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
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, usage_schedules, date, gv, locator,
use_dynamic_infiltration_calculation=False):
"""
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: 3,5
{
'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
:returns: This function does not return anything
:rtype: NoneType
"""
tsd = initialize_timestep_data(bpr, weather_data)
# get schedules
list_uses = usage_schedules['list_uses']
archetype_schedules = usage_schedules['archetype_schedules']
archetype_values = usage_schedules['archetype_values']
schedules = occupancy_model.calc_schedules(list_uses, archetype_schedules, bpr.occupancy, archetype_values)
# calculate occupancy schedule and occupant-related parameters
tsd['people'] = schedules['people'] * bpr.rc_model['Af']
tsd['ve'] = schedules['ve'] * (bpr.comfort['Ve_lps'] * 3.6) * bpr.rc_model['Af'] # in m3/h
tsd['Qs'] = schedules['Qs'] * bpr.internal_loads['Qs_Wp'] * bpr.rc_model['Af'] # in W
# get electrical loads (no auxiliary loads)
tsd = electrical_loads.calc_Eint(tsd, bpr, schedules)
# get refrigeration loads
tsd['Qcref'], tsd['mcpref'], \
tsd['Tcref_re'], tsd['Tcref_sup'] = np.vectorize(refrigeration_loads.calc_Qcref)(tsd['Eref'])
# get server loads
tsd['Qcdataf'], tsd['mcpdataf'], \
tsd['Tcdataf_re'], tsd['Tcdataf_sup'] = np.vectorize(datacenter_loads.calc_Qcdataf)(tsd['Edataf'])
# ground water temperature in C during heating season (winter) according to norm
tsd['Twwf_re'][:] = bpr.building_systems['Tww_re_0']
# ground water temperature in C during non-heating season (summer) according to norm - FIXME: which norm?
tsd['Twwf_re'][gv.seasonhours[0] + 1:gv.seasonhours[1] - 1] = 14
if bpr.rc_model['Af'] > 0: # building has conditioned area
ventilation_air_flows_simple.calc_m_ve_required(bpr, tsd)
ventilation_air_flows_simple.calc_m_ve_leakage_simple(bpr, tsd, gv)
# get internal comfort properties
tsd = controllers.calc_simple_temp_control(tsd, bpr.comfort, gv.seasonhours[0] + 1, gv.seasonhours[1],
date.dayofweek)
# # latent heat gains
tsd['w_int'] = sensible_loads.calc_Qgain_lat(schedules, bpr.internal_loads['X_ghp'], bpr.rc_model['Af'],
bpr.hvac['type_cs'], bpr.hvac['type_hs'])
# end-use demand calculation
for t in range(-720, 8760):
hoy = helpers.seasonhour_2_hoy(t, gv)
# heat flows in [W]
# sensible heat gains
tsd = sensible_loads.calc_Qgain_sen(hoy, tsd, bpr, gv)
if use_dynamic_infiltration_calculation:
# OVERWRITE STATIC INFILTRATION WITH DYNAMIC INFILTRATION RATE
dict_props_nat_vent = ventilation_air_flows_detailed.get_properties_natural_ventilation(bpr, gv)
qm_sum_in, qm_sum_out = ventilation_air_flows_detailed.calc_air_flows(
tsd['theta_a'][hoy - 1] if not np.isnan(tsd['theta_a'][hoy - 1]) else tsd['T_ext'][hoy - 1],
tsd['u_wind'][hoy], tsd['T_ext'][hoy], dict_props_nat_vent)
# INFILTRATION IS FORCED NOT TO REACH ZERO IN ORDER TO AVOID THE RC MODEL TO FAIL
tsd['m_ve_inf'][hoy] = max(qm_sum_in / 3600, 1 / 3600)
# ventilation air flows [kg/s]
ventilation_air_flows_simple.calc_air_mass_flow_mechanical_ventilation(bpr, tsd, hoy)
ventilation_air_flows_simple.calc_air_mass_flow_window_ventilation(bpr, tsd, hoy)
# ventilation air temperature
ventilation_air_flows_simple.calc_theta_ve_mech(bpr, tsd, hoy, gv)
# heating / cooling demand of building
rc_model_crank_nicholson_procedure.calc_rc_model_demand_heating_cooling(bpr, tsd, hoy, gv)
# END OF FOR LOOP
# add emission losses to heating / cooling demand
tsd['Qhs_sen_incl_em_ls'] = tsd['Qhs_sen_sys'] + tsd['Qhs_em_ls']
tsd['Qcs_sen_incl_em_ls'] = tsd['Qcs_sen_sys'] + tsd['Qcs_em_ls']
# Calc of Qhs_dis_ls/Qcs_dis_ls - losses due to distribution of heating/cooling coils
Qhs_d_ls, Qcs_d_ls = np.vectorize(sensible_loads.calc_Qhs_Qcs_dis_ls)(tsd['theta_a'], tsd['T_ext'],
tsd['Qhs_sen_incl_em_ls'],
tsd['Qcs_sen_incl_em_ls'],
bpr.building_systems['Ths_sup_0'],
bpr.building_systems['Ths_re_0'],
bpr.building_systems['Tcs_sup_0'],
bpr.building_systems['Tcs_re_0'],
np.nanmax(tsd['Qhs_sen_incl_em_ls']),
np.nanmin(tsd['Qcs_sen_incl_em_ls']),
gv.D, bpr.building_systems['Y'][0],
bpr.hvac['type_hs'],
bpr.hvac['type_cs'], gv.Bf,
bpr.building_systems['Lv'])
tsd['Qcsf_lat'] = tsd['Qcs_lat_sys']
tsd['Qhsf_lat'] = tsd['Qhs_lat_sys']
# Calc requirements of generation systems (both cooling and heating do not have a storage):
tsd['Qhs'] = tsd['Qhs_sen_sys']
tsd['Qhsf'] = tsd['Qhs'] + tsd['Qhs_em_ls'] + Qhs_d_ls # no latent is considered because it is already added a
# s electricity from the adiabatic system.
tsd['Qcs'] = tsd['Qcs_sen_sys'] + tsd['Qcsf_lat']
tsd['Qcsf'] = tsd['Qcs'] + tsd['Qcs_em_ls'] + Qcs_d_ls
# Calc nominal temperatures of systems
Qhsf_0 = np.nanmax(tsd['Qhsf']) # in W
Qcsf_0 = np.nanmin(tsd['Qcsf']) # in W in negative
# Cal temperatures of all systems
tsd['Tcsf_re'], tsd['Tcsf_sup'], tsd['Thsf_re'], \
tsd['Thsf_sup'], tsd['mcpcsf'], tsd['mcphsf'] = sensible_loads.calc_temperatures_emission_systems(tsd, bpr,
Qcsf_0,
Qhsf_0,
gv)
# Hot water loads -> TODO: is it not possible to have water loads without conditioned area (Af == 0)?
Mww, tsd['Qww'], Qww_ls_st, tsd['Qwwf'], Qwwf_0, Tww_st, Vww, Vw, tsd['mcpwwf'] = hotwater_loads.calc_Qwwf(
bpr.building_systems['Lcww_dis'], bpr.building_systems['Lsww_dis'], bpr.building_systems['Lvww_c'],
bpr.building_systems['Lvww_dis'], tsd['T_ext'], tsd['theta_a'], tsd['Twwf_re'],
bpr.building_systems['Tww_sup_0'], bpr.building_systems['Y'], gv, schedules,
bpr)
# calc auxiliary loads
tsd['Eauxf'], tsd['Eauxf_hs'], tsd['Eauxf_cs'], \
tsd['Eauxf_ve'], tsd['Eauxf_ww'], tsd['Eauxf_fw'] = electrical_loads.calc_Eauxf(bpr.geometry['Blength'],
bpr.geometry['Bwidth'],
Mww, tsd['Qcsf'], Qcsf_0,
tsd['Qhsf'], Qhsf_0,
tsd['Qww'],
tsd['Qwwf'], Qwwf_0,
tsd['Tcsf_re'],
tsd['Tcsf_sup'],
tsd['Thsf_re'],
tsd['Thsf_sup'],
Vw,
bpr.age['built'],
bpr.building_systems[
'fforma'],
gv,
bpr.geometry['floors_ag'],
bpr.occupancy['PFloor'],
bpr.hvac['type_cs'],
bpr.hvac['type_hs'],
tsd['Ehs_lat_aux'],
tsd)
elif bpr.rc_model['Af'] == 0: # if building does not have conditioned area
tsd = update_timestep_data_no_conditioned_area(tsd)
else:
raise
tsd['Qhprof'][:] = schedules['Qhpro'] * bpr.internal_loads['Qhpro_Wm2'] * bpr.rc_model['Af'] # in kWh
# calculate other quantities
tsd['Qcsf_lat'] = abs(tsd['Qcsf_lat'])
tsd['Qcsf'] = abs(tsd['Qcsf'])
tsd['Qcs'] = abs(tsd['Qcs'])
tsd['people'] = np.floor(tsd['people'])
tsd['QHf'] = tsd['Qhsf'] + tsd['Qwwf'] + tsd['Qhprof']
tsd['QCf'] = tsd['Qcsf'] + tsd['Qcdataf'] + tsd['Qcref']
tsd['Ef'] = tsd['Ealf'] + tsd['Edataf'] + tsd['Eprof'] + tsd['Ecaf'] + tsd['Eauxf'] + tsd['Eref']
tsd['QEf'] = tsd['QHf'] + tsd['QCf'] + tsd['Ef']
# write results to csv
gv.demand_writer.results_to_csv(tsd, bpr, locator, date, building_name)
# write report
gv.report(tsd, locator.get_demand_results_folder(), building_name)
return
