def execute(self):
"""
Calculates weather sensitivity using Spearman rank.
Also, outputs data points for energy signature scatter plot.
"""
self.out.log("Starting application: energy signature.", logging.INFO)
self.out.log("Querying database.", logging.INFO)
load_query = self.inp.get_query_sets('load', group_by='hour',
group_by_aggregation=Avg,
exclude={'value':None},
wrap_for_merge=True)
oat_query = self.inp.get_query_sets('oat', group_by='hour',
group_by_aggregation=Avg,
exclude={'value':None},
wrap_for_merge=True)
self.out.log("Getting unit conversions.", logging.INFO)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
load_unit = meta_topics['load'][base_topic['load'][0]]['unit']
self.out.log(
"Convert loads from [{}] to [kW].".format(load_unit),
logging.INFO
)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
temperature_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
self.out.log(
"Convert temperatures from [{}] to [F].".format(temperature_unit),
logging.INFO
)
#print('merge load', merged_load_oat)
load_values = []
oat_values = []
self.out.log("Pulling data from database.", logging.INFO)
merged_load_oat = self.inp.merge(load_query, oat_query)
for x in merged_load_oat:
if temperature_unit == 'celcius':
convertedTemp = cu.convertCelciusToFahrenheit(x['oat'][0])
elif temperature_unit == 'kelvin':
convertedTemp = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(x['oat'][0]))
else:
convertedTemp = x['oat'][0]
load_values.append(x['load'][0]*load_convertfactor)
oat_values.append(convertedTemp)
self.out.insert_row(LOAD_VS_OAT_TABLE_NAME, {
"oat": x['oat'][0],
"load": x['load'][0]
})
self.out.log("Calculating the Spearman rank.", logging.INFO)
#print(load_values)
#print(oat_values)
weather_sensitivity = findSpearmanRank(load_values, oat_values)
self.out.log("Adding weather sensitivity to table.", logging.INFO)
self.out.insert_row(WEATHER_SENSITIVITY_TABLE_NAME, {
"value": "{:.2f}".format(weather_sensitivity)
})
def run(self, current_time, points):
'''
Check algorithm pre-quisites and assemble data set for analysis.
'''
device_dict = {}
result = Results()
topics = self.inp.get_topics()
topic = topics[self.hws_temp_name][0]
current_time = self.inp.localize_sensor_time(topic, current_time)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
unit_dict = {}
if len(base_topic[self.oa_temp_name]) > 0:
unit_dict[self.oa_temp_name] = meta_topics[self.oa_temp_name][
base_topic[self.oa_temp_name][0]]['unit']
if len(base_topic[self.hws_temp_name]) > 0:
unit_dict[self.hws_temp_name] = meta_topics[self.hws_temp_name][
base_topic[self.hws_temp_name][0]]['unit']
if len(base_topic[self.hwr_temp_name]) > 0:
unit_dict[self.hwr_temp_name] = meta_topics[self.hwr_temp_name][
base_topic[self.hwr_temp_name][0]]['unit']
if len(base_topic[self.hw_stsp_name]) > 0:
unit_dict[self.hw_stsp_name] = meta_topics[self.hw_stsp_name][
base_topic[self.hw_stsp_name][0]]['unit']
for key, value in points.items():
device_dict[key.lower()] = value
oatemp_data = []
loop_dp_values = []
loop_dp_stpt_values = []
hw_pump_status_values = []
boiler_status_values = []
hw_pump_vfd_values = []
hw_stsp_values = []
hws_temp_values = []
hwr_temp_values = []
for key, value in device_dict.items():
if key.startswith(self.loop_dp_stpt_name) and value is not None:
loop_dp_stpt_values.append(value)
elif key.startswith(self.loop_dp_name) and value is not None:
loop_dp_values.append(value)
elif key.startswith(
self.hw_pump_status_name) and value is not None:
hw_pump_status_values.append(value)
elif key.startswith(self.boiler_status_name) and value is not None:
boiler_status_values.append(value)
elif key.startswith(self.hw_pump_vfd_name) and value is not None:
hw_pump_vfd_values.append(value)
elif key.startswith(self.hw_stsp_name) and value is not None:
hw_stsp_values.append(value)
elif key.startswith(self.hws_temp_name) and value is not None:
hws_temp_values.append(value)
elif key.startswith(self.hwr_temp_name) and value is not None:
hwr_temp_values.append(value)
elif (key.startswith(self.oa_temp_name) #OAT
and value is not None):
oatemp_data.append(value)
if 'celcius' or 'kelvin' in unit_dict.values:
if self.oa_temp_name in unit_dict:
if unit_dict[self.oa_temp_name] == 'celcius':
oatemp_data = cu.convertCelciusToFahrenheit(oatemp_data)
elif unit_dict[self.oa_temp_name] == 'kelvin':
oatemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(oatemp_data))
if self.hw_stsp_name in unit_dict:
if unit_dict[self.hw_stsp_name] == 'celcius':
hw_stsp_values = cu.convertCelciusToFahrenheit(
hw_stsp_values)
elif unit_dict[self.hw_stsp_name] == 'kelvin':
hw_stsp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hw_stsp_values))
if self.hw_stsp_name in unit_dict:
if unit_dict[self.hws_temp_name] == 'celcius':
hws_temp_values = cu.convertCelciusToFahrenheit(
hws_temp_values)
elif unit_dict[self.hws_temp_name] == 'kelvin':
hws_temp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hws_temp_values))
if self.hwr_temp_name in unit_dict:
if unit_dict[self.hwr_temp_name] == 'celcius':
hwr_temp_values = cu.convertCelciusToFahrenheit(
hwr_temp_values)
elif unit_dict[self.hwr_temp_name] == 'kelvin':
hwr_temp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hwr_temp_values))
oatemp = hw_stsp_temp = hws_temp = hwr_temp = None
loop_dp = loop_dp_stpt = None
hw_pump_status = boiler_status = None
hw_pump_vfd = None
if oatemp_data:
oatemp = (sum(oatemp_data) / len(oatemp_data))
if hw_stsp_values:
hw_stsp_temp = (sum(hw_stsp_values) / len(hw_stsp_values))
if hws_temp_values:
hws_temp = (sum(hws_temp_values) / len(hws_temp_values))
if hwr_temp_values:
hwr_temp = (sum(hwr_temp_values) / len(hwr_temp_values))
if loop_dp_stpt_values:
loop_dp_stpt = (sum(loop_dp_stpt_values) /
len(loop_dp_stpt_values))
if loop_dp_values:
loop_dp = (sum(loop_dp_values) / len(loop_dp_values))
if hw_pump_status_values:
hw_pump_status = (sum(hw_pump_status_values) /
len(hw_pump_status_values))
if boiler_status_values:
boiler_status = (sum(boiler_status_values) /
len(boiler_status_values))
if hw_pump_vfd_values:
hw_pump_vfd = (sum(hw_pump_vfd_values) / len(hw_pump_vfd_values))
out_data = {
'datetime': str(current_time),
}
if not oatemp is None:
out_data['OutdoorAirTemperature'] = oatemp
if not hws_temp is None:
out_data['HotWaterSupplyTemperature'] = hws_temp
if not hwr_temp is None:
out_data['HotWaterReturnTemperature'] = hwr_temp
if not hw_stsp_temp is None:
out_data['HotWaterTemperatureSetPoint'] = hw_stsp_temp
if not loop_dp is None:
out_data['LoopDifferentialPressure'] = loop_dp
if not loop_dp_stpt is None:
out_data['LoopDifferentialPressureSetPoint'] = loop_dp_stpt
if not hw_pump_status is None:
out_data['PumpStatus'] = hw_pump_status
if not boiler_status is None:
out_data['BoilerStatus'] = boiler_status
if not hw_pump_vfd is None:
out_data['HotWaterPumpVfd'] = hw_pump_vfd
result.insert_table_row(self.table_name, out_data)
return result
def run(self, current_time, points):
"""
Main run method that is called by the DrivenBaseClass.
"""
device_dict = {}
result = Results()
topics = self.inp.get_topics()
topic = topics[self.oa_temp_name][0]
current_time = self.inp.localize_sensor_time(topic, current_time)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
unit_dict = {
self.oa_temp_name: meta_topics[self.oa_temp_name][base_topic[self.oa_temp_name][0]]['unit']
}
if len(base_topic[self.ma_temp_name]) > 0:
unit_dict[self.ma_temp_name] = meta_topics[self.ma_temp_name][base_topic[self.ma_temp_name][0]]['unit']
if len(base_topic[self.ra_temp_name]) > 0:
unit_dict[self.ra_temp_name] = meta_topics[self.ra_temp_name][base_topic[self.ra_temp_name][0]]['unit']
if len(base_topic[self.da_temp_name]) > 0:
unit_dict[self.da_temp_name] = meta_topics[self.da_temp_name][base_topic[self.da_temp_name][0]]['unit']
if len(base_topic[self.da_temp_setpoint_name]) > 0:
unit_dict[self.da_temp_setpoint_name] = \
meta_topics[self.da_temp_setpoint_name][base_topic[self.da_temp_setpoint_name][0]]['unit']
for key, value in points.items():
device_dict[key.lower()] = value
damper_data = []
oatemp_data = []
matemp_data = []
ratemp_data = []
datemp_data = []
datemp_stpt_data = []
ccv_data = []
fan_speedcmd_data = []
fan_status_data = []
hcv_data = []
return_fan_speedcmd_data = []
occ_data = []
discharge_sp_data = []
staticpressure_sp_data = []
for key, value in device_dict.items():
if (key.startswith(self.outdoor_damper_name) #Damper
and value is not None):
damper_data.append(value)
elif (key.startswith(self.oa_temp_name) #OAT
and value is not None):
oatemp_data.append(value)
elif (key.startswith(self.ma_temp_name) #MAT
and value is not None):
matemp_data.append(value)
elif (key.startswith(self.ra_temp_name) #RAT
and value is not None):
ratemp_data.append(value)
elif (key.startswith(self.da_temp_name) #DAT
and value is not None):
datemp_data.append(value)
elif (key.startswith(self.da_temp_setpoint_name) #DAT SetPoint
and value is not None):
datemp_stpt_data.append(value)
elif (key.startswith(self.cc_valve_name) #CoolCoilValvePos
and value is not None):
ccv_data.append(value)
elif (key.startswith(self.hc_valve_name) #HeatCoilValvePos
and value is not None):
hcv_data.append(value)
elif (key.startswith(self.fan_speedcmd_name) #Fan speed
and value is not None):
fan_speedcmd_data.append(value)
elif (key.startswith(self.fan_status_name) #Fan status
and value is not None):
fan_status_data.append(value)
elif (key.startswith(self.returnfan_speedcmd_name) #returnfan_speedcmd_name
and value is not None):
return_fan_speedcmd_data.append(value)
elif (key.startswith(self.occ_name) #Occupancy
and value is not None):
occ_data.append(value)
elif (key.startswith(self.discharge_sp_name) #discharge_sp_data
and value is not None):
discharge_sp_data.append(value)
elif (key.startswith(self.sp_setpoint_name) #staticpressure_sp_data
and value is not None):
staticpressure_sp_data.append(value)
if not oatemp_data:
return result
if 'celcius' or 'kelvin' in unit_dict.values:
if unit_dict[self.oa_temp_name] == 'celcius':
oatemp_data = cu.convertCelciusToFahrenheit(oatemp_data)
elif unit_dict[self.oa_temp_name] == 'kelvin':
oatemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(oatemp_data))
#if self.ma_temp_name in unit_dict:
if unit_dict[self.ma_temp_name] == 'celcius':
matemp_data = cu.convertCelciusToFahrenheit(matemp_data)
elif unit_dict[self.ma_temp_name] == 'kelvin':
matemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(matemp_data))
if self.ra_temp_name in unit_dict:
if unit_dict[self.ra_temp_name] == 'celcius':
ratemp_data = cu.convertCelciusToFahrenheit(ratemp_data)
elif unit_dict[self.ra_temp_name] == 'kelvin':
ratemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(ratemp_data))
if self.da_temp_name in unit_dict:
if unit_dict[self.da_temp_name] == 'celcius':
datemp_data = cu.convertCelciusToFahrenheit(datemp_data)
elif unit_dict[self.da_temp_name] == 'kelvin':
datemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(datemp_data))
if self.da_temp_setpoint_name in unit_dict:
if unit_dict[self.da_temp_setpoint_name] == 'celcius':
datemp_stpt_data = cu.convertCelciusToFahrenheit(datemp_stpt_data)
elif unit_dict[self.da_temp_setpoint_name] == 'kelvin':
datemp_stpt_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(datemp_stpt_data))
oatemp = (sum(oatemp_data) / len(oatemp_data))
matemp = (sum(matemp_data) / len(matemp_data))
matemp_data = None
ratemp = datemp = datemp_stpt = None
fanstatus = fanspeed = outdoor_damper = ccv = hcv = None
oaf = None
return_fan_speedcmd = None
occupancy = None
discharge_sp = staticpressure_sp = None
if matemp_data:
matemp = (sum(matemp_data) / len(matemp_data))
if ratemp_data:
ratemp = (sum(ratemp_data) / len(ratemp_data))
if datemp_data:
datemp = (sum(datemp_data) / len(datemp_data))
if datemp_stpt_data:
datemp_stpt = (sum(datemp_stpt_data) / len(datemp_stpt_data))
if fan_status_data:
fanstatus = (sum(fan_status_data) / len(fan_status_data))
if fan_speedcmd_data:
fanspeed = (sum(fan_speedcmd_data) / len(fan_speedcmd_data))
if damper_data:
outdoor_damper = (sum(damper_data) / len(damper_data))
if ccv_data:
ccv = (sum(ccv_data) / len(ccv_data))
if hcv_data:
hcv = (sum(hcv_data) / len(hcv_data))
if matemp_data and ratemp_data:
denominator = oatemp - ratemp
if denominator == 0:
oaf = 0
else:
oaf = (matemp - ratemp) / denominator
if oaf > 1:
oaf = 1
elif oaf < 0:
oaf = 0
oaf *= 100
if return_fan_speedcmd_data:
return_fan_speedcmd = (sum(return_fan_speedcmd_data) / len(return_fan_speedcmd_data))
if occ_data:
occupancy = (sum(occ_data) / len(occ_data))*100
if discharge_sp_data:
discharge_sp = (sum(discharge_sp_data) / len(discharge_sp_data))
if staticpressure_sp_data:
staticpressure_sp = (sum(staticpressure_sp_data) / len(staticpressure_sp_data))
out_data = {
'datetime': str(current_time),
'OutdoorAirTemperature': oatemp,
'MixedAirTemperature': None if not matemp else matemp,
'ReturnAirTemperature': None if ratemp is None else ratemp,
'DischargeAirTemperature': None if datemp is None else datemp,
'DischargeAirTemperatureSetPoint': None if datemp_stpt is None else datemp_stpt,
'SupplyFanStatus': None if fanstatus is None else fanstatus,
'SupplyFanSpeed': None if fanspeed is None else fanspeed,
'OutdoorDamper': None if outdoor_damper is None else outdoor_damper,
'CCV': None if ccv is None else ccv,
'HCV': None if hcv is None else hcv,
'OutdoorAirFraction': None if oaf is None else oaf,
'ReturnFanSpeed': None if return_fan_speedcmd is None else return_fan_speedcmd,
'OccupancyMode': None if occupancy is None else occupancy,
'DuctStaticPressure': None if discharge_sp is None else discharge_sp,
'DuctStaticPressureSetPoint': None if staticpressure_sp is None else staticpressure_sp,
}
result.insert_table_row('AhuEcam', out_data)
return result
def run(self, current_time, points):
"""
Main run method that is called by the DrivenBaseClass.
"""
device_dict = {}
result = Results()
topics = self.inp.get_topics()
topic = topics[self.oa_temp_name][0]
current_time = self.inp.localize_sensor_time(topic, current_time)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
unit_dict = {
self.oa_temp_name:
meta_topics[self.oa_temp_name][base_topic[self.oa_temp_name][0]]
['unit']
}
if len(base_topic[self.ma_temp_name]) > 0:
unit_dict[self.ma_temp_name] = meta_topics[self.ma_temp_name][
base_topic[self.ma_temp_name][0]]['unit']
if len(base_topic[self.ra_temp_name]) > 0:
unit_dict[self.ra_temp_name] = meta_topics[self.ra_temp_name][
base_topic[self.ra_temp_name][0]]['unit']
if len(base_topic[self.da_temp_name]) > 0:
unit_dict[self.da_temp_name] = meta_topics[self.da_temp_name][
base_topic[self.da_temp_name][0]]['unit']
if len(base_topic[self.da_temp_setpoint_name]) > 0:
unit_dict[self.da_temp_setpoint_name] = \
meta_topics[self.da_temp_setpoint_name][base_topic[self.da_temp_setpoint_name][0]]['unit']
for key, value in points.items():
device_dict[key.lower()] = value
damper_data = []
oatemp_data = []
matemp_data = []
ratemp_data = []
datemp_data = []
datemp_stpt_data = []
ccv_data = []
fan_speedcmd_data = []
fan_status_data = []
hcv_data = []
return_fan_speedcmd_data = []
occ_data = []
discharge_sp_data = []
staticpressure_sp_data = []
for key, value in device_dict.items():
if (key.startswith(self.outdoor_damper_name) #Damper
and value is not None):
damper_data.append(value)
elif (key.startswith(self.oa_temp_name) #OAT
and value is not None):
oatemp_data.append(value)
elif (key.startswith(self.ma_temp_name) #MAT
and value is not None):
matemp_data.append(value)
elif (key.startswith(self.ra_temp_name) #RAT
and value is not None):
ratemp_data.append(value)
elif (key.startswith(self.da_temp_name) #DAT
and value is not None):
datemp_data.append(value)
elif (key.startswith(self.da_temp_setpoint_name) #DAT SetPoint
and value is not None):
datemp_stpt_data.append(value)
elif (key.startswith(self.cc_valve_name) #CoolCoilValvePos
and value is not None):
ccv_data.append(value)
elif (key.startswith(self.hc_valve_name) #HeatCoilValvePos
and value is not None):
hcv_data.append(value)
elif (key.startswith(self.fan_speedcmd_name) #Fan speed
and value is not None):
fan_speedcmd_data.append(value)
elif (key.startswith(self.fan_status_name) #Fan status
and value is not None):
fan_status_data.append(value)
elif (key.startswith(
self.returnfan_speedcmd_name) #returnfan_speedcmd_name
and value is not None):
return_fan_speedcmd_data.append(value)
elif (key.startswith(self.occ_name) #Occupancy
and value is not None):
occ_data.append(value)
elif (key.startswith(self.discharge_sp_name) #discharge_sp_data
and value is not None):
discharge_sp_data.append(value)
elif (key.startswith(
self.sp_setpoint_name) #staticpressure_sp_data
and value is not None):
staticpressure_sp_data.append(value)
if not oatemp_data:
return result
if 'celcius' or 'kelvin' in unit_dict.values:
if unit_dict[self.oa_temp_name] == 'celcius':
oatemp_data = cu.convertCelciusToFahrenheit(oatemp_data)
elif unit_dict[self.oa_temp_name] == 'kelvin':
oatemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(oatemp_data))
#if self.ma_temp_name in unit_dict:
if unit_dict[self.ma_temp_name] == 'celcius':
matemp_data = cu.convertCelciusToFahrenheit(matemp_data)
elif unit_dict[self.ma_temp_name] == 'kelvin':
matemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(matemp_data))
if self.ra_temp_name in unit_dict:
if unit_dict[self.ra_temp_name] == 'celcius':
ratemp_data = cu.convertCelciusToFahrenheit(ratemp_data)
elif unit_dict[self.ra_temp_name] == 'kelvin':
ratemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(ratemp_data))
if self.da_temp_name in unit_dict:
if unit_dict[self.da_temp_name] == 'celcius':
datemp_data = cu.convertCelciusToFahrenheit(datemp_data)
elif unit_dict[self.da_temp_name] == 'kelvin':
datemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(datemp_data))
if self.da_temp_setpoint_name in unit_dict:
if unit_dict[self.da_temp_setpoint_name] == 'celcius':
datemp_stpt_data = cu.convertCelciusToFahrenheit(
datemp_stpt_data)
elif unit_dict[self.da_temp_setpoint_name] == 'kelvin':
datemp_stpt_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(datemp_stpt_data))
oatemp = (sum(oatemp_data) / len(oatemp_data))
matemp = (sum(matemp_data) / len(matemp_data))
matemp_data = None
ratemp = datemp = datemp_stpt = None
fanstatus = fanspeed = outdoor_damper = ccv = hcv = None
oaf = None
return_fan_speedcmd = None
occupancy = None
discharge_sp = staticpressure_sp = None
if matemp_data:
matemp = (sum(matemp_data) / len(matemp_data))
if ratemp_data:
ratemp = (sum(ratemp_data) / len(ratemp_data))
if datemp_data:
datemp = (sum(datemp_data) / len(datemp_data))
if datemp_stpt_data:
datemp_stpt = (sum(datemp_stpt_data) / len(datemp_stpt_data))
if fan_status_data:
fanstatus = (sum(fan_status_data) / len(fan_status_data))
if fan_speedcmd_data:
fanspeed = (sum(fan_speedcmd_data) / len(fan_speedcmd_data))
if damper_data:
outdoor_damper = (sum(damper_data) / len(damper_data))
if ccv_data:
ccv = (sum(ccv_data) / len(ccv_data))
if hcv_data:
hcv = (sum(hcv_data) / len(hcv_data))
if matemp_data and ratemp_data:
denominator = oatemp - ratemp
if denominator == 0:
oaf = 0
else:
oaf = (matemp - ratemp) / denominator
if oaf > 1:
oaf = 1
elif oaf < 0:
oaf = 0
oaf *= 100
if return_fan_speedcmd_data:
return_fan_speedcmd = (sum(return_fan_speedcmd_data) /
len(return_fan_speedcmd_data))
if occ_data:
occupancy = (sum(occ_data) / len(occ_data)) * 100
if discharge_sp_data:
discharge_sp = (sum(discharge_sp_data) / len(discharge_sp_data))
if staticpressure_sp_data:
staticpressure_sp = (sum(staticpressure_sp_data) /
len(staticpressure_sp_data))
out_data = {
'datetime':
str(current_time),
'OutdoorAirTemperature':
oatemp,
'MixedAirTemperature':
None if not matemp else matemp,
'ReturnAirTemperature':
None if ratemp is None else ratemp,
'DischargeAirTemperature':
None if datemp is None else datemp,
'DischargeAirTemperatureSetPoint':
None if datemp_stpt is None else datemp_stpt,
'SupplyFanStatus':
None if fanstatus is None else fanstatus,
'SupplyFanSpeed':
None if fanspeed is None else fanspeed,
'OutdoorDamper':
None if outdoor_damper is None else outdoor_damper,
'CCV':
None if ccv is None else ccv,
'HCV':
None if hcv is None else hcv,
'OutdoorAirFraction':
None if oaf is None else oaf,
'ReturnFanSpeed':
None if return_fan_speedcmd is None else return_fan_speedcmd,
'OccupancyMode':
None if occupancy is None else occupancy,
'DuctStaticPressure':
None if discharge_sp is None else discharge_sp,
'DuctStaticPressureSetPoint':
None if staticpressure_sp is None else staticpressure_sp,
}
result.insert_table_row('AhuEcam', out_data)
return result
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)
# Gather loads and outside air temperatures. Reduced to an hourly average
load_query = self.inp.get_query_sets('load', group_by='hour',
group_by_aggregation=Avg,
exclude={'value':None},
wrap_for_merge=True)
oat_query = self.inp.get_query_sets('oat', group_by='hour',
group_by_aggregation=Avg,
exclude={'value':None},
wrap_for_merge=True)
# Get conversion factor
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
load_unit = meta_topics['load'][base_topic['load'][0]]['unit']
temperature_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
load_convertfactor = cu.conversiontoKWH(load_unit)
# Match the values by timestamp
merged_load_oat = self.inp.merge(load_query, oat_query)
load_values = []
oat_values = []
datetime_values = []
for x in merged_load_oat:
if temperature_unit == 'celcius':
convertedTemp = cu.convertCelciusToFahrenheit(x['oat'][0])
elif temperature_unit == 'kelvin':
convertedTemp = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(x['oat'][0]))
else:
convertedTemp = x['oat'][0]
load_values.append(x['load'][0] * load_convertfactor) #Converted to kWh
oat_values.append(convertedTemp)
datetime_values.append(dt.datetime.strptime(x['time'],'%Y-%m-%d %H'))
indexList = {}
indexList['trainingStart'] = ttow.findDateIndex(datetime_values, self.baseline_start)
self.out.log('@trainingStart '+str(indexList['trainingStart']), logging.INFO)
indexList['trainingStop'] = ttow.findDateIndex(datetime_values, self.baseline_stop)
self.out.log('@trainingStop '+str(indexList['trainingStop']), logging.INFO)
indexList['predictStart'] = ttow.findDateIndex(datetime_values, self.savings_start)
self.out.log('@predictStart '+str(indexList['predictStart']), logging.INFO)
indexList['predictStop'] = ttow.findDateIndex(datetime_values, self.savings_stop)
self.out.log('@predictStop '+str(indexList['predictStop']), logging.INFO)
for indx in indexList.keys():
if indexList[indx] == None:
self.out.log("Date not found in the datelist", logging.WARNING)
# Break up data into training and prediction periods.
timesTrain = datetime_values[indexList['trainingStart']:indexList['trainingStop']]
timesPredict = datetime_values[indexList['predictStart']:indexList['predictStop']]
valsTrain = load_values[indexList['trainingStart']:indexList['trainingStop']]
valsActual = load_values[indexList['predictStart']:indexList['predictStop']]
oatsTrain = oat_values[indexList['trainingStart']:indexList['trainingStop']]
oatsPredict = oat_values[indexList['predictStart']:indexList['predictStop']]
# Generate other information needed for model.
timeStepMinutes = (timesTrain[1] - timesTrain[0]).total_seconds()/60
# TODO: Should this be calculated in the utility function
binCt = 6 # TODO: Allow caller to pass this in as an argument.
# Form the temperature-time-of-week model.
self.out.log("Starting baseline model", logging.INFO)
ttowModel = ttow.formModel(timesTrain,
oatsTrain,
valsTrain,
timeStepMinutes,
binCt)
# Apply the model.
self.out.log("Applying baseline model", logging.INFO)
valsPredict = ttow.applyModel(ttowModel, timesPredict, oatsPredict)
# Output for scatter plot
prevSum = 0
for ctr in range(len(timesPredict)):
# Calculate cumulative savings.
prevSum += (valsPredict[ctr] - valsActual[ctr])
self.out.insert_row("DayTimeTemperatureModel", {
"datetimeValues": timesPredict[ctr],
"measured": valsActual[ctr],
"predicted": valsPredict[ctr],
"cumulativeSum": prevSum
})
def execute(self):
"""
Calculates weather sensitivity using Spearman rank.
Also, outputs data points for energy signature scatter plot.
"""
self.out.log("Starting application: energy signature.", logging.INFO)
self.out.log("Querying database.", logging.INFO)
load_query = self.inp.get_query_sets('load',
group_by='hour',
group_by_aggregation=Avg,
exclude={'value': None},
wrap_for_merge=True)
oat_query = self.inp.get_query_sets('oat',
group_by='hour',
group_by_aggregation=Avg,
exclude={'value': None},
wrap_for_merge=True)
self.out.log("Getting unit conversions.", logging.INFO)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
load_unit = meta_topics['load'][base_topic['load'][0]]['unit']
self.out.log("Convert loads from [{}] to [kW].".format(load_unit),
logging.INFO)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
temperature_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
self.out.log(
"Convert temperatures from [{}] to [F].".format(temperature_unit),
logging.INFO)
#print('merge load', merged_load_oat)
load_values = []
oat_values = []
self.out.log("Pulling data from database.", logging.INFO)
merged_load_oat = self.inp.merge(load_query, oat_query)
for x in merged_load_oat:
if temperature_unit == 'celcius':
convertedTemp = cu.convertCelciusToFahrenheit(x['oat'][0])
elif temperature_unit == 'kelvin':
convertedTemp = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(x['oat'][0]))
else:
convertedTemp = x['oat'][0]
load_values.append(x['load'][0] * load_convertfactor)
oat_values.append(convertedTemp)
self.out.insert_row(LOAD_VS_OAT_TABLE_NAME, {
"oat": x['oat'][0],
"load": x['load'][0]
})
self.out.log("Calculating the Spearman rank.", logging.INFO)
#print(load_values)
#print(oat_values)
weather_sensitivity = findSpearmanRank(load_values, oat_values)
self.out.log("Adding weather sensitivity to table.", logging.INFO)
self.out.insert_row(WEATHER_SENSITIVITY_TABLE_NAME,
{"value": "{:.2f}".format(weather_sensitivity)})
def run(self, current_time, points):
'''
Check algorithm pre-quisites and assemble data set for analysis.
'''
device_dict = {}
result = Results()
topics = self.inp.get_topics()
topic = topics[self.hws_temp_name][0]
current_time = self.inp.localize_sensor_time(topic, current_time)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
unit_dict = {}
if len(base_topic[self.oa_temp_name]) > 0:
unit_dict[self.oa_temp_name] = meta_topics[self.oa_temp_name][base_topic[self.oa_temp_name][0]]['unit']
if len(base_topic[self.hws_temp_name]) > 0:
unit_dict[self.hws_temp_name] = meta_topics[self.hws_temp_name][base_topic[self.hws_temp_name][0]]['unit']
if len(base_topic[self.hwr_temp_name]) > 0:
unit_dict[self.hwr_temp_name] = meta_topics[self.hwr_temp_name][base_topic[self.hwr_temp_name][0]]['unit']
if len(base_topic[self.hw_stsp_name]) > 0:
unit_dict[self.hw_stsp_name] = meta_topics[self.hw_stsp_name][base_topic[self.hw_stsp_name][0]]['unit']
for key, value in points.items():
device_dict[key.lower()] = value
oatemp_data = []
loop_dp_values = []
loop_dp_stpt_values = []
hw_pump_status_values = []
boiler_status_values = []
hw_pump_vfd_values = []
hw_stsp_values = []
hws_temp_values = []
hwr_temp_values = []
for key, value in device_dict.items():
if key.startswith(self.loop_dp_stpt_name) and value is not None:
loop_dp_stpt_values.append(value)
elif key.startswith(self.loop_dp_name) and value is not None:
loop_dp_values.append(value)
elif key.startswith(self.hw_pump_status_name) and value is not None:
hw_pump_status_values.append(value)
elif key.startswith(self.boiler_status_name) and value is not None:
boiler_status_values.append(value)
elif key.startswith(self.hw_pump_vfd_name) and value is not None:
hw_pump_vfd_values.append(value)
elif key.startswith(self.hw_stsp_name) and value is not None:
hw_stsp_values.append(value)
elif key.startswith(self.hws_temp_name) and value is not None:
hws_temp_values.append(value)
elif key.startswith(self.hwr_temp_name) and value is not None:
hwr_temp_values.append(value)
elif (key.startswith(self.oa_temp_name) #OAT
and value is not None):
oatemp_data.append(value)
if 'celcius' or 'kelvin' in unit_dict.values:
if self.oa_temp_name in unit_dict:
if unit_dict[self.oa_temp_name] == 'celcius':
oatemp_data = cu.convertCelciusToFahrenheit(oatemp_data)
elif unit_dict[self.oa_temp_name] == 'kelvin':
oatemp_data = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(oatemp_data))
if self.hw_stsp_name in unit_dict:
if unit_dict[self.hw_stsp_name] == 'celcius':
hw_stsp_values = cu.convertCelciusToFahrenheit(hw_stsp_values)
elif unit_dict[self.hw_stsp_name] == 'kelvin':
hw_stsp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hw_stsp_values))
if self.hw_stsp_name in unit_dict:
if unit_dict[self.hws_temp_name] == 'celcius':
hws_temp_values = cu.convertCelciusToFahrenheit(hws_temp_values)
elif unit_dict[self.hws_temp_name] == 'kelvin':
hws_temp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hws_temp_values))
if self.hwr_temp_name in unit_dict:
if unit_dict[self.hwr_temp_name] == 'celcius':
hwr_temp_values = cu.convertCelciusToFahrenheit(hwr_temp_values)
elif unit_dict[self.hwr_temp_name] == 'kelvin':
hwr_temp_values = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(hwr_temp_values))
oatemp = hw_stsp_temp = hws_temp = hwr_temp = None
loop_dp = loop_dp_stpt = None
hw_pump_status = boiler_status = None
hw_pump_vfd = None
if oatemp_data:
oatemp = (sum(oatemp_data) / len(oatemp_data))
if hw_stsp_values:
hw_stsp_temp = (sum(hw_stsp_values) / len(hw_stsp_values))
if hws_temp_values:
hws_temp = (sum(hws_temp_values) / len(hws_temp_values))
if hwr_temp_values:
hwr_temp = (sum(hwr_temp_values) / len(hwr_temp_values))
if loop_dp_stpt_values:
loop_dp_stpt = (sum(loop_dp_stpt_values) / len(loop_dp_stpt_values))
if loop_dp_values:
loop_dp = (sum(loop_dp_values) / len(loop_dp_values))
if hw_pump_status_values:
hw_pump_status = (sum(hw_pump_status_values) / len(hw_pump_status_values))
if boiler_status_values:
boiler_status = (sum(boiler_status_values) / len(boiler_status_values))
if hw_pump_vfd_values:
hw_pump_vfd = (sum(hw_pump_vfd_values) / len(hw_pump_vfd_values))
out_data = {
'datetime': str(current_time),
}
if not oatemp is None:
out_data['OutdoorAirTemperature'] = oatemp
if not hws_temp is None:
out_data['HotWaterSupplyTemperature'] = hws_temp
if not hwr_temp is None:
out_data['HotWaterReturnTemperature'] = hwr_temp
if not hw_stsp_temp is None:
out_data['HotWaterTemperatureSetPoint'] = hw_stsp_temp
if not loop_dp is None:
out_data['LoopDifferentialPressure'] = loop_dp
if not loop_dp_stpt is None:
out_data['LoopDifferentialPressureSetPoint'] = loop_dp_stpt
if not hw_pump_status is None:
out_data['PumpStatus'] = hw_pump_status
if not boiler_status is None:
out_data['BoilerStatus'] = boiler_status
if not hw_pump_vfd is None:
out_data['HotWaterPumpVfd'] = hw_pump_vfd
result.insert_table_row(self.table_name, out_data)
return result
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'])
})
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: whole building energy savings.",
logging.INFO)
# Gather loads and outside air temperatures. Reduced to an hourly average
self.out.log("Querying database.", logging.INFO)
load_query = self.inp.get_query_sets('load',
group_by='hour',
group_by_aggregation=Avg,
exclude={'value': None},
wrap_for_merge=True)
oat_query = self.inp.get_query_sets('oat',
group_by='hour',
group_by_aggregation=Avg,
exclude={'value': None},
wrap_for_merge=True)
self.out.log("Getting unit conversions.", logging.INFO)
base_topic = self.inp.get_topics()
meta_topics = self.inp.get_topics_meta()
load_unit = meta_topics['load'][base_topic['load'][0]]['unit']
self.out.log("Convert loads from [{}] to [kW].".format(load_unit),
logging.INFO)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
temperature_unit = meta_topics['oat'][base_topic['oat'][0]]['unit']
self.out.log(
"Convert temperatures from [{}] to [F].".format(temperature_unit),
logging.INFO)
# Match the values by timestamp
merged_load_oat = self.inp.merge(load_query, oat_query)
load_values = []
oat_values = []
datetime_values = []
for x in merged_load_oat:
if temperature_unit == 'celcius':
convertedTemp = cu.convertCelciusToFahrenheit(x['oat'][0])
elif temperature_unit == 'kelvin':
convertedTemp = cu.convertKelvinToCelcius(
cu.convertCelciusToFahrenheit(x['oat'][0]))
else:
convertedTemp = x['oat'][0]
load_values.append(x['load'][0] *
load_convertfactor) #Converted to kWh
oat_values.append(convertedTemp)
datetime_values.append(x['time'])
indexList = {}
indexList['trainingStart'] = ttow.findDateIndex(
datetime_values, self.baseline_start)
self.out.log('@trainingStart ' + str(indexList['trainingStart']),
logging.INFO)
indexList['trainingStop'] = ttow.findDateIndex(datetime_values,
self.baseline_stop)
self.out.log('@trainingStop ' + str(indexList['trainingStop']),
logging.INFO)
indexList['predictStart'] = ttow.findDateIndex(datetime_values,
self.savings_start)
self.out.log('@predictStart ' + str(indexList['predictStart']),
logging.INFO)
indexList['predictStop'] = ttow.findDateIndex(datetime_values,
self.savings_stop)
self.out.log('@predictStop ' + str(indexList['predictStop']),
logging.INFO)
for indx in indexList.keys():
if indexList[indx] == None:
self.out.log("Date not found in the datelist", logging.WARNING)
# Break up data into training and prediction periods.
timesTrain = datetime_values[
indexList['trainingStart']:indexList['trainingStop']]
timesPredict = datetime_values[
indexList['predictStart']:indexList['predictStop']]
valsTrain = load_values[
indexList['trainingStart']:indexList['trainingStop']]
valsActual = load_values[
indexList['predictStart']:indexList['predictStop']]
oatsTrain = oat_values[
indexList['trainingStart']:indexList['trainingStop']]
oatsPredict = oat_values[
indexList['predictStart']:indexList['predictStop']]
# Generate other information needed for model.
timeStepMinutes = (timesTrain[1] - timesTrain[0]).total_seconds() / 60
# TODO: Should this be calculated in the utility function
binCt = 6 # TODO: Allow caller to pass this in as an argument.
# Form the temperature-time-of-week model.
self.out.log("Finding baseline model", logging.INFO)
ttowModel = ttow.formModel(timesTrain, oatsTrain, valsTrain,
timeStepMinutes, binCt)
# Apply the model.
self.out.log("Applying baseline model", logging.INFO)
valsPredict = ttow.applyModel(ttowModel, timesPredict, oatsPredict)
# Output for scatter plot
prevSum = 0
for ctr in range(len(timesPredict)):
# Calculate cumulative savings.
prevSum += (valsPredict[ctr] - valsActual[ctr])
local_time = str(
self.inp.localize_sensor_time(base_topic['load'][0],
timesPredict[ctr]))
self.out.insert_row(
"DayTimeTemperatureModel", {
"datetimeValues": local_time,
"measured": valsActual[ctr],
"predicted": valsPredict[ctr],
"cumulativeSum": prevSum
})
