def execute(self):
#Called after User hits GO
"""
Will output the following: year, aggregated load amounts,
and aggregated gas amounts.
"""
self.out.log("Starting application: longitudinal benchmarking.",
logging.INFO)
self.out.log("Querying database.", logging.INFO)
# Note: Assumes all of the energy data are in a per hour basis.
# TODO: The caveat above must be made stronger. Aggregating by summing
# only converts, e.g., [kW] to [kWh] for hourly observations.
# Similar problem for gas data.
# TODO: The query here presumably groups by calendar year. Need to check
# whether application actually wants a year's worth of data, looking
# backward from most recent observation.
# Valid calculation to sum the data by 'year'.
load_by_year = self.inp.get_query_sets('load',
group_by='year',
group_by_aggregation=Sum,
exclude={'value': None},
wrap_for_merge=True)
gas_by_year = self.inp.get_query_sets('natgas',
group_by='year',
group_by_aggregation=Sum,
exclude={'value': None},
wrap_for_merge=True)
merge_load_gas = self.inp.merge(load_by_year, gas_by_year)
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]; integration will take to [kWh].".
format(load_unit), logging.INFO)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
natgas_unit = meta_topics['natgas'][base_topic['natgas'][0]]['unit']
self.out.log(
"Convert natgas from [{}] to [kBtu/hr]; integration will take to [kBtu]."
.format(natgas_unit), logging.INFO)
natgas_convertfactor = cu.getFactor_powertoKBtu_hr(natgas_unit)
self.out.log("Compiling the report table.", logging.INFO)
for x in merge_load_gas:
self.out.insert_row(
'Longitudinal_BM', {
'year': x['time'].year,
'load': x['load'][0] * load_convertfactor,
'natgas': x['natgas'][0] * natgas_convertfactor
})
def execute(self):
#Called after User hits GO
"""
Will output the following: year, aggregated load amounts,
and aggregated gas amounts.
"""
self.out.log("Starting application: longitudinal benchmarking.", logging.INFO)
self.out.log("Querying database.", logging.INFO)
# Note: Assumes all of the energy data are in a per hour basis.
# TODO: The caveat above must be made stronger. Aggregating by summing
# only converts, e.g., [kW] to [kWh] for hourly observations.
# Similar problem for gas data.
# TODO: The query here presumably groups by calendar year. Need to check
# whether application actually wants a year's worth of data, looking
# backward from most recent observation.
# Valid calculation to sum the data by 'year'.
load_by_year = self.inp.get_query_sets('load', group_by='year',
group_by_aggregation=Sum,
exclude={'value':None},
wrap_for_merge=True)
gas_by_year = self.inp.get_query_sets('natgas', group_by='year',
group_by_aggregation=Sum,
exclude={'value':None},
wrap_for_merge=True)
merge_load_gas = self.inp.merge(load_by_year, gas_by_year)
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]; integration will take to [kWh].".format(load_unit),
logging.INFO
)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
natgas_unit = meta_topics['natgas'][base_topic['natgas'][0]]['unit']
self.out.log(
"Convert natgas from [{}] to [kBtu/hr]; integration will take to [kBtu].".format(natgas_unit),
logging.INFO
)
natgas_convertfactor = cu.getFactor_powertoKBtu_hr(natgas_unit)
self.out.log("Compiling the report table.", logging.INFO)
for x in merge_load_gas:
self.out.insert_row('Longitudinal_BM', {
'year': x['time'].year,
'load': x['load'][0]*load_convertfactor,
'natgas': x['natgas'][0]*natgas_convertfactor
})
def execute(self):
#Called after User hits GO
"""Outputs the ENERGY Star Score from Target Finder API"""
#NOTE: Connection check happens after data is formatted into XML and
# sent into the web service request.
self.out.log("Starting application: cross-sectional benchmarking.", logging.INFO)
self.out.log("Querying the database for model parameters.", logging.INFO)
bldgMetaData = dict()
bldgMetaData['floor-area'] = self.sq_ft
bldgMetaData['year-built'] = self.building_year
bldgMetaData['bldg-name'] = self.building_name
bldgMetaData['function'] = self.building_function
bldgMetaData['zipcode'] = self.building_zipcode
self.out.log("Querying the database for most recent year of energy load.", logging.INFO)
# NOTE: Hourly values are preferred to make calculations easier.
# TODO: The caveat above must be made stronger. Aggregating by summing
# only converts, e.g., [kW] to [kWh] for hourly observations.
# Similar problem for gas data.
# TODO: The query here presumably groups by calendar year. Need to check
# whether application actually wants a year's worth of data, looking
# backward from most recent observation.
load_by_year = self.inp.get_query_sets('load', group_by='year',
group_by_aggregation=Sum,
exclude={'value':None},
wrap_for_merge=True)
gas_by_year = self.inp.get_query_sets('natgas', group_by='year',
group_by_aggregation=Sum,
exclude={'value':None},
wrap_for_merge=True)
merge_load_gas = self.inp.merge(load_by_year, gas_by_year)
# Convert the generator to a list that can be indexed.
merge_data_list = []
for item in merge_load_gas:
merge_data_list.append((item['time'], item['load'][0], item['natgas'][0]))
recent_record = merge_data_list[len(merge_data_list)-1]
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]; integration will take to [kWh].".format(load_unit),
logging.INFO
)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
natgas_unit = meta_topics['natgas'][base_topic['natgas'][0]]['unit']
self.out.log(
"Convert natgas from [{}] to [kBtu/hr]; integration will take to [kBtu].".format(natgas_unit),
logging.INFO
)
natgas_convertfactor = cu.getFactor_powertoKBtu_hr(natgas_unit)
#TODO: Convert values to units that are PM Manager valid values.
energyUseList = [['Electric','kWh (thousand Watt-hours)',int(recent_record[1]*load_convertfactor)],
['Natural Gas','kBtu (thousand Btu)',int(recent_record[2]*natgas_convertfactor)]]
self.out.log("Generate XML-formatted data to pass data to the webservice.", logging.INFO)
targetFinder_xml = gen_xml_targetFinder(bldgMetaData,energyUseList,'z_targetFinder_xml')
self.out.log("Function that sends a URL Request with ENERGY STAR web server.", logging.INFO)
PMMetrics = retrieveScore(targetFinder_xml)
self.out.log("Compile report table.", logging.INFO)
if PMMetrics['status'] == 'success':
self.out.log('Analysis successful', logging.INFO)
self.out.insert_row('CrossSectional_BM', {
'Metric Name': 'Target Finder Score',
'Value': str(PMMetrics['designScore'][0])
})
else:
self.out.log(str(PMMetrics['status'])+'\nReason:\t'+str(PMMetrics['reason']), logging.WARNING)
self.out.insert_row('CrossSectional_BM', {
'Metric Name': 'Target Finder Score',
'Value': 'Check log for error.'
})
def execute(self):
#Called after User hits GO
"""Outputs the ENERGY Star Score from Target Finder API"""
#NOTE: Connection check happens after data is formatted into XML and
# sent into the web service request.
self.out.log("Starting application: cross-sectional benchmarking.",
logging.INFO)
self.out.log("Querying the database for model parameters.",
logging.INFO)
bldgMetaData = dict()
bldgMetaData['floor-area'] = self.sq_ft
bldgMetaData['year-built'] = self.building_year
bldgMetaData['bldg-name'] = self.building_name
bldgMetaData['function'] = self.building_function
bldgMetaData['zipcode'] = self.building_zipcode
self.out.log(
"Querying the database for most recent year of energy load.",
logging.INFO)
# NOTE: Hourly values are preferred to make calculations easier.
# TODO: The caveat above must be made stronger. Aggregating by summing
# only converts, e.g., [kW] to [kWh] for hourly observations.
# Similar problem for gas data.
# TODO: The query here presumably groups by calendar year. Need to check
# whether application actually wants a year's worth of data, looking
# backward from most recent observation.
load_by_year = self.inp.get_query_sets('load',
group_by='year',
group_by_aggregation=Sum,
exclude={'value': None},
wrap_for_merge=True)
gas_by_year = self.inp.get_query_sets('natgas',
group_by='year',
group_by_aggregation=Sum,
exclude={'value': None},
wrap_for_merge=True)
merge_load_gas = self.inp.merge(load_by_year, gas_by_year)
# Convert the generator to a list that can be indexed.
merge_data_list = []
for item in merge_load_gas:
merge_data_list.append(
(item['time'], item['load'][0], item['natgas'][0]))
recent_record = merge_data_list[len(merge_data_list) - 1]
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]; integration will take to [kWh].".
format(load_unit), logging.INFO)
load_convertfactor = cu.getFactor_powertoKW(load_unit)
natgas_unit = meta_topics['natgas'][base_topic['natgas'][0]]['unit']
self.out.log(
"Convert natgas from [{}] to [kBtu/hr]; integration will take to [kBtu]."
.format(natgas_unit), logging.INFO)
natgas_convertfactor = cu.getFactor_powertoKBtu_hr(natgas_unit)
#TODO: Convert values to units that are PM Manager valid values.
energyUseList = [[
'Electric', 'kWh (thousand Watt-hours)',
int(recent_record[1] * load_convertfactor)
],
[
'Natural Gas', 'kBtu (thousand Btu)',
int(recent_record[2] * natgas_convertfactor)
]]
self.out.log(
"Generate XML-formatted data to pass data to the webservice.",
logging.INFO)
targetFinder_xml = gen_xml_targetFinder(bldgMetaData, energyUseList,
'z_targetFinder_xml')
self.out.log(
"Function that sends a URL Request with ENERGY STAR web server.",
logging.INFO)
PMMetrics = retrieveScore(targetFinder_xml)
self.out.log("Compile report table.", logging.INFO)
if PMMetrics['status'] == 'success':
self.out.log('Analysis successful', logging.INFO)
self.out.insert_row(
'CrossSectional_BM', {
'Metric Name': 'Target Finder Score',
'Value': str(PMMetrics['designScore'][0])
})
else:
self.out.log(
str(PMMetrics['status']) + '\nReason:\t' +
str(PMMetrics['reason']), logging.WARNING)
self.out.insert_row(
'CrossSectional_BM', {
'Metric Name': 'Target Finder Score',
'Value': 'Check log for error.'
})
