def demand_calculation(locator, config):
"""
Algorithm to calculate the hourly demand of energy services in buildings
using the integrated model of [Fonseca2015]_.
Produces a demand file per building and a total demand file for the whole zone of interest:
- a csv file for every building with hourly demand data.
- ``Total_demand.csv``, csv file of yearly demand data per building.
:param locator: An InputLocator to locate input files
:type locator: cea.inputlocator.InputLocator
:param weather_path: A path to the EnergyPlus weather data file (.epw)
:type weather_path: str
:param use_dynamic_infiltration_calculation: Set this to ``True`` if the (slower) dynamic infiltration
calculation method (:py:func:`cea.demand.ventilation_air_flows_detailed.calc_air_flows`) should be used instead
of the standard.
:type use_dynamic_infiltration_calculation: bool
:param multiprocessing: Set this to ``True`` if the :py:mod:`multiprocessing` module should be used to speed up
calculations by making use of multiple cores.
:type multiprocessing: bool
:returns: None
:rtype: NoneType
.. [Fonseca2015] Fonseca, Jimeno A., and Arno Schlueter. “Integrated Model for Characterization of
Spatiotemporal Building Energy Consumption Patterns in Neighborhoods and City Districts.”
Applied Energy 142 (2015): 247–265.
"""
# INITIALIZE TIMER
t0 = time.clock()
# LOCAL VARIABLES
building_names = config.demand.buildings
use_dynamic_infiltration = config.demand.use_dynamic_infiltration_calculation
resolution_output = config.demand.resolution_output
loads_output = config.demand.loads_output
massflows_output = config.demand.massflows_output
temperatures_output = config.demand.temperatures_output
debug = config.debug
weather_path = locator.get_weather_file()
weather_data = epwreader.epw_reader(weather_path)[['year', 'drybulb_C', 'wetbulb_C',
'relhum_percent', 'windspd_ms', 'skytemp_C']]
year = weather_data['year'][0]
# create date range for the calculation year
date_range = get_date_range_hours_from_year(year)
# SPECIFY NUMBER OF BUILDINGS TO SIMULATE
print('Running demand calculation for the following buildings=%s' % building_names)
# CALCULATE OBJECT WITH PROPERTIES OF ALL BUILDINGS
building_properties = BuildingProperties(locator, building_names)
# add a message i2065 of warning. This needs a more elegant solution
def calc_buildings_less_100m2(building_properties):
footprint = building_properties._prop_geometry.footprint
floors = building_properties._prop_geometry.floors_ag
names = building_properties._prop_geometry.index
GFA_m2 = [x * y for x, y in zip(footprint, floors)]
list_buildings_less_100m2 = []
for name, gfa in zip(names, GFA_m2):
if gfa < 100.0:
list_buildings_less_100m2.append(name)
return list_buildings_less_100m2
list_buildings_less_100m2 = calc_buildings_less_100m2(building_properties)
if list_buildings_less_100m2 != []:
print('Warning! The following list of buildings have less than 100 m2 of gross floor area, CEA might fail: %s' % list_buildings_less_100m2)
# DEMAND CALCULATION
n = len(building_names)
calc_thermal_loads = cea.utilities.parallel.vectorize(thermal_loads.calc_thermal_loads,
config.get_number_of_processes(), on_complete=print_progress)
calc_thermal_loads(
building_names,
[building_properties[b] for b in building_names],
repeat(weather_data, n),
repeat(date_range, n),
repeat(locator, n),
repeat(use_dynamic_infiltration, n),
repeat(resolution_output, n),
repeat(loads_output, n),
repeat(massflows_output, n),
repeat(temperatures_output, n),
repeat(config, n),
repeat(debug, n))
# WRITE TOTAL YEARLY VALUES
writer_totals = demand_writers.YearlyDemandWriter(loads_output, massflows_output, temperatures_output)
totals, time_series = writer_totals.write_to_csv(building_names, locator)
time_elapsed = time.clock() - t0
print('done - time elapsed: %d.2 seconds' % time_elapsed)
return totals, time_series
def demand_calculation(locator, config):
"""
Algorithm to calculate the hourly demand of energy services in buildings
using the integrated model of [Fonseca2015]_.
Produces a demand file per building and a total demand file for the whole zone of interest:
- a csv file for every building with hourly demand data.
- ``Total_demand.csv``, csv file of yearly demand data per building.
:param locator: An InputLocator to locate input files
:type locator: cea.inputlocator.InputLocator
:param weather_path: A path to the EnergyPlus weather data file (.epw)
:type weather_path: str
:param use_dynamic_infiltration_calculation: Set this to ``True`` if the (slower) dynamic infiltration
calculation method (:py:func:`cea.demand.ventilation_air_flows_detailed.calc_air_flows`) should be used instead
of the standard.
:type use_dynamic_infiltration_calculation: bool
:param multiprocessing: Set this to ``True`` if the :py:mod:`multiprocessing` module should be used to speed up
calculations by making use of multiple cores.
:type multiprocessing: bool
:returns: None
:rtype: NoneType
.. [Fonseca2015] Fonseca, Jimeno A., and Arno Schlueter. “Integrated Model for Characterization of
Spatiotemporal Building Energy Consumption Patterns in Neighborhoods and City Districts.”
Applied Energy 142 (2015): 247–265.
"""
# INITIALIZE TIMER
t0 = time.clock()
# LOCAL VARIABLES
multiprocessing = config.multiprocessing
region = config.region
list_building_names = config.demand.buildings
use_dynamic_infiltration = config.demand.use_dynamic_infiltration_calculation
use_daysim_radiation = config.demand.use_daysim_radiation
use_stochastic_occupancy = config.demand.use_stochastic_occupancy
resolution_output = config.demand.resolution_output
loads_output = config.demand.loads_output
massflows_output = config.demand.massflows_output
temperatures_output = config.demand.temperatures_output
format_output = config.demand.format_output
override_variables = config.demand.override_variables
weather_data = epwreader.epw_reader(config.weather)[[
'year', 'drybulb_C', 'wetbulb_C', 'relhum_percent', 'windspd_ms',
'skytemp_C'
]]
year = weather_data['year'][0]
# CALCULATE OBJECT WITH PROPERTIES OF ALL BUILDINGS
building_properties, schedules_dict, date = properties_and_schedule(
locator, region, year, use_daysim_radiation, override_variables)
# SPECIFY NUMBER OF BUILDINGS TO SIMULATE
if not list_building_names:
list_building_names = building_properties.list_building_names()
print('Running demand calculation for all buildings in the zone')
else:
print('Running demand calculation for the next buildings=%s' %
list_building_names)
# DEMAND CALCULATION
if multiprocessing and mp.cpu_count() > 1:
calc_demand_multiprocessing(building_properties, date, locator,
list_building_names, schedules_dict,
weather_data, use_dynamic_infiltration,
use_stochastic_occupancy,
resolution_output, loads_output,
massflows_output, temperatures_output,
format_output, config, region)
else:
calc_demand_singleprocessing(
building_properties, date, locator, list_building_names,
schedules_dict, weather_data, use_dynamic_infiltration,
use_stochastic_occupancy, resolution_output, loads_output,
massflows_output, temperatures_output, format_output, region)
# WRITE TOTAL YEARLY VALUES
writer_totals = demand_writers.YearlyDemandWriter(loads_output,
massflows_output,
temperatures_output)
if format_output == 'csv':
totals, time_series = writer_totals.write_to_csv(
list_building_names, locator)
elif format_output == 'hdf5':
totals, time_series = writer_totals.write_to_hdf5(
list_building_names, locator)
else:
raise Exception('error')
time_elapsed = time.clock() - t0
print('done - time elapsed: %d.2f seconds' % time_elapsed)
return totals, time_series