def test_setback_comfort_success_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
self.assertEqual(comfort_result, {})
self.assertEqual(setback_result, {})
def test_comfort_undercooling(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [200, 200, 200, 200, 200, 200, 200, 200, 200, 200]
append_data_to_datetime(IAT, IAT_temp)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost)
expected = {
'Problem': "Under-conditioning, thermostat cooling " + \
"setpoint is high.",
'Diagnostic': "More than 30 percent of the time, the cooling setpoint " + \
"during occupied hours was greater than 80F.",
'Recommendation': "Program your thermostats to decrease the cooling " + \
"setpoint to improve building comfort during occupied hours."
}
self.assertEqual(comfort_result, expected)
def test_comfort_undercooling(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [200, 200, 200, 200, 200, 200, 200, 200, 200, 200]
append_data_to_datetime(IAT, IAT_temp)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost)
expected = {
'Problem': "Under-conditioning, thermostat cooling " + \
"setpoint is high.",
'Diagnostic': "More than 30 percent of the time, the cooling setpoint " + \
"during occupied hours was greater than 80F.",
'Recommendation': "Program your thermostats to decrease the cooling " + \
"setpoint to improve building comfort during occupied hours."
}
self.assertEqual(comfort_result, expected)
def test_setback_comfort_success_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
self.assertEqual(comfort_result, {})
self.assertEqual(setback_result, {})
def test_comfort_overheating_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [80, 80, 80, 80, 80, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_heat_cost = (80 - 72) * 0.03 * 1 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Over-conditioning, thermostat heating " + \
"setpoint is high.",
'Diagnostic': "More than 30 percent of the time, the heating setpoint " + \
"during occupied hours was greater than 72F, a temperature that " + \
"is comfortable to most occupants.",
'Recommendation': "Program your thermostats to decrease the heating " + \
"setpoint during occupied hours.",
'Savings': round(exp_heat_cost, 2)
}
self.assertEqual(comfort_result, expected)
def test_setback_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_cool_cost = (76 - 50) * 0.03 * 0.5 * 0.07 * 1 * test_elec_cost
exp_heat_cost = (80 - 72) * 0.03 * 0.5 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Overly narrow separation between heating " + \
"and cooling setpoints.",
'Diagnostic': "During occupied hours, the cooling setpoint was lower " + \
"than 76F and the heating setpoint was greater than 72F.",
'Recommendation': "Adjust the heating and cooling setpoints so that " + \
"they differ by more than four degrees.",
'Savings': round(exp_cool_cost + exp_heat_cost, 2)
}
self.assertEqual(setback_result, expected)
def test_comfort_underheating_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [50, 50, 50, 50, 50, 50, 50, 50, 50, 50]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_cool_cost = (76 - 50) * 0.03 * 0.5 * 0.07 * 1 * test_elec_cost
exp_heat_cost = (80 - 72) * 0.03 * 0.5 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Under-conditioning - thermostat heating " + \
"setpoint is low.",
'Diagnostic': "For more than 30% of the time, the cooling setpoint " + \
"during occupied hours was less than 69 degrees F.",
'Recommendation': "Program thermostats to increase the heating " + \
"setpoint to improve building comfort during occupied hours."
}
self.assertEqual(comfort_result, expected)
def test_comfort_overheating_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [80, 80, 80, 80, 80, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_heat_cost = (80 - 72) * 0.03 * 1 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Over-conditioning, thermostat heating " + \
"setpoint is high.",
'Diagnostic': "More than 30 percent of the time, the heating setpoint " + \
"during occupied hours was greater than 72F, a temperature that " + \
"is comfortable to most occupants.",
'Recommendation': "Program your thermostats to decrease the heating " + \
"setpoint during occupied hours.",
'Savings': round(exp_heat_cost, 2)
}
self.assertEqual(comfort_result, expected)
def test_setback_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 100, 100, 100, 100, 100]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 80, 80, 80, 80, 80]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_cool_cost = (76 - 50) * 0.03 * 0.5 * 0.07 * 1 * test_elec_cost
exp_heat_cost = (80 - 72) * 0.03 * 0.5 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Overly narrow separation between heating " + \
"and cooling setpoints.",
'Diagnostic': "During occupied hours, the cooling setpoint was lower " + \
"than 76F and the heating setpoint was greater than 72F.",
'Recommendation': "Adjust the heating and cooling setpoints so that " + \
"they differ by more than four degrees.",
'Savings': round(exp_cool_cost + exp_heat_cost, 2)
}
self.assertEqual(setback_result, expected)
def test_comfort_underheating_HVAC(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [50, 50, 50, 50, 50, 50, 50, 50, 50, 50]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(IAT, IAT_temp)
HVAC = copy.deepcopy(base)
HVAC_stat = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
append_data_to_datetime(HVAC, HVAC_stat)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost, HVAC)
exp_cool_cost = (76 - 50) * 0.03 * 0.5 * 0.07 * 1 * test_elec_cost
exp_heat_cost = (80 - 72) * 0.03 * 0.5 * 0.31 * 1 * test_elec_cost
expected = {
'Problem': "Under-conditioning - thermostat heating " + \
"setpoint is low.",
'Diagnostic': "For more than 30% of the time, the cooling setpoint " + \
"during occupied hours was less than 69 degrees F.",
'Recommendation': "Program thermostats to increase the heating " + \
"setpoint to improve building comfort during occupied hours."
}
self.assertEqual(comfort_result, expected)
def test_comfort_overcooling(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 50, 50, 50, 50, 50]
append_data_to_datetime(IAT, IAT_temp)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost)
exp_cool_cost = (76 - 50) * 0.03 * 1 * 0.07 * 1 * test_elec_cost
expected = {
'Problem': "Over-conditioning, thermostat cooling setpoint is low",
'Diagnostic': "More than 30 percent of the time, the cooling setpoint " + \
"during occupied hours was lower than 75F, a temperature that " + \
"is comfortable to most occupants",
'Recommendation': "Program your thermostats to increase the cooling " + \
"setpoint during occupied hours.",
'Savings': round(exp_cool_cost, 2)
}
self.assertEqual(comfort_result, expected)
def test_comfort_overcooling(self):
a = datetime.datetime(2014, 1, 1, 0, 0, 0, 0)
b = datetime.datetime(2014, 1, 3, 6, 0, 0, 0)
#delta = 6 hours
base = set_up_datetimes(a, b, 21600)
DAT = copy.deepcopy(base)
DAT_temp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
append_data_to_datetime(DAT, DAT_temp)
IAT = copy.deepcopy(base)
IAT_temp = [50, 50, 50, 50, 50, 50, 50, 50, 50, 50]
append_data_to_datetime(IAT, IAT_temp)
# 24/7, no holidays
op_hours = [[0, 23], [1, 2, 3, 4, 5, 6, 7], []]
test_area = 5000
test_elec_cost = 10000
comfort_result, setback_result = comfort_and_setpoint(IAT, DAT, op_hours, \
test_area, test_elec_cost)
exp_cool_cost = (76 - 50) * 0.03 * 1 * 0.07 * 1 * test_elec_cost
expected = {
'Problem': "Over-conditioning, thermostat cooling setpoint is low",
'Diagnostic': "More than 30 percent of the time, the cooling setpoint " + \
"during occupied hours was lower than 75F, a temperature that " + \
"is comfortable to most occupants",
'Recommendation': "Program your thermostats to increase the cooling " + \
"setpoint during occupied hours.",
'Savings': round(exp_cool_cost, 2)
}
self.assertEqual(comfort_result, expected)
def execute(self):
# Called after User hits GO
"""
Calculates weather sensitivity using Spearman rank.
Also, outputs data points for energy signature scatter plot.
"""
self.out.log("Starting Day Time Temperature Analysis", logging.INFO)
self.out.log('@operating_sched'+str(self.operating_sched), logging.INFO)
self.out.log('@building_area'+str(self.building_area), logging.INFO)
self.out.log('@electricity_cost'+str(self.electricity_cost), logging.INFO)
# Query the database
zat_query = self.inp.get_query_sets('zat', exclude={'value':None},
wrap_for_merge=True,
group_by='minute')
dat_query = self.inp.get_query_sets('dat', exclude={'value':None},
wrap_for_merge=True,
group_by='minute')
oat_query = self.inp.get_query_sets('oat', exclude={'value':None},
wrap_for_merge=True,
group_by='minute')
status_query = self.inp.get_query_sets('hvacstatus', exclude={'value':None},
wrap_for_merge=True,
group_by='minute')
# Merge temperatures and the HVACStatus
merged_temperatures_status = self.inp.merge(zat_query, oat_query, dat_query,status_query)
# Get conversion factor
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
zat_unit = meta_topics['zat'][base_topic['zat'][0]]['unit']
dat_unit = meta_topics['dat'][base_topic['dat'][0]]['unit']
oat_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
#From the merged values make the arrays for the models
datetime_ZAT = []
datetime_DAT = []
datetime_OAT = []
datetime_HVACStatus = []
for line in merged_temperatures_status:
# Convert datetime str to datetime obj
datetimeObj = dt.datetime.strptime(line['time'],'%Y-%m-%d %H:%M')
# Convert ZAT
if zat_unit == 'celcius':
convertedZAT = cu.convertCelciusToFahrenheit(line['zat'][0])
elif zat_unit == 'kelvin':
convertedZAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['zat'][0]))
else:
convertedZAT = line['zat'][0]
#
datetime_ZAT.append((datetimeObj, convertedZAT))
# Convert DAT
if dat_unit == 'celcius':
convertedDAT = cu.convertCelciusToFahrenheit(line['dat'][0])
elif dat_unit == 'kelvin':
convertedDAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['dat'][0]))
else:
convertedDAT = line['dat'][0]
#
datetime_DAT.append((datetimeObj, convertedDAT))
# Convert OAT
if oat_unit == 'celcius':
convertedOAT = cu.convertCelciusToFahrenheit(line['oat'][0])
elif oat_unit == 'kelvin':
convertedOAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['oat'][0]))
else:
convertedOAT = line['oat'][0]
#
datetime_OAT.append((datetimeObj, convertedOAT))
#
datetime_HVACStatus.append((datetimeObj, line['hvacstatus'][0]))
# Apply the comfort_and_setpoint model.
comfort_flag, setback_flag = cs.comfort_and_setpoint(datetime_ZAT,
datetime_DAT,
self.operating_sched,
self.building_area,
self.electricity_cost,
datetime_HVACStatus)
if comfort_flag != {}:
self.out.insert_row('SensorSuitcaseHVAC', {
'analysis': 'Comfort Optimization',
'problem': comfort_flag['Problem'],
'diagnostic': comfort_flag['Diagnostic'],
'recommendation': comfort_flag['Recommendation'],
'savings': '${:.2f}'.format(comfort_flag['Savings'])
})
if setback_flag != {}:
self.out.insert_row('SensorSuitcaseHVAC', {
'analysis': 'Comfort Optimization',
'problem': setback_flag['Problem'],
'diagnostic': setback_flag['Diagnostic'],
'recommendation': setback_flag['Recommendation'],
'savings': '${:.2f}'.format(setback_flag['Savings'])
})
# Apply the economizer model.
economizer_flag = ecn.economizer(datetime_DAT,
datetime_OAT,
datetime_HVACStatus,
self.electricity_cost,
self.building_area)
if economizer_flag != {}:
self.out.insert_row('SensorSuitcaseHVAC', {
'analysis': 'Economizer Diagnostics',
'problem': economizer_flag['Problem'],
'diagnostic': economizer_flag['Diagnostic'],
'recommendation': economizer_flag['Recommendation'],
'savings': '${:.2f}'.format(economizer_flag['Savings'])
})
# Apply the setback_non_op model.
setback_nonop_flag = sb.setback_non_op(datetime_ZAT,
datetime_DAT,
self.operating_sched,
self.electricity_cost,
self.building_area,
datetime_HVACStatus)
if setback_nonop_flag != {}:
self.out.insert_row('SensorSuitcaseHVAC', {
'analysis': 'Setback During Unoccupied Hours',
'problem': setback_nonop_flag['Problem'],
'diagnostic': setback_nonop_flag['Diagnostic'],
'recommendation': setback_nonop_flag['Recommendation'],
'savings': '${:.2f}'.format(setback_nonop_flag['Savings'])
})
# Apply the short_cycling model.
shortcycling_flag = shc.short_cycling(datetime_HVACStatus,
self.electricity_cost)
if shortcycling_flag != {}:
self.out.insert_row('SensorSuitcaseHVAC', {
'analysis': 'Shortcycling',
'problem': shortcycling_flag['Problem'],
'diagnostic': shortcycling_flag['Diagnostic'],
'recommendation': shortcycling_flag['Recommendation'],
'savings': '${:.2f}'.format(shortcycling_flag['Savings'])
})
def execute(self):
# Called after User hits GO
"""
Calculates weather sensitivity using Spearman rank.
Also, outputs data points for energy signature scatter plot.
"""
self.out.log("Starting application: Sensor Suitcase HVAC.",
logging.INFO)
self.out.log('@operating_sched' + str(self.operating_sched),
logging.INFO)
self.out.log('@building_area' + str(self.building_area), logging.INFO)
self.out.log('@electricity_cost' + str(self.electricity_cost),
logging.INFO)
# Query the database
zat_query = self.inp.get_query_sets('zat',
exclude={'value': None},
wrap_for_merge=True,
group_by='minute')
dat_query = self.inp.get_query_sets('dat',
exclude={'value': None},
wrap_for_merge=True,
group_by='minute')
oat_query = self.inp.get_query_sets('oat',
exclude={'value': None},
wrap_for_merge=True,
group_by='minute')
status_query = self.inp.get_query_sets('hvacstatus',
exclude={'value': None},
wrap_for_merge=True,
group_by='minute')
# Merge temperatures and the HVACStatus
merged_temperatures_status = self.inp.merge(zat_query, oat_query,
dat_query, status_query)
self.out.log("Getting unit conversions.", logging.INFO)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
zat_unit = meta_topics['zat'][base_topic['zat'][0]]['unit']
self.out.log(
"Convert zone air temperatures from [{}] to [F].".format(zat_unit),
logging.INFO)
dat_unit = meta_topics['dat'][base_topic['dat'][0]]['unit']
self.out.log(
"Convert discharge air temperatures from [{}] to [F].".format(
dat_unit), logging.INFO)
oat_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
self.out.log(
"Convert outside air temperatures from [{}] to [F].".format(
oat_unit), logging.INFO)
#From the merged values make the arrays for the models
datetime_ZAT = []
datetime_DAT = []
datetime_OAT = []
datetime_HVACStatus = []
for line in merged_temperatures_status:
# Convert datetime str to datetime obj
# datetimeObj = dt.datetime.strptime(line['time'],'%Y-%m-%d %H:%M')
datetimeObj = line['time']
# Convert ZAT
if zat_unit == 'celcius':
convertedZAT = cu.convertCelciusToFahrenheit(line['zat'][0])
elif zat_unit == 'kelvin':
convertedZAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['zat'][0]))
else:
convertedZAT = line['zat'][0]
#
datetime_ZAT.append((datetimeObj, convertedZAT))
# Convert DAT
if dat_unit == 'celcius':
convertedDAT = cu.convertCelciusToFahrenheit(line['dat'][0])
elif dat_unit == 'kelvin':
convertedDAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['dat'][0]))
else:
convertedDAT = line['dat'][0]
#
datetime_DAT.append((datetimeObj, convertedDAT))
# Convert OAT
if oat_unit == 'celcius':
convertedOAT = cu.convertCelciusToFahrenheit(line['oat'][0])
elif oat_unit == 'kelvin':
convertedOAT = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(line['oat'][0]))
else:
convertedOAT = line['oat'][0]
#
datetime_OAT.append((datetimeObj, convertedOAT))
#
datetime_HVACStatus.append((datetimeObj, line['hvacstatus'][0]))
# Apply the comfort_and_setpoint model.
comfort_flag, setback_flag = cs.comfort_and_setpoint(
datetime_ZAT, datetime_DAT, self.operating_sched,
self.building_area, self.electricity_cost, datetime_HVACStatus)
if comfort_flag != {}:
self.out.insert_row(
'SensorSuitcaseHVAC', {
'analysis': 'Comfort Optimization',
'problem': comfort_flag['Problem'],
'diagnostic': comfort_flag['Diagnostic'],
'recommendation': comfort_flag['Recommendation'],
'savings': '${:.2f}'.format(comfort_flag['Savings'])
})
if setback_flag != {}:
self.out.insert_row(
'SensorSuitcaseHVAC', {
'analysis': 'Comfort Optimization',
'problem': setback_flag['Problem'],
'diagnostic': setback_flag['Diagnostic'],
'recommendation': setback_flag['Recommendation'],
'savings': '${:.2f}'.format(setback_flag['Savings'])
})
# Apply the economizer model.
economizer_flag = ecn.economizer(datetime_DAT, datetime_OAT,
datetime_HVACStatus,
self.electricity_cost,
self.building_area)
if economizer_flag != {}:
self.out.insert_row(
'SensorSuitcaseHVAC', {
'analysis': 'Economizer Diagnostics',
'problem': economizer_flag['Problem'],
'diagnostic': economizer_flag['Diagnostic'],
'recommendation': economizer_flag['Recommendation'],
'savings': '${:.2f}'.format(economizer_flag['Savings'])
})
# Apply the setback_non_op model.
setback_nonop_flag = sb.setback_non_op(datetime_ZAT, datetime_DAT,
self.operating_sched,
self.electricity_cost,
self.building_area,
datetime_HVACStatus)
if setback_nonop_flag != {}:
self.out.insert_row(
'SensorSuitcaseHVAC', {
'analysis': 'Setback During Unoccupied Hours',
'problem': setback_nonop_flag['Problem'],
'diagnostic': setback_nonop_flag['Diagnostic'],
'recommendation': setback_nonop_flag['Recommendation'],
'savings': '${:.2f}'.format(setback_nonop_flag['Savings'])
})
# Apply the short_cycling model.
shortcycling_flag = shc.short_cycling(datetime_HVACStatus,
self.electricity_cost,
self.building_area)
if shortcycling_flag != {}:
self.out.insert_row(
'SensorSuitcaseHVAC', {
'analysis': 'Shortcycling',
'problem': shortcycling_flag['Problem'],
'diagnostic': shortcycling_flag['Diagnostic'],
'recommendation': shortcycling_flag['Recommendation'],
'savings': '${:.2f}'.format(shortcycling_flag['Savings'])
})
