range(0, len(E_UL), 24)]
vbp, vbe = [], []
dispatched_d = [False] * 365
# Decide what days to dispatch
zipped = zip(dailyLoadPredictions, df['month'][-8760:], dailyPl, dailyPu,
dailyEu)
for i, (load, m, pl, pu, eu) in enumerate(zipped):
peak = max(load)
if fc.shouldDispatchPS(peak, m, df, float(ind['confidence']) / 100):
dispatched_d[i] = True
p, e = fc.pulp24hrVbat(ind, load, pl, pu, eu)
vbp.extend(p)
vbe.extend(e)
else:
vbp.extend([0] * 24)
vbe.extend([0] * 24)
### TESTING FOR ACCURACY ###
assert len(dailyPl) == 365
assert all([len(i) == 24 for i in dailyPl])
VB_power, VB_energy = vbp, vbe
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = list(clf.predict(X_test))
o['trainAccuracy'] = round(clf.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = round(clf.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False] * 365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([
b for b in
[i and j for (i, j) in zip(dispatched_d, shouldHaveDispatched)] if b
])
falsePositive = len([
b for b in
[i and (not j) for (i, j) in zip(dispatched_d, shouldHaveDispatched)]
if b
])
falseNegative = len([
b for b in [(not i) and j
for (i, j) in zip(dispatched_d, shouldHaveDispatched)] if b
])
o['confidence'] = ind['confidence']
o['precision'] = round(
truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(
truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched_d if i])
o['MAE'] = round(
sum([abs(l - m) / m * 100
for l, m in zip(predictions, list(y_test))]) / 8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
o['VBpower'], o['VBenergy'] = list(VB_power), list(VB_energy)
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f + 1) for (s, f) in zip(start, finish)]
demand = list(y_test)
peakDemand = [max(demand[s:f]) for s, f in monthHours]
energyMonthly = [sum(demand[s:f]) for s, f in monthHours]
demandAdj = [d + p for d, p in zip(demand, o['VBpower'])]
peakAdjustedDemand = [max(demandAdj[s:f]) for s, f in monthHours]
energyAdjustedMonthly = [sum(demandAdj[s:f]) for s, f in monthHours]
o['demand'] = demand
o['peakDemand'] = peakDemand
o['energyMonthly'] = energyMonthly
o['demandAdjusted'] = demandAdj
o['peakAdjustedDemand'] = peakAdjustedDemand
o['energyAdjustedMonthly'] = energyAdjustedMonthly
cellCost = float(ind['unitDeviceCost']) * float(ind['number_devices'])
eCost = float(ind['electricityCost'])
dCharge = float(ind['demandChargeCost'])
o['VBdispatch'] = [dal - d for dal, d in zip(demandAdj, demand)]
o['energyCost'] = [em * eCost for em in energyMonthly]
o['energyCostAdjusted'] = [eam * eCost for eam in energyAdjustedMonthly]
o['demandCharge'] = [peak * dCharge for peak in peakDemand]
o['demandChargeAdjusted'] = [
pad * dCharge for pad in o['peakAdjustedDemand']
]
o['totalCost'] = [
ec + dcm for ec, dcm in zip(o['energyCost'], o['demandCharge'])
]
o['totalCostAdjusted'] = [
eca + dca
for eca, dca in zip(o['energyCostAdjusted'], o['demandChargeAdjusted'])
]
o['savings'] = [
tot - tota for tot, tota in zip(o['totalCost'], o['totalCostAdjusted'])
]
annualEarnings = sum(o['savings']) - float(ind['unitUpkeepCost']) * float(
ind['number_devices'])
cashFlowList = [annualEarnings] * int(ind['projectionLength'])
cashFlowList.insert(0, -1 * cellCost)
o['NPV'] = np.npv(float(ind['discountRate']) / 100, cashFlowList)
o['SPP'] = cellCost / annualEarnings
o['netCashflow'] = cashFlowList
o['cumulativeCashflow'] = [
sum(cashFlowList[:i + 1]) for i, d in enumerate(cashFlowList)
]
o['stdout'] = 'Success'
return o
def work(modelDir, ind):
#print(ind)
''' Run the model in its directory.'''
# drop inverter efficiency
# drop DoD
(cellCapacity, dischargeRate, chargeRate, cellQuantity, cellCost) = \
[float(ind[x]) for x in ('cellCapacity', 'dischargeRate', 'chargeRate', 'cellQuantity', 'cellCost')]
battEff = float(ind.get("batteryEfficiency")) / 100.0
dodFactor = float(ind.get('dodFactor')) / 100.0
projYears = int(ind.get('projYears'))
batteryCycleLife = int(ind.get('batteryCycleLife'))
o = {}
try:
with open(pJoin(modelDir, 'hist.csv'), 'w') as f:
f.write(ind['historicalData'].replace('\r', ''))
df = pd.read_csv(pJoin(modelDir, 'hist.csv'), parse_dates=['dates'])
df['month'] = df['dates'].dt.month
df['dayOfYear'] = df['dates'].dt.dayofyear
assert df.shape[0] >= 26280 # must be longer than 3 years
assert df.shape[1] == 5
except:
raise Exception("CSV file is incorrect format.")
# retrieve goal
goal = ind['goal']
threshold = float(ind['transformerThreshold']) * 1000
confidence = float(ind['confidence']) / 100
# train model on previous data
all_X = fc.makeUsefulDf(df)
all_y = df['load']
X_train, y_train = all_X[:-8760], all_y[:-8760]
clf = linear_model.SGDRegressor(max_iter=10000, tol=1e-4)
clf.fit(X_train, y_train)
# ---------------------- MAKE PREDICTIONS ------------------------------- #
X_test, y_test = all_X[-8760:], all_y[-8760:]
# Collect data necessary for dispatch calculations
predictions = clf.predict(X_test)
dailyLoadPredictions = [
predictions[i:i + 24] for i in range(0, len(predictions), 24)
]
weather = df['tempc'][-8760:]
dailyWeatherPredictions = [
weather[i:i + 24] for i in range(0, len(weather), 24)
]
month = df['month'][-8760:]
dispatched = [False] * 365
# decide to implement VBAT every day for a year
VB_power, VB_energy = [], []
for i, (load24, temp24, m) in enumerate(
zip(dailyLoadPredictions, dailyWeatherPredictions, month)):
peak = max(load24)
if fc.shouldDispatchPS(peak, m, df, confidence):
dispatched[i] = True
vbp, vbe = fc.pulp24hrBattery(load24, dischargeRate * cellQuantity,
cellCapacity * cellQuantity, battEff)
VB_power.extend(vbp)
VB_energy.extend(vbe)
else:
VB_power.extend([0] * 24)
VB_energy.extend([0] * 24)
# -------------------- MODEL ACCURACY ANALYSIS -------------------------- #
o['predictedLoad'] = list(clf.predict(X_test))
o['trainAccuracy'] = round(clf.score(X_train, y_train) * 100, 2)
o['testAccuracy'] = round(clf.score(X_test, y_test) * 100, 2)
# PRECISION AND RECALL
maxDays = []
for month in range(1, 13):
test = df[df['month'] == month]
maxDays.append(test.loc[test['load'].idxmax()]['dayOfYear'])
shouldHaveDispatched = [False] * 365
for day in maxDays:
shouldHaveDispatched[day] = True
truePositive = len([
b
for b in [i and j for (i, j) in zip(dispatched, shouldHaveDispatched)]
if b
])
falsePositive = len([
b for b in
[i and (not j) for (i, j) in zip(dispatched, shouldHaveDispatched)]
if b
])
falseNegative = len([
b for b in [(not i) and j
for (i, j) in zip(dispatched, shouldHaveDispatched)] if b
])
o['precision'] = round(
truePositive / float(truePositive + falsePositive) * 100, 2)
o['recall'] = round(
truePositive / float(truePositive + falseNegative) * 100, 2)
o['number_of_dispatches'] = len([i for i in dispatched if i])
o['MAE'] = round(
sum([abs(l - m) / m * 100
for l, m in zip(predictions, list(y_test))]) / 8760., 2)
# ---------------------- FINANCIAL ANALYSIS ----------------------------- #
o['VBpower'], o['VBenergy'] = list(VB_power), list(VB_energy)
# Calculate monthHours
year = df[-8760:].copy()
year.reset_index(inplace=True)
year['hour'] = list(year.index)
start = list(year.groupby('month').first()['hour'])
finish = list(year.groupby('month').last()['hour'])
monthHours = [(s, f + 1) for (s, f) in zip(start, finish)]
demand = list(y_test)
peakDemand = [max(demand[s:f]) for s, f in monthHours]
energyMonthly = [sum(demand[s:f]) for s, f in monthHours]
demandAdj = [d + p for d, p in zip(demand, o['VBpower'])]
peakAdjustedDemand = [max(demandAdj[s:f]) for s, f in monthHours]
energyAdjustedMonthly = [sum(demandAdj[s:f]) for s, f in monthHours]
o['demand'] = demand
o['peakDemand'] = peakDemand
o['energyMonthly'] = energyMonthly
o['demandAdjusted'] = demandAdj
o['peakAdjustedDemand'] = peakAdjustedDemand
o['energyAdjustedMonthly'] = energyAdjustedMonthly
initInvestment = cellCost * cellQuantity
eCost = float(ind['electricityCost'])
dCharge = float(ind['demandChargeCost'])
o['VBdispatch'] = [dal - d for dal, d in zip(demandAdj, demand)]
o['energyCost'] = [em * eCost for em in energyMonthly]
o['energyCostAdjusted'] = [eam * eCost for eam in energyAdjustedMonthly]
o['demandCharge'] = [peak * dCharge for peak in peakDemand]
o['demandChargeAdjusted'] = [
pad * dCharge for pad in o['peakAdjustedDemand']
]
o['totalCost'] = [
ec + dcm for ec, dcm in zip(o['energyCost'], o['demandCharge'])
]
o['totalCostAdjusted'] = [
eca + dca
for eca, dca in zip(o['energyCostAdjusted'], o['demandChargeAdjusted'])
]
o['savings'] = [
tot - tota for tot, tota in zip(o['totalCost'], o['totalCostAdjusted'])
]
annualEarnings = sum(o['savings']) # - something!
cashFlowList = [annualEarnings] * int(ind['projectionLength'])
cashFlowList.insert(0, -1 * initInvestment)
o['NPV'] = np.npv(float(ind['discountRate']) / 100, cashFlowList)
o['SPP'] = initInvestment / annualEarnings
o['netCashflow'] = cashFlowList
o['cumulativeCashflow'] = [
sum(cashFlowList[:i + 1]) for i, d in enumerate(cashFlowList)
]
o['dataCheck'] = 'Threshold exceeded' if any(
[threshold > i for i in demandAdj]) and goal == 'deferral' else ''
o['transformerThreshold'] = threshold if goal == 'deferral' else None
o['stdout'] = 'Success'
return o