def calc_thermal_loads(building_name, bpr, weather_data, usage_schedules, date,
gv, locator, use_stochastic_occupancy,
use_dynamic_infiltration_calculation,
resolution_outputs, loads_output, massflows_output,
temperatures_output, format_output, region):
"""
Calculate thermal loads of a single building with mechanical or natural ventilation.
Calculation procedure follows the methodology of ISO 13790
The structure of ``usage_schedules`` is:
.. code-block:: python
:emphasize-lines: 2,4
{
'list_uses': ['ADMIN', 'GYM', ...],
'schedules': [ ([...], [...], [...], [...]), (), (), () ]
}
* each element of the 'list_uses' entry represents a building occupancy type.
* each element of the 'schedules' entry represents the schedules for a building occupancy type.
* the schedules for a building occupancy type are a 4-tuple (occupancy, electricity, domestic hot water,
probability of use), with each element of the 4-tuple being a list of hourly values (8760 values).
Side effect include a number of files in two folders:
* ``scenario/outputs/data/demand``
* ``${Name}.csv`` for each building
* temporary folder (as returned by ``tempfile.gettempdir()``)
* ``${Name}T.csv`` for each building
daren-thomas: as far as I can tell, these are the only side-effects.
:param building_name: name of building
:type building_name: str
:param bpr: a collection of building properties for the building used for thermal loads calculation
:type bpr: BuildingPropertiesRow
:param weather_data: data from the .epw weather file. Each row represents an hour of the year. The columns are:
``drybulb_C``, ``relhum_percent``, and ``windspd_ms``
:type weather_data: pandas.DataFrame
:param usage_schedules: dict containing schedules and function names of buildings.
:type usage_schedules: dict
:param date: the dates (hours) of the year (8760)
:type date: pandas.tseries.index.DatetimeIndex
:param gv: global variables / context
:type gv: GlobalVariables
:param locator:
:param use_dynamic_infiltration_calculation:
:returns: This function does not return anything
:rtype: NoneType
"""
schedules, tsd = initialize_inputs(bpr, usage_schedules, weather_data,
use_stochastic_occupancy)
# CALCULATE ELECTRICITY LOADS
tsd = electrical_loads.calc_Eal_Epro(tsd, bpr, schedules)
# CALCULATE REFRIGERATION LOADS
if refrigeration_loads.has_refrigeration_load(bpr):
tsd = refrigeration_loads.calc_Qcre_sys(bpr, tsd, schedules)
tsd = refrigeration_loads.calc_Qref(locator, bpr, tsd, region)
else:
tsd['DC_cre'] = tsd['Qcre_sys'] = tsd['Qcre'] = np.zeros(8760)
tsd['mcpcre_sys'] = tsd['Tcre_sys_re'] = tsd[
'Tcre_sys_sup'] = np.zeros(8760)
tsd['E_cre'] = np.zeros(8760)
if np.isclose(bpr.rc_model['Af'],
0.0): # if building does not have conditioned area
#UPDATE ALL VALUES TO 0
tsd = update_timestep_data_no_conditioned_area(tsd)
else:
#CALCULATE PROCESS HEATING
tsd['Qhpro_sys'][:] = schedules['Qhpro'] * bpr.internal_loads[
'Qhpro_Wm2'] # in kWh
# CALCULATE DATA CENTER LOADS
if datacenter_loads.has_data_load(bpr):
tsd = datacenter_loads.calc_Edata(bpr, tsd,
schedules) # end-use electricity
tsd = datacenter_loads.calc_Qcdata_sys(
tsd) # system need for cooling
tsd = datacenter_loads.calc_Qcdataf(
locator, bpr, tsd, region) # final need for cooling
else:
tsd['DC_cdata'] = tsd['Qcdata_sys'] = tsd['Qcdata'] = np.zeros(
8760)
tsd['mcpcdata_sys'] = tsd['Tcdata_sys_re'] = tsd[
'Tcdata_sys_sup'] = np.zeros(8760)
tsd['Edata'] = tsd['E_cdata'] = np.zeros(8760)
#CALCULATE HEATING AND COOLING DEMAND
tsd = calc_Qhs_Qcs(
bpr, date, tsd, use_dynamic_infiltration_calculation,
region) #end-use demand latent and sensible + ventilation
tsd = sensible_loads.calc_Qhs_Qcs_loss(bpr, tsd) # losses
tsd = sensible_loads.calc_Qhs_sys_Qcs_sys(tsd) # system (incl. losses)
tsd = sensible_loads.calc_temperatures_emission_systems(
bpr, tsd) # calculate temperatures
tsd = electrical_loads.calc_Eauxf_ve(
tsd) #calc auxiliary loads ventilation
tsd = electrical_loads.calc_Eaux_Qhs_Qcs(
tsd, bpr) #calc auxiliary loads heating and cooling
#SOME TRICKS FOR THE GRAPHS - see where to put this.
tsd = latent_loads.calc_latent_gains_from_people(tsd, bpr)
tsd['Qcs_lat_sys'] = abs(tsd['Qcs_lat_sys'])
tsd['DC_cs'] = abs(tsd['DC_cs'])
tsd['Qcs_sys'] = abs(tsd['Qcs_sys'])
tsd = calc_Qcs_sys(bpr, tsd) # final : including fuels and renewables
tsd = calc_Qhs_sys(bpr, tsd) # final : including fuels and renewables
#CALCULATE HOT WATER LOADS
if hotwater_loads.has_hot_water_technical_system(bpr):
tsd = electrical_loads.calc_Eaux_fw(tsd, bpr, schedules)
tsd = hotwater_loads.calc_Qww(bpr, tsd, schedules) # end-use
tsd = hotwater_loads.calc_Qww_sys(bpr, tsd,
gv) # system (incl. losses)
tsd = electrical_loads.calc_Eaux_ww(tsd,
bpr) #calc auxiliary loads
tsd = hotwater_loads.calc_Qwwf(bpr, tsd) #final
else:
tsd = electrical_loads.calc_Eaux_fw(tsd, bpr, schedules)
tsd['Qww'] = tsd['DH_ww'] = tsd['Qww_sys'] = np.zeros(8760)
tsd['mcpww_sys'] = tsd['Tww_sys_re'] = tsd[
'Tww_sys_sup'] = np.zeros(8760)
tsd['Eaux_ww'] = tsd['SOLAR_ww'] = np.zeros(8760)
tsd['NG_ww'] = tsd['COAL_ww'] = tsd['OIL_ww'] = tsd[
'WOOD_ww'] = np.zeros(8760)
tsd['E_ww'] = np.zeros(8760)
# CALCULATE SUM OF HEATING AND COOLING LOADS
tsd = calc_QH_sys_QC_sys(tsd) # aggregated cooling and heating loads
#CALCULATE ELECTRICITY LOADS PART 2/2 AUXILIARY LOADS + ENERGY GENERATION
tsd = electrical_loads.calc_Eaux(tsd) # auxiliary totals
tsd = electrical_loads.calc_E_sys(tsd) # system (incl. losses)
tsd = electrical_loads.calc_Ef(bpr, tsd) # final (incl. self. generated)
#WRITE SOLAR RESULTS
write_results(bpr, building_name, date, format_output, gv, loads_output,
locator, massflows_output, resolution_outputs,
temperatures_output, tsd)
return
def calc_thermal_loads(building_name, bpr, weather_data, date_range, locator,
use_dynamic_infiltration_calculation,
resolution_outputs, loads_output, massflows_output,
temperatures_output, config, debug):
"""
Calculate thermal loads of a single building with mechanical or natural ventilation.
Calculation procedure follows the methodology of ISO 13790
The structure of ``usage_schedules`` is:
.. code-block:: python
:emphasize-lines: 2,4
{
'list_uses': ['ADMIN', 'GYM', ...],
'schedules': [ ([...], [...], [...], [...]), (), (), () ]
}
* each element of the 'list_uses' entry represents a building occupancy type.
* each element of the 'schedules' entry represents the schedules for a building occupancy type.
* the schedules for a building occupancy type are a 4-tuple (occupancy, electricity, domestic hot water,
probability of use), with each element of the 4-tuple being a list of hourly values (HOURS_IN_YEAR values).
Side effect include a number of files in two folders:
* ``scenario/outputs/data/demand``
* ``${Name}.csv`` for each building
* temporary folder (as returned by ``tempfile.gettempdir()``)
* ``${Name}T.csv`` for each building
daren-thomas: as far as I can tell, these are the only side-effects.
:param building_name: name of building
:type building_name: str
:param bpr: a collection of building properties for the building used for thermal loads calculation
:type bpr: BuildingPropertiesRow
:param weather_data: data from the .epw weather file. Each row represents an hour of the year. The columns are:
``drybulb_C``, ``relhum_percent``, and ``windspd_ms``
:type weather_data: pandas.DataFrame
:param locator:
:param use_dynamic_infiltration_calculation:
:returns: This function does not return anything
:rtype: NoneType
"""
schedules, tsd = initialize_inputs(bpr, weather_data, locator)
# CALCULATE ELECTRICITY LOADS
tsd = electrical_loads.calc_Eal_Epro(tsd, schedules)
# CALCULATE REFRIGERATION LOADS
if refrigeration_loads.has_refrigeration_load(bpr):
tsd = refrigeration_loads.calc_Qcre_sys(bpr, tsd, schedules)
tsd = refrigeration_loads.calc_Qref(locator, bpr, tsd)
else:
tsd['DC_cre'] = tsd['Qcre_sys'] = tsd['Qcre'] = np.zeros(HOURS_IN_YEAR)
tsd['mcpcre_sys'] = tsd['Tcre_sys_re'] = tsd[
'Tcre_sys_sup'] = np.zeros(HOURS_IN_YEAR)
tsd['E_cre'] = np.zeros(HOURS_IN_YEAR)
if np.isclose(bpr.rc_model['Af'],
0.0): # if building does not have conditioned area
tsd['T_int'] = tsd['T_ext']
tsd['x_int'] = np.vectorize(convert_rh_to_moisture_content)(
tsd['rh_ext'], tsd['T_int'])
print("building () does not have an air-conditioned area".format(
bpr.name))
else:
# CALCULATE PROCESS HEATING
tsd['Qhpro_sys'] = schedules['Qhpro_W'] # in Wh
# CALCULATE PROCESS COOLING
tsd['Qcpro_sys'] = schedules['Qcpro_W'] # in Wh
# CALCULATE DATA CENTER LOADS
if datacenter_loads.has_data_load(bpr):
tsd = datacenter_loads.calc_Edata(tsd,
schedules) # end-use electricity
tsd = datacenter_loads.calc_Qcdata_sys(
bpr, tsd) # system need for cooling
tsd = datacenter_loads.calc_Qcdataf(locator, bpr,
tsd) # final need for cooling
else:
tsd['DC_cdata'] = tsd['Qcdata_sys'] = tsd['Qcdata'] = np.zeros(
HOURS_IN_YEAR)
tsd['mcpcdata_sys'] = tsd['Tcdata_sys_re'] = tsd[
'Tcdata_sys_sup'] = np.zeros(HOURS_IN_YEAR)
tsd['Edata'] = tsd['E_cdata'] = np.zeros(HOURS_IN_YEAR)
# CALCULATE SPACE CONDITIONING DEMANDS
tsd = latent_loads.calc_Qgain_lat(tsd, schedules)
tsd = calc_set_points(
bpr, date_range, tsd, building_name, config, locator,
schedules) # calculate the setpoints for every hour
tsd = calc_Qhs_Qcs(
bpr, tsd, use_dynamic_infiltration_calculation
) # end-use demand latent and sensible + ventilation
tsd = sensible_loads.calc_Qhs_Qcs_loss(bpr, tsd) # losses
tsd = sensible_loads.calc_Qhs_sys_Qcs_sys(tsd) # system (incl. losses)
tsd = sensible_loads.calc_temperatures_emission_systems(
bpr, tsd) # calculate temperatures
tsd = electrical_loads.calc_Eauxf_ve(
tsd) # calc auxiliary loads ventilation
tsd = electrical_loads.calc_Eaux_Qhs_Qcs(
tsd, bpr) # calc auxiliary loads heating and cooling
tsd = calc_Qcs_sys(bpr, tsd) # final : including fuels and renewables
tsd = calc_Qhs_sys(bpr, tsd) # final : including fuels and renewables
# Positive loads
tsd['Qcs_lat_sys'] = abs(tsd['Qcs_lat_sys'])
tsd['DC_cs'] = abs(tsd['DC_cs'])
tsd['Qcs_sys'] = abs(tsd['Qcs_sys'])
tsd['Qcre_sys'] = abs(
tsd['Qcre_sys']
) # inverting sign of cooling loads for reporting and graphs
tsd['Qcdata_sys'] = abs(
tsd['Qcdata_sys']
) # inverting sign of cooling loads for reporting and graphs
# CALCULATE HOT WATER LOADS
if hotwater_loads.has_hot_water_technical_system(bpr):
tsd = electrical_loads.calc_Eaux_fw(tsd, bpr, schedules)
tsd = hotwater_loads.calc_Qww(bpr, tsd, schedules) # end-use
tsd = hotwater_loads.calc_Qww_sys(bpr, tsd) # system (incl. losses)
tsd = electrical_loads.calc_Eaux_ww(tsd, bpr) # calc auxiliary loads
tsd = hotwater_loads.calc_Qwwf(bpr, tsd) # final
else:
tsd = electrical_loads.calc_Eaux_fw(tsd, bpr, schedules)
tsd['Qww'] = tsd['DH_ww'] = tsd['Qww_sys'] = np.zeros(HOURS_IN_YEAR)
tsd['mcpww_sys'] = tsd['Tww_sys_re'] = tsd['Tww_sys_sup'] = np.zeros(
HOURS_IN_YEAR)
tsd['Eaux_ww'] = tsd['SOLAR_ww'] = np.zeros(HOURS_IN_YEAR)
tsd['NG_ww'] = tsd['COAL_ww'] = tsd['OIL_ww'] = tsd[
'WOOD_ww'] = np.zeros(HOURS_IN_YEAR)
tsd['E_ww'] = np.zeros(HOURS_IN_YEAR)
# CALCULATE SUM OF HEATING AND COOLING LOADS
tsd = calc_QH_sys_QC_sys(tsd) # aggregated cooling and heating loads
# CALCULATE ELECTRICITY LOADS PART 2/2 AUXILIARY LOADS + ENERGY GENERATION
tsd = electrical_loads.calc_Eaux(tsd) # auxiliary totals
tsd = electrical_loads.calc_E_sys(tsd) # system (incl. losses)
tsd = electrical_loads.calc_Ef(bpr, tsd) # final (incl. self. generated)
# WRITE SOLAR RESULTS
write_results(bpr, building_name, date_range, loads_output, locator,
massflows_output, resolution_outputs, temperatures_output,
tsd, debug)
return