Subpackages¶

Submodules¶

cea.analysis.benchmark module¶

cea.analysis.embodied module¶

cea.analysis.mcda module¶

cea.analysis.mobility module¶

cea.analysis.operation module¶

Module contents¶

Subpackages¶

Module contents¶

Subpackages¶

Submodules¶

cea.demand.airconditioning_model module¶

Contains debugged version of HVAC model from [Kämpf2009]

• originally coded by J. Fonseca
• debugged by G. Happle

[Kämpf2009]

(1, 2, 3, 4) Kämpf, Jérôme Henri On the modelling and optimisation of urban energy fluxes http://dx.doi.org/10.5075/epfl-thesis-4548

cea.demand.airconditioning_model.calc_hvac_cooling(tsd, hoy, gv)[source]¶

Calculate AC air mass flows, energy demand and temperatures For the cooling case for AC systems with demand controlled ventilation air flows (mechanical ventilation) and conditioning of recirculated air (outdoor air flows are not modified)

Parameters:	tsd (Dict[str, numpy.ndarray[numpy.float64]]) – time series data dict hoy (int) – time step gv (cea.globalvar.GlobalVariables) – global variables
Returns:	AC air mass flows, energy demand and temperatures for the cooling case
Return type:	Dict[str, numpy.float64]

cea.demand.airconditioning_model.calc_hvac_heating(tsd, hoy, gv)[source]¶

Calculate AC air mass flows, energy demand and temperatures for the heating case For AC system with demand controlled ventilation air flows (mechanical ventilation) and conditioning of recirculated air (outdoor air flows are not modified)

Parameters:	tsd (Dict[str, numpy.ndarray[numpy.float64]]) – time series data dict hoy (int) – time step gv (cea.globalvar.GlobalVariables) – global variables
Returns:	AC air mass flows, energy demand and temperatures for the heating case
Return type:	Dict[str, numpy.float64]

cea.demand.airconditioning_model.calc_w3_cooling_case(t5, w2, t3, w5)[source]¶

Algorithm 2 Determination of the room’s supply moisture content (w3) for the cooling case from Kaempf’s HVAC model for non-evaporative cooling

Source: [Kämpf2009]

Parameters:	t5 (numpy.float64) – temperature 5 in (°C)

:param w2 : moisture content 2 in (kg/kg dry air) :type w2: numpy.float64

Parameters:	t3 (numpy.float64) – temperature 3 in (°C) w5 (numpy.float64) – moisture content 5 in (kg/kg dry air)
Returns:	w3, moisture content of HVAC supply air in (kg/kg dry air)
Return type:	numpy.float64

cea.demand.airconditioning_model.calc_w3_heating_case(t5, w2, w5, t3, gv)[source]¶

Algorithm 1 Determination of the room’s supply moisture content (w3) for the heating case from Kaempf’s HVAC model [Kämpf2009]

Parameters:	t5 (numpy.float64) – temperature 5 in (°C) w2 (numpy.float64) – moisture content 2 in (kg/kg dry air) w5 (numpy.float64) – moisture content 5 in (kg/kg dry air) t3 (numpy.float64) – temperature 3 in (°C) gv (cea.globalvar.GlobalVariables) – global variables
Returns:	w3, moisture content of HVAC supply air in (kg/kg dry air)
Return type:	numpy.float64

cea.demand.control_heating_cooling_systems module¶

cea.demand.control_heating_cooling_systems.cooling_system_is_ac(bpr)[source]¶

determines whether a building’s heating system is ac or not

Param:	bpr: building properties row object
Return type:	bool

cea.demand.control_heating_cooling_systems.has_cooling_system(bpr)[source]¶

determines whether a building has a cooling system installed or not

Parameters:	bpr – building properties row object
Return type:	bool

cea.demand.control_heating_cooling_systems.has_heating_system(bpr)[source]¶

determines whether a building has a heating system installed or not

bpr : building properties row object

Returns:
Return type:	bool

cea.demand.control_heating_cooling_systems.heating_system_is_ac(bpr)[source]¶

determines whether a building’s heating system is ac or not

Parameters:	bpr – building properties row object
Return type:	bool

cea.demand.control_heating_cooling_systems.is_active_cooling_system(bpr, tsd, t)[source]¶

cea.demand.control_heating_cooling_systems.is_active_heating_system(bpr, tsd, t)[source]¶

cea.demand.control_ventilation_systems module¶

cea.demand.control_ventilation_systems.has_mechanical_ventilation(bpr)[source]¶

cea.demand.control_ventilation_systems.has_mechanical_ventilation_economizer(bpr)[source]¶

cea.demand.control_ventilation_systems.has_mechanical_ventilation_heat_recovery(bpr)[source]¶

cea.demand.control_ventilation_systems.has_night_flushing(bpr)[source]¶

cea.demand.control_ventilation_systems.has_window_ventilation(bpr)[source]¶

cea.demand.control_ventilation_systems.is_economizer_active(bpr, tsd, t)[source]¶

Control of activity of economizer of mechanical ventilation system Economizer of mechanical ventilation is controlled via zone set point temperatures, indoor air temperature and outdoor air temperature. Economizer is active during cooling season if the indoor air temperature exceeds the set point and the outdoor temperatures are lower than the set point. Economizer increases mechanical ventilation flow rate to the maximum.

Author: Gabriel Happle Date: APR 2017

Parameters:	bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	Economizer ON/OFF status
Return type:	bool

cea.demand.control_ventilation_systems.is_mechanical_ventilation_active(bpr, tsd, t)[source]¶

cea.demand.control_ventilation_systems.is_mechanical_ventilation_heat_recovery_active(bpr, tsd, t)[source]¶

Control of activity of heat exchanger of mechanical ventilation system

Author: Gabriel Happle Date: APR 2017

Parameters:	bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	Heat exchanger ON/OFF status
Return type:	bool

cea.demand.control_ventilation_systems.is_night_flushing_active(bpr, tsd, t)[source]¶

cea.demand.control_ventilation_systems.is_window_ventilation_active(bpr, tsd, t)[source]¶

cea.demand.datacenter_loads module¶

datacenter loads

cea.demand.datacenter_loads.calc_Qcdataf(Edataf)[source]¶

cea.demand.demand_main module¶

cea.demand.demand_writers module¶

A collection of classes that write out the demand results files. The cea.globalvar.GlobalVariables.demand_writer variable references the DemandWriter to use. The default is HourlyDemandWriter. A MonthlyDemandWriter is provided that sums the values up monthly. See the cea.analysis.sensitivity.sensitivity_demand module for an example of using the MonthlyDemandWriter.

class cea.demand.demand_writers.DemandWriter(gv)[source]¶

Bases: object

This is meant to be an abstract base class: Use the subclasses of this class instead.

Subclasses are expected to:

• set the gv field to a cea.globalvar.GlobalVariables instance in the constructor
• set the vars_to_print field in the constructor (FIXME: describe the vars_to_print structure.
• implement the write_to_csv method

results_to_csv(tsd, bpr, locator, date, building_name)[source]¶

class cea.demand.demand_writers.HourlyDemandWriter(gv)[source]¶

Bases: cea.demand.demand_writers.DemandWriter

Write out the hourly demand results

write_to_csv(building_name, columns, hourly_data, locator)[source]¶

write_totals_csv(building_properties, locator)[source]¶

read in the temporary results files and append them to the Totals.csv file.

class cea.demand.demand_writers.MonthlyDemandWriter(gv)[source]¶

Bases: cea.demand.demand_writers.DemandWriter

Write out the monthly demand results

write_to_csv(building_name, columns, hourly_data, locator)[source]¶

write_totals_csv(building_properties, locator)[source]¶

read in the temporary results files and append them to the Totals.csv file.

cea.demand.electrical_loads module¶

Electrical loads

cea.demand.electrical_loads.average_appliances_lighting_schedule(list_uses, schedules, building_uses)[source]¶

Calculate the schedule to use for lighting and appliances based on the building uses from the schedules defined for the project unsing a weighted average.

Parameters:	list_uses (List[str]) – the schedule names for the schedules parameter schedules (List[Tuple[List[float], List[float], List[float]]]) – the schedules, one for each name in list_uses. Each schedule is a list of 8760 floats... building_uses (Dict[str, float]) – A set of weights for the schedules as they apply to this particular building.
Returns:	appliances lighting schedule as a weighted average of the schedules for a specific building.
Return type:	numpy.ndarray[numpy.float64]

cea.demand.electrical_loads.calc_Eauxf(Ll, Lw, Mww, Qcsf, Qcsf_0, Qhsf, Qhsf_0, Qww, Qwwf, Qwwf_0, Tcs_re, Tcs_sup, Ths_re, Ths_sup, Vw, Year, fforma, gv, nf_ag, nfp, sys_e_cooling, sys_e_heating, Ehs_lat_aux, tsd)[source]¶

cea.demand.electrical_loads.calc_Eauxf_cs_dis(Qcsf, Qcsf0, Imax, deltaP_des, b, ts, tr, cpw)[source]¶

cea.demand.electrical_loads.calc_Eauxf_fw(freshw, nf, gv)[source]¶

cea.demand.electrical_loads.calc_Eauxf_hs_dis(Qhsf, Qhsf0, Imax, deltaP_des, b, ts, tr, cpw)[source]¶

cea.demand.electrical_loads.calc_Eauxf_ve(tsd, gv)[source]¶

calculation of auxiliary electricity consumption of mechanical ventilation and AC fans

Parameters:	tsd (dict) – Time series data of building gv – global variables

:type gv :return: electrical energy for fans of mechanical ventilation in [Wh/h] :rtype: float

cea.demand.electrical_loads.calc_Eauxf_ww(Qww, Qwwf, Qwwf0, Imax, deltaP_des, b, qV_des)[source]¶

cea.demand.electrical_loads.calc_Edataf(schedule, Ed_Wm2, Aef)[source]¶

Calculates the final electricity consumption in data centers.

Parameters:	schedule (ndarray) – The data center schedule as calculated by calc_Ea_El_Edata_Eref_schedule but just for the SERVERROOM portion of the occupancy. Ed_Wm2 (float64) – The maximum electrical consumption due to servers per unit of gross floor area (as taken from the building properties / internal loads file) Aef (float64) – The floor area with electricity in [m2]
Returns:	final electricity consumption in data centers per hour in [W]
Return type:	numpy.ndarray[numpy.float64]

cea.demand.electrical_loads.calc_Eint(tsd, bpr, list_uses, schedules)[source]¶

Calculate final internal electrical loads (without auxiliary loads)

Parameters:	tsd (Dict[str, numpy.ndarray]) – Timestep data bpr (cea.demand.thermal_loads.BuildingPropertiesRow) – building properties list_uses (list) – The list of uses used in the project schedules (List[numpy.ndarray]) – The list of schedules defined for the project - in the same order as list_uses building_uses (Dict[str, numpy.ndarray]) – for each use in list_uses, the percentage of that use for this building. Sum of values is 1.0
Returns:	tsd with new keys: [‘Eaf’, ‘Elf’, ‘Ealf’, ‘Edataf’, ‘Eref’, ‘Eprof’]
Return type:	Dict[str, numpy.ndarray]

cea.demand.electrical_loads.calc_Eprof(schedule, Epro_Wm2, Aef)[source]¶

cea.demand.electrical_loads.calc_Eprof_schedule(list_uses, schedules, building_uses)[source]¶

Calculate a weighted average of the schedules as defined in list_uses and schedules, using the weights in building_uses (which is taken from bpr.occupancy).

Parameters:	list_uses (list of str) – the schedule names for the schedules parameter schedules (list of list of float) – the schedules, one for each name in list_uses. Each schedule is a list of 8760 floats... building_uses (dict of (str, float)) – A set of weights for the schedules as they apply to this particular building.
Returns:	A weighted average of the schedules for a specific building.
Return type:	numpy.ndarray[numpy.float64]

cea.demand.electrical_loads.calc_Eref(schedule, Ere_Wm2, Aef)[source]¶

cea.demand.hotwater_loads module¶

Hotwater load (it also calculates fresh water needs)

cea.demand.hotwater_loads.calc_Qww(mww, Tww_sup_0, Tww_re, Cpw)[source]¶

cea.demand.hotwater_loads.calc_Qww_dis_ls_nr(tair, Qww, Lvww_dis, Lvww_c, Y, Qww_0, V, Flowtap, twws, Cpw, Pwater, Bf, te, gv)[source]¶

cea.demand.hotwater_loads.calc_Qww_dis_ls_r(Tair, Qww, lsww_dis, lcww_dis, Y, Qww_0, V, Flowtap, twws, Cpw, Pwater, gv)[source]¶

cea.demand.hotwater_loads.calc_Qww_schedule(list_uses, schedules, building_uses)[source]¶

Algoithm to calculate the schedule of Qww use

Parameters:	list_uses – schedules – building_uses –

cea.demand.hotwater_loads.calc_Qww_st_ls(T_ext, Ta, Qww, Vww, Qww_dis_ls_r, Qww_dis_ls_nr, gv)[source]¶

cea.demand.hotwater_loads.calc_Qwwf(Af, Lcww_dis, Lsww_dis, Lvww_c, Lvww_dis, T_ext, Ta, Tww_re, Tww_sup_0, Y, gv, Vww_lpd, Vw_lpd, Occ_m2p, list_uses, schedules, building_uses)[source]¶

This function calculates the distribution heat loss and final energy consumption of domestic hot water. Final energy consumption of dhw includes dhw demand, sensible heat loss in hot water storage tank, and heat loss in the distribution network. :param Af: Conditioned floor area in m2. :param Lcww_dis: Length of dhw usage circulation pipeline in m. :param Lsww_dis: Length of dhw usage distribution pipeline in m. :param Lvww_c: Length of dhw heating circulation pipeline in m. :param Lvww_dis: Length of dhw heating distribution pipeline in m. :param T_ext: Ambient temperature in C. :param Ta: Room temperature in C. :param Tww_re: Domestic hot water tank return temperature in C, this temperature is the ground water temperature, set according to norm. :param Tww_sup_0: Domestic hot water suppply set point temperature. :param vw: specific fresh water consumption in m3/hr*m2. :param vww: specific domestic hot water consumption in m3/hr*m2. :return:

cea.demand.hotwater_loads.calc_disls(tamb, hotw, Flowtap, V, twws, Lsww_dis, p, cpw, Y, gv)[source]¶

cea.demand.hotwater_loads.calc_mw(schedule, Vw_lpd, Occ_m2p, Af, Pwater)[source]¶

cea.demand.hotwater_loads.calc_mww(schedule, Vww_lpd, Occ_m2p, Af, Pwater)[source]¶

Algorithm to calculate the hourly mass flow rate of domestic hot water

Parameters:	schedule – hourly DHW demand profile [1/h] Vww_lpd – DHW demand per person per day in [L/person/day] Occ_m2p – Occupant density in [m2/person] Af – Total floor area per building [m2] Pwater – water density [kg/m3]

cea.demand.occupancy_model module¶

Query schedules according to database

cea.demand.occupancy_model.calc_occ(list_uses, schedules, bpr)[source]¶

Calculate the occupancy in number of people for the whole building per timestep.

Parameters:	list_uses (list) – The list of uses used in the project schedules (list[ndarray[float]]) – The list of schedules defined for the project - in the same order as list_uses bpr (cea.demand.thermal_loads.BuildingPropertiesRow) – The properties of the building to calculate
Returns:	Occupancy as number of persons per timestep for the whole building
Return type:	ndarray

cea.demand.occupancy_model.calc_occ_schedule(list_uses, schedules, building_uses)[source]¶

Given schedule data for archetypical building uses, calc_occ_schedule calculates the schedule for a building with possibly a mixed schedule as defined in building_uses using a weighted average approach.

Parameters:	list_uses (list) – The list of uses used in the project schedules (list[ndarray[float]]) – The list of schedules defined for the project - in the same order as list_uses building_uses (dict[str, float]) – for each use in list_uses, the percentage of that use for this building. Sum of values is 1.0
Returns:
Return type:	ndarray

cea.demand.occupancy_model.read_schedules(use, x)[source]¶

cea.demand.occupancy_model.schedule_maker(dates, locator, list_uses)[source]¶

cea.demand.rc_model_SIA module¶

cea.demand.rc_model_SIA.calc_f_ic(a_t, a_m, h_ec)[source]¶

Parameters:	a_t – see bpr.rc_model['Atot'] a_m – see bpr.rc_model['Am'] h_ec – see calc_h_ec
Returns:

cea.demand.rc_model_SIA.calc_f_im(a_t, a_m)[source]¶

Parameters:	a_t – see bpr.rc_model['Atot'] a_m – see bpr.rc_model['Am']
Returns:

cea.demand.rc_model_SIA.calc_f_sc(a_t, a_m, a_w, h_ec)[source]¶

Parameters:	a_t – see bpr.rc_model['Atot'] a_m – see bpr.rc_model['Am'] a_w – see bpr.rc_model['Aw'] h_ec – see calc_h_ec
Returns:

cea.demand.rc_model_SIA.calc_f_sm(a_t, a_m, a_w)[source]¶

Parameters:	a_t – bpr.rc_model[‘Atot’] a_m – bpr.rc_model[‘Am’] a_w – bpr.rc_model[‘Aw’]
Returns:

cea.demand.rc_model_SIA.calc_h_1(h_ea, h_ac)[source]¶

cea.demand.rc_model_SIA.calc_h_2(h_1, h_ec)[source]¶

cea.demand.rc_model_SIA.calc_h_3(h_2, h_mc)[source]¶

cea.demand.rc_model_SIA.calc_h_ac(a_t)[source]¶

Parameters:	a_t – equivalent to bpr.rc_model['Atot']
Returns:

cea.demand.rc_model_SIA.calc_h_ea(m_ve_mech, m_ve_window, m_ve_inf_simple)[source]¶

cea.demand.rc_model_SIA.calc_h_ec(Htr_w)[source]¶

cea.demand.rc_model_SIA.calc_h_em(h_op_m, h_mc)[source]¶

cea.demand.rc_model_SIA.calc_h_j_em()[source]¶

cea.demand.rc_model_SIA.calc_h_mc(a_m)[source]¶

Parameters:	a_m – see bpr.rc_model['Am']
Returns:

cea.demand.rc_model_SIA.calc_h_op_m(Htr_op)[source]¶

cea.demand.rc_model_SIA.calc_h_tabs()[source]¶

cea.demand.rc_model_SIA.calc_phi_a(phi_hc_cv, phi_i_l, phi_i_a, phi_i_p, I_sol)[source]¶

cea.demand.rc_model_SIA.calc_phi_c(phi_hc_r, phi_i_l, phi_i_a, phi_i_p, I_sol, f_ic, f_sc)[source]¶

cea.demand.rc_model_SIA.calc_phi_hc_cv(phi_hc, f_hc_cv)[source]¶

cea.demand.rc_model_SIA.calc_phi_hc_r(phi_hc, f_hc_cv)[source]¶

cea.demand.rc_model_SIA.calc_phi_i_a(Eaf, Qcdataf, Qcref)[source]¶

cea.demand.rc_model_SIA.calc_phi_i_l(Elf)[source]¶

cea.demand.rc_model_SIA.calc_phi_i_p(Qs_Wp, people)[source]¶

cea.demand.rc_model_SIA.calc_phi_m(phi_hc_r, phi_i_l, phi_i_a, phi_i_p, I_sol, f_im, f_sm)[source]¶

cea.demand.rc_model_SIA.calc_phi_m_tot(phi_m, phi_a, phi_c, theta_ea, theta_em, theta_ec, h_1, h_2, h_3, h_ec, h_ea, h_em)[source]¶

cea.demand.rc_model_SIA.calc_phi_m_tot_tabs()[source]¶

cea.demand.rc_model_SIA.calc_phi_tabs()[source]¶

cea.demand.rc_model_SIA.calc_rc_model_temperatures(phi_hc_cv, phi_hc_r, bpr, tsd, t)[source]¶

cea.demand.rc_model_SIA.calc_rc_model_temperatures_cooling(phi_hc, bpr, tsd, t)[source]¶

This function executes the equations of SIA 2044 R-C-Building-Model to calculate the node temperatures for a given cooling energy demand

:py:func: cea.demand.rc_model_SIA.lookup_f_hc_cv_cooling :py:func: cea.demand.rc_model_SIA.calc_phi_hc_cv :py:func: cea.demand.rc_model_SIA.calc_phi_hc_r :py:func: cea.demand.rc_model_SIA.calc_rc_model_temperatures

Author: Gabriel Happle Date: FEB 2017

Parameters:	phi_hc (float) – Heating or cooling energy demand of building bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	R-C-Building-Model node temperatures
Return type:	dict

cea.demand.rc_model_SIA.calc_rc_model_temperatures_heating(phi_hc, bpr, tsd, t)[source]¶

This function executes the equations of SIA 2044 R-C-Building-Model to calculate the node temperatures for a given heating energy demand

:py:func: cea.demand.rc_model_SIA.lookup_f_hc_cv_heating :py:func: cea.demand.rc_model_SIA.calc_phi_hc_cv :py:func: cea.demand.rc_model_SIA.calc_phi_hc_r :py:func: cea.demand.rc_model_SIA.calc_rc_model_temperatures

Author: Gabriel Happle Date: FEB 2017

Parameters:	phi_hc (float) – Heating or cooling energy demand of building bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	R-C-Building-Model node temperatures
Return type:	dict

cea.demand.rc_model_SIA.calc_rc_model_temperatures_no_heating_cooling(bpr, tsd, t)[source]¶

Calculates R-C-Model temperatures are calculated with zero heating/cooling power according to SIA 2044 procedure.

:py:func: cea.demand.rc_model_SIA.calc_rc_model_temperatures_no_heating_cooling

Author: Gabriel Happle Date: FEB 2017

Parameters:	bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	R-C-Model node temperatures
Return type:	dict

cea.demand.rc_model_SIA.calc_theta_a(phi_a, theta_ea, theta_c, h_ac, h_ea)[source]¶

cea.demand.rc_model_SIA.calc_theta_c(phi_a, phi_c, theta_ea, theta_ec, theta_m, h_1, h_mc, h_ec, h_ea)[source]¶

cea.demand.rc_model_SIA.calc_theta_e_star()[source]¶

cea.demand.rc_model_SIA.calc_theta_ea(m_ve_mech, m_ve_window, m_ve_inf_simple, theta_ve_mech, T_ext)[source]¶

cea.demand.rc_model_SIA.calc_theta_ec(T_ext)[source]¶

cea.demand.rc_model_SIA.calc_theta_em(T_ext)[source]¶

cea.demand.rc_model_SIA.calc_theta_m(theta_m_t, theta_m_t_1)[source]¶

cea.demand.rc_model_SIA.calc_theta_m_t(phi_m_tot, theta_m_t_1, h_em, h_3, c_m)[source]¶

cea.demand.rc_model_SIA.calc_theta_o(theta_a, theta_c)[source]¶

cea.demand.rc_model_SIA.calc_theta_tabs_su()[source]¶

cea.demand.rc_model_SIA.has_cooling_demand(bpr, tsd, t)[source]¶

This function checks whether the building R-C-Model has a cooling demand according to the procedure in SIA 2044. R-C-Model temperatures are calculated with zero cooling power and checked versus the set-point temperature. Function includes a temperature tolerance according to the precision of the result reporting.

:py:func: cea.demand.rc_model_SIA.calc_rc_model_temperatures_no_heating_cooling

Author: Gabriel Happle Date: FEB 2017

Parameters:	bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	True or False
Return type:	bool

cea.demand.rc_model_SIA.has_heating_demand(bpr, tsd, t)[source]¶

This function checks whether the building R-C-Model has a heating demand according to the procedure in SIA 2044. R-C-Model temperatures are calculated with zero heating power and checked versus the set-point temperature. Function includes a temperature tolerance according to the precision of the result reporting.

:py:func: cea.demand.rc_model_SIA.calc_rc_model_temperatures_no_heating_cooling

Author: Gabriel Happle Date: FEB 2017

Parameters:	bpr (BuildingPropertiesRow) – Building Properties tsd (dict) – Time series data of building t (int) – time step / hour of the year
Returns:	True or False
Return type:	bool

cea.demand.rc_model_SIA.lookup_f_hc_cv_cooling(bpr)[source]¶

cea.demand.rc_model_SIA.lookup_f_hc_cv_heating(bpr)[source]¶

cea.demand.rc_model_crank_nicholson_procedure module¶

cea.demand.rc_model_crank_nicholson_procedure.calc_rc_model_demand_heating_cooling(bpr, tsd, t, gv)[source]¶

Crank-Nicholson Procedure to calculate heating / cooling demand of buildings following the procedure in 2.3.2 in SIA 2044 / Korrigenda C1 zum Merkblatt SIA 2044:2011 / Korrigenda C2 zum Mekblatt SIA 2044:2011

Special procedures for updating ventilation air AC-heated and AC-cooled buildings

Author: Gabriel Happle Date: 01/2017

Parameters:	bpr – building properties row object tsd – time series data dict t – time step / hour of year [0..8760] gv – globalvars
Returns:	updates values in tsd

cea.demand.rc_model_crank_nicholson_procedure.update_tsd_no_cooling(tsd, t)[source]¶

updates NaN values in tsd for case of no cooling demand

Author: Gabriel Happle Date: 01/2017

Parameters:	tsd – time series data dict t – time step / hour of year [0..8760]
Returns:	updates tsd values

cea.demand.rc_model_crank_nicholson_procedure.update_tsd_no_heating(tsd, t)[source]¶

updates NaN values in tsd for case of no heating demand

Author: Gabriel Happle Date: 01/2017

Parameters:	tsd – time series data dict t – time step / hour of year [0..8760]
Returns:	updates tsd values

cea.demand.refrigeration_loads module¶

refrigeration loads

cea.demand.refrigeration_loads.calc_Qcref(Eref)[source]¶

cea.demand.sensible_loads module¶

Sensible space heating and space cooling loads EN-13970

cea.demand.sensible_loads.calc_Asol(t, bpr, gv)[source]¶

This function calculates the effective collecting solar area accounting for use of blinds according to ISO 13790, for the sake of simplicity and to avoid iterations, the delta is calculated based on the last time step.

Parameters:	t – time of the year bpr – building properties object gv – global variables class
Returns:

cea.demand.sensible_loads.calc_I_rad(t, tsd, bpr, Rse)[source]¶

This function calculates the solar radiation re-irradiated from a building to the sky according to ISO 13790

Parameters:	t – hour of the year tsd – time series dataframe bpr – building properties object gv – global variables class
Returns:	I_rad: vector solar radiation re-irradiated to the sky.

cea.demand.sensible_loads.calc_I_sol(t, bpr, tsd, gv)[source]¶

This function calculates the net solar radiation (incident -reflected - re-irradiated) according to ISO 13790

Parameters:	t – hour of the year bpr – building properties object tsd – time series dataframe gv – global variables class
Returns:	I_sol: vector of net solar radiation to the building I_rad: vector solar radiation re-irradiated to the sky.

cea.demand.sensible_loads.calc_Qgain_lat(people, X_ghp, sys_e_cooling, sys_e_heating)[source]¶

cea.demand.sensible_loads.calc_Qgain_sen(t, tsd, bpr, gv)[source]¶

cea.demand.sensible_loads.calc_Qhs_Qcs_dis_ls(tair, text, Qhs, Qcs, tsh, trh, tsc, trc, Qhs_max, Qcs_max, D, Y, SystemH, SystemC, Bf, Lv)[source]¶

calculates distribution losses based on ISO 15316

cea.demand.sensible_loads.calc_Qhs_Qcs_sys_max(Af, prop_HVAC)[source]¶

cea.demand.sensible_loads.calc_hr(emissivity, theta_ss)[source]¶

This function calculates the external radiative heat transfer coefficient according to ISO 13790

Parameters:	emissivity – emissivity of the considered surface theta_ss – delta of temperature between building surface and the sky.
Returns:	hr:

cea.demand.sensible_loads.calc_temperatures_emission_systems(tsd, bpr, Qcsf_0, Qhsf_0, gv)[source]¶

cea.demand.space_emission_systems module¶

Space emission systems (heating and cooling) EN 15316-2 prEN 15316-2:2014

cea.demand.space_emission_systems.calc_delta_theta_int_inc_cooling(cooling_system, control_system)[source]¶

Model of losses in the emission and control system for space heating and cooling.

Correction factor for the heating and cooling setpoints. Extracted from EN 15316-2

(see ceadatabasesCHSystemsemission_systems.xls for valid values for the heating and cooling system values)

T0 means there’s no heating/cooling systems installed, therefore, also no control systems for heating/cooling. In short, when the input system is T0, the output set point correction should be 0.0. So if there is no cooling systems, the setpoint_correction_for_space_emission_systems function input: (T1, T0, T1) (type_hs, type_cs, type_ctrl), return should be (2.65, 0.0), the control system is only specified for the heating system. In another case with no heating systems: input: (T0, T3, T1) return: (0.0, -2.0), the control system is only specified for the heating system.

Parameters:	heating_system (str) – The heating system used. Valid values: T0, T1, T2, T3, T4 cooling_system (str) – The cooling system used. Valid values: T0, T1, T2, T3 control_system (str) – The control system used. Valid values: T1, T2, T3, T4 - as defined in the contributors manual under Databases / Archetypes / Building Properties / Mechanical systems. T1 for none, T2 for PI control, T3 for PI control with optimum tuning, and T4 for room temperature control (electromagnetically/electronically).
Returns:	two delta T to correct the set point temperature, dT_heating, dT_cooling
Return type:	tuple(double, double)

cea.demand.space_emission_systems.calc_delta_theta_int_inc_heating(heating_system, control_system)[source]¶

Model of losses in the emission and control system for space heating and cooling.

Correction factor for the heating and cooling setpoints. Extracted from EN 15316-2

(see ceadatabasesCHSystemsemission_systems.xls for valid values for the heating and cooling system values)

T0 means there’s no heating/cooling systems installed, therefore, also no control systems for heating/cooling. In short, when the input system is T0, the output set point correction should be 0.0. So if there is no cooling systems, the setpoint_correction_for_space_emission_systems function input: (T1, T0, T1) (type_hs, type_cs, type_ctrl), return should be (2.65, 0.0), the control system is only specified for the heating system. In another case with no heating systems: input: (T0, T3, T1) return: (0.0, -2.0), the control system is only specified for the heating system.

Parameters:	heating_system (str) – The heating system used. Valid values: T0, T1, T2, T3, T4 cooling_system (str) – The cooling system used. Valid values: T0, T1, T2, T3 control_system (str) – The control system used. Valid values: T1, T2, T3, T4 - as defined in the contributors manual under Databases / Archetypes / Building Properties / Mechanical systems. T1 for none, T2 for PI control, T3 for PI control with optimum tuning, and T4 for room temperature control (electromagnetically/electronically).
Returns:	two delta T to correct the set point temperature, dT_heating, dT_cooling
Return type:	tuple(double, double)

cea.demand.space_emission_systems.calc_q_em_ls(q_em_out, delta_theta_int_inc, theta_int_inc, theta_e_comb, q_em_max)[source]¶

Eq. (8) in [prEN 15316-2:2014]

With modification of capping emission losses at system capacity [Happle 01/2017]

Parameters:	q_em_out – heating power of emission system (W) delta_theta_int_inc – delta temperature caused by all losses (K) theta_int_inc – equivalent room temperature (°C) theta_e_comb – ?comb? outdoor temperature (°C)
Returns:

cea.demand.space_emission_systems.calc_q_em_ls_cooling(bpr, tsd, hoy)[source]¶

calculation procedure for space emissions losses in the cooling case [prEN 15316-2:2014]

Returns:

cea.demand.space_emission_systems.calc_q_em_ls_heating(bpr, tsd, hoy)[source]¶

calculation procedure for space emissions losses in the heating case [prEN 15316-2:2014]

Returns:

cea.demand.space_emission_systems.calc_theta_e_comb_cooling(theta_e, bpr)[source]¶

Eq. (10) in [prEN 15316-2:2014]

Returns:

cea.demand.space_emission_systems.calc_theta_e_comb_heating(theta_e)[source]¶

Eq. (9) in [prEN 15316-2:2014]

Returns:

cea.demand.space_emission_systems.calc_theta_int_inc(theta_int_ini, delta_theta_int_inc)[source]¶

Eq. (1) in [prEN 15316-2:2014]

Parameters:	theta_int_ini –
Returns:

cea.demand.space_emission_systems.get_delta_theta_e_sol(bpr)[source]¶

Appendix B.7 in [prEN 15316-2:2014]

delta_theta_e_sol = 8K – for medium window fraction or internal loads (e.g. residential) delta_theta_e_sol = 12K – for large window fraction or internal loads (e.g. office)

Parameters:	bpr –
Returns:

cea.demand.thermal_loads module¶

cea.demand.ventilation_air_flows_detailed module¶

Ventilation according to [DIN-16798-7] and [ISO-9972]

[DIN-16798-7]

Energieeffizienz von Gebäuden - Teil 7: Modul M5-1, M 5-5, M 5-6, M 5-8 – Berechnungsmethoden zur Bestimmung der Luftvolumenströme in Gebäuden inklusive Infiltration; Deutsche Fassung prEN 16798-7:2014

[ISO-9972]

Wärmetechnisches Verhalten von Gebäuden – Bestimmung der Luftdurchlässigkeit von Gebäuden – Differenzdruckverfahren (ISO 9972:2015); Deutsche Fassung EN ISO 9972:2015

Convention: all temperature inputs in (°C)

cea.demand.ventilation_air_flows_detailed.allocate_default_leakage_paths(coeff_lea_zone, area_facade_zone, area_roof_zone, height_zone)[source]¶

Allocate default leakage paths according to B.1.3.17 in [1]

Parameters:	coeff_lea_zone – leakage coefficient of zone area_facade_zone – facade area of zone (m2) area_roof_zone – roof area of zone (m2) height_zone – height of zone (m)
Returns:	coeff_lea_path : coefficients of default leakage paths height_lea_path : heights of default leakage paths (m) orientation_lea_path : orientation index of default leakage paths (-)

cea.demand.ventilation_air_flows_detailed.allocate_default_ventilation_openings(coeff_vent_zone, height_zone)[source]¶

Allocate default ventilation openings according to B.1.3.13 in [1]

:param coeff_vent_zone : coefficient of ventilation openings of zone :param height_zone : height of zone (m)

Returns:	coeff_vent_path : coefficients of default ventilation opening paths height_vent_path : heights of default ventilation opening paths (m) orientation_vent_path : orientation index of default ventilation opening paths (-)

cea.demand.ventilation_air_flows_detailed.calc_air_flow_mass_balance(p_zone_ref, temp_zone, u_wind_10, temp_ext, dict_props_nat_vent, option)[source]¶

Air flow mass balance for iterative calculation according to 6.4.3.9 in [1]

:param p_zone_ref : zone reference pressure (Pa) :param temp_zone : air temperature in ventilation zone (°C) :param u_wind_10 : meteorological wind velocity (m/s) :param temp_ext : exterior air temperature (°C) :param dict_props_nat_vent : dictionary containing natural ventilation properties of zone :param option : ‘minimize’ = returns sum of air mass flows, ‘calculate’ = returns air mass flows

Returns:	sum of air mass flows in and out of zone in (kg/h)

cea.demand.ventilation_air_flows_detailed.calc_air_flows(temp_zone, u_wind, temp_ext, dict_props_nat_vent)[source]¶

Minimization of variable air flows as a function of zone gauge

Parameters:	temp_zone – zone indoor air temperature (°C) u_wind – wind velocity (m/s) temp_ext – exterior air temperature (°C) dict_props_nat_vent – dictionary containing natural ventilation properties of zone

qm_sum_in : total air mass flow rates into zone (kg/h) qm_sum_out : total air mass flow rates out of zone (kg/h)

cea.demand.ventilation_air_flows_detailed.calc_area_window_cros(dict_windows_building, r_window_arg)[source]¶

Calculate cross-ventilation window area according to the procedure in 6.4.3.5.4.3 in [1]

:param dict_windows_building : dictionary containing information of all windows in building :param r_window_arg : fraction of window opening (-)

Returns:	area_window_cros : effective window area for cross ventilation (m2)

cea.demand.ventilation_air_flows_detailed.calc_area_window_free(area_window_max, r_window_arg)[source]¶

Calculate free window opening area according to 6.4.3.5.2 in [1]

:param area_window_max : area of single operable window (m2) :param r_window_arg : fraction of window opening (-)

Returns:	area_window_free : open area of window (m2)

cea.demand.ventilation_air_flows_detailed.calc_area_window_tot(dict_windows_building, r_window_arg)[source]¶

Calculation of total open window area according to 6.4.3.5.2 in [1]

:param dict_windows_building : dictionary containing information of all windows in building :param r_window_arg : fraction of window opening (-)

Returns:	area_window_tot = total open area of windows in building (m2)

cea.demand.ventilation_air_flows_detailed.calc_coeff_lea_zone(qv_delta_p_lea_ref)[source]¶

Calculate default leakage coefficient of zone according to B.1.3.16 in [1]

Parameters:	qv_delta_p_lea_ref – air volume flow rate at reference pressure (m3/h)
Returns:	coeff_lea_zone : leakage coefficient of zone

cea.demand.ventilation_air_flows_detailed.calc_coeff_vent_zone(area_vent_zone)[source]¶

Calculate air volume flow coefficient of ventilation openings of zone according to 6.4.3.6.4 in [1]

:param area_vent_zone : total area of ventilation openings of zone (cm2)

:returns coeff_vent_zone : coefficient of ventilation openings of zone

cea.demand.ventilation_air_flows_detailed.calc_delta_p_path(p_zone_ref, height_path, temp_zone, coeff_wind_pressure_path, u_wind_site, temp_ext)[source]¶

Calculation of indoor-outdoor pressure difference at air path according to 6.4.2.4 in [1]

Parameters:	p_zone_ref – zone reference pressure (Pa) height_path – height of ventilation path (m) temp_zone – air temperature of ventilation zone in (°C) coeff_wind_pressure_path – wind pressure coefficient of ventilation path (-) u_wind_site – wind velocity (m/s) temp_ext – external air temperature (°C)
Returns:	delta_p_path, pressure difference across ventilation path (Pa)

cea.demand.ventilation_air_flows_detailed.calc_effective_stack_height(dict_windows_building)[source]¶

Calculation of effective stack height for window ventilation according to 6.4.3.4.1 in [1]

:param dict_windows_building : dictionary containing information of all windows in building

Returns:	height_window_stack : effective stack height of windows of building (m)

cea.demand.ventilation_air_flows_detailed.calc_qm_arg(factor_cros, temp_ext, dict_windows_building, u_wind_10, temp_zone, r_window_arg)[source]¶

Calculation of cross ventilated and non-cross ventilated window ventilation according to procedure in 6.4.3.5.4 in [1]

:param factor_cros : cross ventilation factor [0,1] :param temp_ext : exterior temperature (°C) :param dict_windows_building : dictionary containing information of all windows in building :param u_wind_10 : wind velocity (m/s) :param temp_zone : zone temperature (°C) :param r_window_arg : fraction of window opening (-)

Returns:	window ventilation air mass flows in (kg/h)

cea.demand.ventilation_air_flows_detailed.calc_qm_lea(p_zone_ref, temp_zone, temp_ext, u_wind_site, dict_props_nat_vent)[source]¶

Calculation of leakage infiltration and exfiltration air mass flow as a function of zone indoor reference pressure

Parameters:	p_zone_ref – zone reference pressure (Pa) temp_zone – air temperature in ventilation zone (°C) temp_ext – exterior air temperature (°C) u_wind_site – wind velocity (m/s) dict_props_nat_vent – dictionary containing natural ventilation properties of zone
Returns:	qm_lea_in : air mass flow rate into zone through leakages (kg/h) qm_lea_out : air mass flow rate out of zone through leakages (kg/h)

cea.demand.ventilation_air_flows_detailed.calc_qm_vent(p_zone_ref, temp_zone, temp_ext, u_wind_site, dict_props_nat_vent)[source]¶

Calculation of air flows through ventilation openings in the facade

:param p_zone_ref : zone reference pressure (Pa) :param temp_zone : zone air temperature (°C) :param temp_ext : exterior air temperature (°C) :param u_wind_site : wind velocity (m/s) :param dict_props_nat_vent : dictionary containing natural ventilation properties of zone

Returns:	qm_vent_in : air mass flow rate into zone through ventilation openings (kg/h) qm_vent_out : air mass flow rate out of zone through ventilation openings (kg/h)

cea.demand.ventilation_air_flows_detailed.calc_qv_delta_p_ref(n_delta_p_ref, vol_building)[source]¶

Calculate airflow at reference pressure according to 6.3.2 in [2]

Parameters:	n_delta_p_ref – air changes at reference pressure [1/h] vol_building – building_volume [m3]
Returns:	qv_delta_p_ref : air volume flow rate at reference pressure (m3/h)

cea.demand.ventilation_air_flows_detailed.calc_qv_lea_path(coeff_lea_path, delta_p_lea_path)[source]¶

Calculate volume air flow of single leakage path according to 6.4.3.6.5 in [1]

Parameters:	coeff_lea_path – coefficient of leakage path delta_p_lea_path – pressure difference across leakage path (Pa)
Returns:	qv_lea_path : volume flow rate across leakage path (m3/h)

cea.demand.ventilation_air_flows_detailed.calc_qv_vent_path(coeff_vent_path, delta_p_vent_path)[source]¶

Calculate volume air flow of single ventilation opening path according to 6.4.3.6.4 in [1]

:param coeff_vent_path : ventilation opening coefficient of air path :param delta_p_vent_path : pressure difference across air path (Pa)

Returns:	qv_vent_path : air volume flow rate across air path (m3/h)

cea.demand.ventilation_air_flows_detailed.calc_u_wind_site(u_wind_10)[source]¶

Adjusts meteorological wind velocity to site surroundings according to 6.4.2.2 in [1]

Parameters:	u_wind_10 – meteorological wind velocity (m/s)
Returns:	u_wind_site, site wind velocity (m/s)

cea.demand.ventilation_air_flows_detailed.get_properties_natural_ventilation(bpr, gv)[source]¶

gdf_geometry_building : GeoDataFrame containing geometry properties of single building gdf_architecture_building : GeoDataFrame containing architecture props of single building :param gv: globalvars :param bpr: building propert row

Returns:	dictionary containing natural ventilation properties of zone

cea.demand.ventilation_air_flows_detailed.lookup_coeff_wind_pressure(height_path, class_shielding, orientation_path, slope_roof, factor_cros)[source]¶

Lookup default wind pressure coefficients for air leakage paths according to B.1.3.3 in [1]

Parameters:	height_path – class_shielding – orientation_path – slope_roof – factor_cros –
Returns:	wind pressure coefficients (-)

Conventions:

class_shielding = 0 : open terrain class_shielding = 1 : normal class_shielding = 2 : shielded

orientation_path = 0 : facade facing wind

1 : facade not facing wind 2 : roof

factor_cros = 0 : cross ventilation not possible

= 1 : cross ventilation possible

cea.demand.ventilation_air_flows_detailed.testing()[source]¶

cea.demand.ventilation_air_flows_simple module¶

cea.demand.ventilation_air_flows_simple.calc_air_mass_flow_mechanical_ventilation(bpr, tsd, t)[source]¶

Calculates mass flow rate of mechanical ventilation at time step t according to ventilation control options and

building systems properties

Author: Gabriel Happle Date: 01/2017

Parameters:	bpr – Building properties row object tsd – Time series data dict t – time step [0..8760]
Returns:	updates tsd

cea.demand.ventilation_air_flows_simple.calc_air_mass_flow_window_ventilation(bpr, tsd, t)[source]¶

Calculates mass flow rate of window ventilation at time step t according to ventilation control options and

building systems properties

Author: Gabriel Happle Date: 01/2017

Parameters:	bpr – Building properties row object tsd – Time series data dict t – time step [0..8760]
Returns:	updates tsd

cea.demand.ventilation_air_flows_simple.calc_m_ve_leakage()[source]¶

cea.demand.ventilation_air_flows_simple.calc_m_ve_leakage_simple(bpr, tsd, gv)[source]¶

Calculates mass flow rate of leakage at time step t according to ventilation control options and

building systems properties

Estimation of infiltration air volume flow rate according to Eq. (3) in DIN 1946-6

Author: Gabriel Happle Date: 01/2017

Parameters:	bpr – Building properties row object tsd – Time series data dict gv – globalvars
Returns:	updates tsd

cea.demand.ventilation_air_flows_simple.calc_m_ve_required(bpr, tsd)[source]¶

Calculate required outdoor air ventilation rate according to occupancy

Author: Legacy Date: old

Parameters:	bpr – Building properties row object tsd – Time series data dict
Returns:	updates tsd

cea.demand.ventilation_air_flows_simple.calc_theta_ve_mech(bpr, tsd, t, gv)[source]¶

Calculates supply temperature of mechanical ventilation system according to ventilation control options and

building systems properties

Author: Gabriel Happle Date: 01/2017

Parameters:	bpr – Building properties row object tsd – Time series data dict t – time step [0..8760] gv – globalvars
Returns:	updates tsd

Module contents¶

Submodules¶

cea.geometry.geometry_reader module¶

algorithms for manipulation of building geometry

cea.geometry.geometry_reader.create_windows(df_prop_surfaces, gdf_building_architecture)[source]¶

Creates windows on exposed building surfaces according to building win-wall-ratio

Parameters:	df_prop_surfaces (DataFrame) – DataFrame containing all exposed building surfaces (this is the properties_surfaces.csv file from the radiation calculation) gdf_building_architecture (GeoDataFrame) – GeoDataFrame containing building architecture - this is the architecture.shp file from the scenario input, containing the win_wall column with the window to wall ratio.
Returns:	DataFrame containing all windows of all buildings
Return type:	DataFrame

Sample rows of output::

       angle_window  area_window  height_window_above_ground          0            90     1.910858                         1.5
1            90     2.276739                         1.5
2            90     2.276739                         4.5
3            90     2.276739                         7.5
4            90     2.276739                        10.5

   height_window_in_zone name_building  orientation_window
0                    1.5       B140589                   0
1                    1.5       B140590                 180
2                    4.5       B140590                 180
3                    7.5       B140590                 180
4                   10.5       B140590                 180

[5 rows x 6 columns]

cea.geometry.geometry_reader.get_building_geometry_ventilation(gdf_building_geometry)[source]¶

:param gdf_building_geometry : GeoDataFrame contains single building

Returns:	building properties for natural ventilation calculation

Module contents¶

Submodules¶

cea.plots.graphs_demand module¶

graphs algorithm

cea.plots.graphs_demand.create_demand_graph_for_building(analysis_fields, area_df, color_palette, fields_date, locator, name, total_demand)[source]¶

cea.plots.graphs_demand.demand_graph_fields(scenario_path)[source]¶

Lists the available fields for the demand graphs - these are fields that are present in both the building demand results files as well as the totals file (albeit with different units).

cea.plots.graphs_demand.graphs_demand(locator, analysis_fields, gv)[source]¶

algorithm to print graphs in PDF concerning the dynamics of each and all buildings

Parameters:	locator (inputlocator.InputLocator) – an InputLocator set to the scenario to compute analysis_fields (list[string]) – list of fields (column names in Totals.csv) to analyse
Returns:	Graphs of each building and total: .Pdf heat map file per variable of interest n.

cea.plots.graphs_demand.run_as_script(scenario_path=None, analysis_fields=['Ealf_kWh', 'Qhsf_kWh', 'Qwwf_kWh', 'Qcsf_kWh'])[source]¶

cea.plots.graphs_optimization module¶

cea.plots.graphs_solar_potential module¶

Solar graphs

cea.plots.graphs_solar_potential.calc_graph_I_sol(hourlydata_groups)[source]¶

cea.plots.graphs_solar_potential.calc_graph_PV(results, results_perarea)[source]¶

cea.plots.graphs_solar_potential.calc_graph_SC(result, Tin)[source]¶

cea.plots.heatmaps module¶

cea.plots.scenario_plots module¶

scenario_plots.py

Create a list of plots for comparing multiple scenarios.

cea.plots.scenario_plots.create_page_demand(locators, pdf, scenario_names)[source]¶

Create Page one: Demand :param locators: list of InputLocators, one for each scenario :param pdf: the PdfFile to write the page to :param scenario_names: list of scenario names :return: None

cea.plots.scenario_plots.create_page_lca_embodied(locators, pdf, scenario_names)[source]¶

Create Page Two: LCA Embodied :param locators: list of InputLocators, one for each scenario :param pdf: the PdfFile to write the page to :param scenario_names: list of scenario names :return: None

cea.plots.scenario_plots.create_page_lca_operation(locators, pdf, scenario_names)[source]¶

Create Page Three: LCA Operation :param locators: list of InputLocators, one for each scenario :param pdf: the PdfFile to write the page to :param scenario_names: list of scenario names :return: None

cea.plots.scenario_plots.plot_demand(ax, locators, scenario_names, column, title)[source]¶

cea.plots.scenario_plots.plot_lca_embodied(ax, locators, scenario_names, column, title, unit)[source]¶

cea.plots.scenario_plots.plot_lca_operation(ax, locators, scenario_names, column, title, unit)[source]¶

cea.plots.scenario_plots.plot_scenarios(scenarios, output_file)[source]¶

List each scenario in the folder scenario_root and plot demand and lca (operations, embodied) data.

Parameters:	scenarios – A list of scenario folders. output_file – The filename (pdf) to save the results as.
Returns:	(None)

cea.plots.scenario_plots.run_as_script(scenario_folders=None, output_file=None)[source]¶

cea.plots.sensitivity_demand_graphs module¶

Graphs for sensitivity_demand.py

cea.plots.sensitivity_demand_graphs.graph(locator, parameters, method, samples)[source]¶

Parameters:	locator – locator class parameters – list of output parameters to analyse method – ‘morris’ or ‘sobol’ methods samples – number of samples to calculate
Returns:	.pdf file per output_parameter stored in locator.get_sensitivity_plots_file()

cea.plots.sensitivity_demand_graphs.run_as_script()[source]¶

cea.plots.timeseries_interactive_graph module¶

Module contents¶

Submodules¶

cea.resources.geothermal module¶

cea.resources.geothermal.calc_ground_temperature(T_ambient, gv)[source]¶

Calculates hourly ground temperature fluctuation over a year following [Kusuda, T. et al., 1965]_.

Parameters:	T_ambient (np array) – vector with outdoor temperature gv – globalvar.py
Return Tg:	vector with ground temperatures in [K]
Rtype Tg:	np array

..[Kusuda, T. et al., 1965] Kusuda, T. and P.R. Achenbach (1965). Earth Temperatures and Thermal Diffusivity at Selected Stations in the United States. ASHRAE Transactions. 71(1):61-74

cea.resources.natural_gas module¶

natural gas

cea.resources.natural_gas.calc_Cinv_gas(PnomGas, gV)[source]¶

Calculate investment cost of natural gas connections.

Parameters:	PnomGas (float) – peak natural gas supply in [W] gV – globalvar.py
Returns InvCa:
Rtype InvCa:

cea.resources.radiation module¶

Module contents¶

Submodules¶

cea.technologies.blinds module¶

blinds

cea.technologies.blinds.calc_blinds_activation(radiation, g_gl, Rf_sh)[source]¶

This function calculates the blind operation according to ISO 13790.

Parameters:	radiation – radiation in [W/m2] g_gl – window g value Rf_sh – shading factor

cea.technologies.boilers module¶

condensing boilers

cea.technologies.boilers.calc_Cinv_boiler(Q_design, Q_annual, gV)[source]¶

Calculates the annual cost of a boiler (based on A+W cost of oil boilers) [CHF / a] and Faz. 2012 data

:type Q_design : float :param Q_design: Design Load of Boiler in [W]

:type Q_annual : float :param Q_annual: Annual thermal load required from Boiler in [Wh]

Parameters:	gV – globalvar.py

:rtype InvCa : float :returns InvCa: Annualized investment costs in CHF/a including Maintenance Cost

cea.technologies.boilers.calc_Cop_boiler(Q_load, Q_design, T_return_to_boiler)[source]¶

This function calculates efficiency for operation of condensing Boilers based on LHV. This efficiency accounts for boiler efficiency only (not plant efficiency!)

operational efficiency after:

http://www.greenshootscontrols.net/?p=153

Parameters:	Q_load (float) – Load of time step Q_design (float) – Design Load of Boiler

:type T_return_to_boiler : float :param T_return_to_boiler: Return Temperature of the network to the boiler [K]

Retype boiler_eff:
	float
Returns boiler_eff:
	efficiency of Boiler (Lower Heating Value), in abs. numbers

cea.technologies.boilers.cond_boiler_op_cost(Q_therm, Q_design, T_return_to_boiler, BoilerFuelType, ElectricityType, gV)[source]¶

Calculates the operation cost of a Condensing Boiler (only operation, not annualized cost)

:type Q_therm : float :param Q_therm: Load of time step

Parameters:	Q_design (float) – Design Load of Boiler

:type T_return_to_boiler : float :param T_return_to_boiler: return temperature to Boiler (from DH network)

Parameters:	gV – globalvar.py

:rtype C_boil_therm : float :returns C_boil_therm: Total generation cost for required load (per hour) in CHF

:rtype C_boil_per_Wh : float :returns C_boil_per_Wh: cost per Wh in CHF / kWh

:rtype Q_primary : float :returns Q_primary: required thermal energy per hour (in Wh Natural Gas)

Rtype E_aux_Boiler:
	float
Returns E_aux_Boiler:
	auxiliary electricity of boiler operation

cea.technologies.boilers.cond_boiler_operation(Q_load, Q_design, T_return_to_boiler)[source]¶

This function calculates efficiency for operation of condensing Boilers at DH plant based on LHV. This efficiency accounts for boiler efficiency only (not plant efficiency!)

operational efficiency after:

http://www.greenshootscontrols.net/?p=153

Parameters:	Q_load (float) – Load of time step Q_design (float) – Design Load of Boiler

:type T_return_to_boiler : float :param T_return_to_boiler: Return Temperature of the network to the boiler [K]

Retype boiler_eff:
	float
Returns boiler_eff:
	efficiency of Boiler (Lower Heating Value), in abs. numbers

cea.technologies.chillers module¶

Vapor-compressor chiller

cea.technologies.chillers.calc_Cinv_VCC(qcold, gV)[source]¶

Annualized investment costs for the vapor compressor chiller

:type qcold : float :param qcold: peak cooling demand in [W] :param gV: globalvar.py

Returns InvCa:	annualized chiller investment cost in CHF/a
Rtype InvCa:	float

cea.technologies.chillers.calc_VCC(mdot, tsup, tret, gV)[source]¶

For the operation of a Vapor-compressor chiller between a district cooling network and a condenser with fresh water to a cooling tower following [D.J. Swider, 2003]_.

:type mdot : float :param mdot: plant supply mass flow rate to the district cooling network :type tsup : float :param tsup: plant supply temperature to DCN :type tret : float :param tret: plant return temperature from DCN :param gV: globalvar.py

:rtype wdot : float :returns wdot: chiller electric power requirement :rtype qhotdot : float :returns qhotdot: condenser heat rejection

..[D.J. Swider, 2003] D.J. Swider (2003). A comparison of empirically based steady-state models for vapor-compression liquid chillers. Applied Thermal Engineering.

cea.technologies.cogeneration module¶

cogeneration (combined heat and power)

cea.technologies.cogeneration.CC_Op(wdot, gt_size, fuel, tDH, gV)[source]¶

Operation Function of Combined Cycle at given electricity Demand (wdot). The gas turbine (GT) exhaust gas is used by the steam turbine (ST).

:type wdot : float :param wdot: Electric load that is demanded to the gas turbine (only GT output, not CC output!) :type gt_size : float :param gt_size: size of the gas turbine and (not CC)(P_el_max) :type fuel : string :param fuel: fuel used, either ‘NG’ (natural gas) or ‘BG’ (biogas) :type tDH : float :param tDH: plant supply temperature to district heating network (hot) :param gV: globalvar.py

:rtype wtot : float :returns wtot: total electric power output from the combined cycle (both GT + ST !) :rtype qdot : float :returns qdot: thermal output from teh combined cycle :rtype eta_elec : float :returns eta_elec: total electric efficiency :rtype eta_heat : float :returns eta_heat: total thermal efficiency :rtype eta_all : float :returns eta_all: sum of total electric and thermal efficiency

cea.technologies.cogeneration.GT_fullLoadParam(gt_size, fuel, gV)[source]¶

Calculates gas turbine efficiency and exhaust gas mass flow rate at full load.

:type gt_size : float :param gt_size: Maximum electric load that is demanded to the gas turbine :type fuel : string :param fuel: fuel used, either NG (Natural Gas) or BG (Biogas) :param gV: globalvar.py

:rtype eta0 : float :returns eta0: efficiency at full load :rtype mdot0 : float :returns mdot0: exhaust gas mass flow rate at full load

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.cogeneration.GT_partLoadParam(wdot, gt_size, eta0, mdot0, fuel, gV)[source]¶

Calculates GT operational parameters at part load

:type wdot : float :param wdot: GT electric output (load) :type gt_size : float :param gt_size: Maximum electric load that is demanded to the gas turbine :type eta0 : float :param eta0: GT part-load electric efficiency :type mdot0 : float :param mdot0: GT part-load exhaust gas mass flow :type fuel : string :param fuel: fuel used, either ‘NG’ (natural gas) or ‘BG’ (biogas)

:rtype eta : float :returns eta: GT part-load electric efficiency :rtype mdot : float :returns mdot: GT part-load exhaust gas mass flow rate :rtype texh : float :returns texh: exhaust gas temperature :rtype mdotfuel : float :returns mdotfuel: mass flow rate of fuel(gas) requirement

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.cogeneration.ST_Op(mdot, texh, tDH, fuel, gV)[source]¶

Operation of a double pressure (LP,HP) steam turbine connected to a district heating network following [C. Weber, 2008]_

:type mdot : float :param mdot: GT part-load exhaust gas mass flow rate :type texh : float :param texh: GT exhaust gas temperature :type tDH : float :param tDH: plant supply temperature to district heating network (hot) :param fuel: fuel used, either ‘NG’ (natural gas) or ‘BG’ (biogas) :type tDH : float :param gV: globalvar.py

:rtype qdot : float :returns qdot: heat power supplied to the DHN :rtype wdotfin : float :returns wdotfin: electric power generated from the steam cycle

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.cogeneration.calc_Cinv_CCT(CC_size, gV)[source]¶

Annualized investment costs for the Combined cycle

:type CC_size : float :param CC_size: Electrical size of the CC

:rtype InvCa : float :returns InvCa: annualized investment costs in CHF

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.cogeneration.calc_Cinv_FC(P_design, gV)[source]¶

Calculates the investment cost of a Fuel Cell in CHF

http://hexis.com/sites/default/files/media/publikationen/140623_hexis_galileo_ibb_profitpaket.pdf?utm_source=HEXIS+Mitarbeitende&utm_campaign=06d2c528a5-1_Newsletter_2014_Mitarbeitende_DE&utm_medium=email&utm_term=0_e97bc1703e-06d2c528a5-

:type P_design : float :param P_design: Design thermal Load of Fuel Cell [W_th]

Rtype InvCa:	float
Returns InvCa:	annualized investment costs in CHF

cea.technologies.cogeneration.calc_Cop_CCT(GT_SIZE, T_DH_Supply, fuel, gV)[source]¶

The function iterate the CCT operation between its nominal capacity and minimum load and generate linear functions of the GT operation.

This generated function calculates Operation Point and associated costs of the cogeneration at given thermal load (Q_therm_requested).

How to use the return functions : input Q_therm_requested into the output interpolation functions Conditions: not below or above boundaries Q_therm_min & Q_therm_max

:type GT_SIZE : float :param GT_SIZE: Nominal capacity of Gas Turbine (only GT not cogeneration)

:type T_DH_Supply : float :param T_DH_Supply: CHP plant supply temperature to DHN

:type fuel : string :param fuel: type of fuel, either “NG” or “BG”

Parameters:	gV – globalvar.py

:rtype wdot_interpol : function :returns wdot_interpol: interpolation function for part load electricity requirement for given Q_therm_requested

Rtype Q_used_prim_interpol:
	function
Returns Q_used_prim_interpol:
	interpolation function, primary energy used for given Q_therm_requested

:rtype cost_per_Wh_th_incl_el_interpol : function :returns cost_per_Wh_th_incl_el_interpol: interpolation function, operation cost per thermal energy generated at Q_therm_requested

:rtype Q_therm_min : float :returns Q_therm_min: minimum thermal energy output

:rtype Q_therm_max : float :returns Q_therm_max: maximum thermal energy output

Rtype eta_elec_interpol:
	function
Returns eta_elec_interpol:
	interpolation function, electrical efficiency at Q_therm_requested

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.cogeneration.calc_eta_FC(Q_load, Q_design, phi_threshold, approach_call)[source]¶

Efficiency for operation of a SOFC (based on LHV of NG) including all auxiliary losses Valid for Q_load in range of 1-10 [kW_el]

Modeled after:

Approach A (NREL Approach):

http://energy.gov/eere/fuelcells/distributedstationary-fuel-cell-systems and NREL : p.5 of [M. Zolot et al., 2004]_

Approach B (Empiric Approach): [Iain Staffell]_

:type Q_load : float :param Q_load: Load at each time step

:type Q_design : float :param Q_design: Design Load of FC

:type phi_threshold : float :param phi_threshold: where Maximum Efficiency is reached, used for Approach A

:type approach_call : string :param appraoch_call: choose “A” or “B”: A = NREL-Approach, B = Empiric Approach

:rtype eta_el : float :returns eta_el: electric efficiency of FC (Lower Heating Value), in abs. numbers

:rtype Q_fuel : float :returns Q_fuel: Heat demand from fuel (in Watt)

..[M. Zolot et al., 2004] M. Zolot et al., Analysis of Fuel Cell Hybridization and Implications for Energy Storage Devices, NREL, 4th International Advanced Automotive Battery. http://www.nrel.gov/vehiclesandfuels/energystorage/pdfs/36169.pdf

..[Iain Staffell, 2009] Iain Staffell, For Domestic Heat and Power: Are They Worth It?, PhD Thesis, Birmingham: University of Birmingham. http://etheses.bham.ac.uk/641/1/Staffell10PhD.pdf

cea.technologies.controllers module¶

controllers

cea.technologies.controllers.calc_simple_temp_control(tsd, prop_comfort, limit_inf_season, limit_sup_season, weekday)[source]¶

cea.technologies.controllers.calc_simple_ventilation_control(ve, people, Af, gv, hour_day, hour_year, n50)[source]¶

Modified version of calc_simple_ventilation_control from functions. Fixed infiltration according to schedule is only considered for mechanically ventilated buildings.

ve : required ventilation rate according to schedule (?) people : occupancy schedules (pax?) Af : conditioned floor area (m2) gv : globalvars hour_day : hour of the day [0..23] hour_year : hour of the year [0..8760] n50 : building envelope leakiness from archetypes

q_req : required ventilation rate schedule (m3/s)

cea.technologies.controllers.calc_ventialtion_HVAC_buildings(area_envelope, HVAC_on, Tin, Tout, ws, method)[source]¶

infiltration according to energy plus, blast, or DOE2 tools

cea.technologies.controllers.temperature_control_tabs(bpr, tsd, hoy, gv, control)[source]¶

Controls for TABS operating temperature based on the operating parameters defined by Koschenz and Lehmann “Thermoaktive Bauteilsysteme (TABS)” (2000), that is: maximum surface temperature and maximum temperature difference between TABS surface and air. If either of these is exceeded, they are set to the maximum and all temperatures are recalculated. The formulas below are simply reformulations of the calculations in the R-C model.

cea.technologies.cooling_tower module¶

System Modeling: Cooling tower

cea.technologies.cooling_tower.calc_CT(qhotdot, Qdesign, gV)[source]¶

For the operation of a water condenser + direct cooling tower based on [B. Stephane, 2012]_

:type qhotdot : float :param qhotdot: heating power to condenser, From Model_VCC :type Qdesign : float :param Qdesign: Max cooling power

:type wdot : float :param wdot: electric power needed for the variable speed drive fan

..[B. Stephane, 2012] B. Stephane (2012), Evidence-Based Model Calibration for Efficient Building Energy Services. PhD Thesis, University de Liege, Belgium

cea.technologies.cooling_tower.calc_Cinv_CT(CT_size, gV)[source]¶

Annualized investment costs for the Combined cycle

:type CT_size : float :param CT_size: Size of the Cooling tower in [W]

:rtype InvCa : float :returns InvCa: annualized investment costs in Dollars

cea.technologies.furnace module¶

furnaces

cea.technologies.furnace.calc_Cinv_furnace(Q_design, Q_annual, gv)[source]¶

Calculates the annualized investment cost of a Furnace based on Bioenergy 2020 (AFO) and POLYCITY Ostfildern

:type Q_design : float :param Q_design: Design Load of Boiler

:type Q_annual : float :param Q_annual: annual thermal Power output [Wh]

Parameters:	gV – globalvar.py

:rtype InvC_return : float :returns InvC_return: total investment Cost for building the plant

:rtype InvCa : float :returns InvCa: annualized investment costs in [CHF] including O&M

cea.technologies.furnace.calc_eta_furnace(Q_load, Q_design, T_return_to_boiler, MOIST_TYPE, gv)[source]¶

Efficiency for Furnace Plant (Wood Chip CHP Plant, Condensing Boiler) based on LHV.

Capacity : 1-10 [MW], Minimum Part Load: 30% of P_design Source: POLYCITY HANDBOOK 2012

:type Q_load : float :param Q_load: Load of time step

:type Q_design : float :param Q_design: Design Load of Boiler

:type T_return_to_boiler : float :param T_return_to_boiler: return temperature to the boiler

:type MOIST_TYPE : float :param MOIST_TYPE: moisture type of the fuel, set in MasterToSlaveVariables (‘wet’ or ‘dry’)

Parameters:	gV – globalvar.py

up to 6MW_therm_out Capacity proven! = 8 MW th (burner)

:rtype eta_therm : float :returns eta_therm: thermal Efficiency of Furnace (LHV), in abs. numbers

:rtype eta_el : float :returns eat_el: electric efficiency of Furnace (LHV), in abs. numbers

:rtyp;e Q_aux : float :returns Q_aux: auxiliary power for Plant operation [W]

cea.technologies.furnace.furnace_op_cost(Q_therm, Q_design, T_return_to_boiler, MOIST_TYPE, gv)[source]¶

Calculates the operation cost of a furnace plant (only operation, no annualized cost!)

:type Q_therm : float :param Q_therm: thermal energy required from furnace plant in [Wh]

:type Q_design : float :param Q_design: Design Load of Boiler [W]

:type T_return_to_boiler : float :param T_return_to_boiler: return temperature to the boiler

:type MOIST_TYPE : float :param MOIST_TYPE: moisture type of the fuel, set in MasterToSlaveVariables (‘wet’ or ‘dry’)

Parameters:	gV – globalvar.py

:rtype C_furn : float :returns C_furn: Total generation cost for required load (per hour) in [CHF], including profits from electricity sold

:rtype C_furn_per_kWh : float :returns C_furn_per_kWh: cost generation per kWh thermal energy produced in [Rp / kWh], including profits from electricity sold

:rtype Q_primary : float :returns Q_primary: required thermal energy per hour [Wh] of wood chips

:rtype E_furn_el_produced : float :returns E_furn_el_produced: electricity produced by furnace plant in [Wh]

cea.technologies.heat_exchangers module¶

heat exchangers

cea.technologies.heat_exchangers.calc_Cinv_HEX(Q_design, gV)[source]¶

Calculates the cost of a heat exchanger (based on A+W cost of oil boilers) [CHF / a]

:type Q_design : float :param Q_design: Design Load of Boiler

Parameters:	gV – globalvar.py

:rtype InvC_return : float :returns InvC_return: total investment Cost in [CHF]

:rtype InvCa : float :returns InvCa: annualized investment costs in [CHF/a]

cea.technologies.heating_coils module¶

Heating and cooling coils of Air handling units

cea.technologies.heating_coils.calc_cooling_coil(Qcsf, Qcsf_0, Ta_sup_cs, Ta_re_cs, Tcs_sup_0, Tcs_re_0, ma_sup_cs, ma_sup_0, Ta_sup_0, Ta_re_0, Cpa, gv)[source]¶

cea.technologies.heating_coils.calc_heating_coil(Qhsf, Qhsf_0, Ta_sup_hs, Ta_re_hs, Ths_sup_0, Ths_re_0, ma_sup_hs, ma_sup_0, Ta_sup_0, Ta_re_0, Cpa, gv)[source]¶

cea.technologies.heatpumps module¶

heatpumps

cea.technologies.heatpumps.GHP_InvCost(GHP_Size, gV)[source]¶

Calculates the annualized investment costs for the geothermal heat pump

:type GHP_Size : float :param GHP_Size: Design electrical size of the heat pump in [Wel]

InvCa : float

annualized investment costs in EUROS/a

cea.technologies.heatpumps.GHP_Op_max(tsup, tground, nProbes, gV)[source]¶

For the operation of a Geothermal heat pump (GSHP) at maximum capacity supplying DHN.

:type tsup : float :param tsup: supply temperature to the DHN (hot) :type tground : float :param tground: ground temperature :type nProbes: float :param nProbes: bumber of probes :param gV: globalvar.py

Rtype qhotdot:	float
Returns qhotdot:
	heating energy provided from GHSP
Rtype COP:	float
Returns COP:	coefficient of performance of GSHP

cea.technologies.heatpumps.GHP_op_cost(mdot, tsup, tret, gV, COP)[source]¶

Operation cost of GSHP supplying DHN

:type mdot : float :param mdot: supply mass flow rate to the DHN :type tsup : float :param tsup: supply temperature to the DHN (hot) :type tret : float :param tret: return temeprature from the DHN (cold) :type COP: float :param COP: coefficient of performance of GSHP :param gV: globalvar.py

Rtype C_GHP_el:	float
Returns C_GHP_el:
	electricity cost of GSHP operation
Rtype wdot:	float
Returns wdot:	electricty required for GSHP operation
Rtype qcoldot:	float
Returns qcoldot:
	cold power requirement
Rtype q_therm:	float
Returns q_therm:
	thermal energy supplied to DHN

cea.technologies.heatpumps.HPLake_Op(mdot, tsup, tret, tlake, gV)[source]¶

For the operation of a Heat pump between a district heating network and a lake

:type mdot : float :param mdot: supply mass flow rate to the DHN :type tsup : float :param tsup: supply temperature to the DHN (hot) :type tret : float :param tret: return temeprature from the DHN (cold) :type tlake : float :param tlake: lake temperature :param gV: globalvar.py

:rtype wdot_el : float :returns wdot_el: total electric power requirement for compressor and auxiliary el. :rtype qcolddot : float :returns qcolddot: cold power requirement

..[L. Girardin et al., 2010] L. Girardin, F. Marechal, M. Dubuis, N. Calame-Darbellay, D. Favrat (2010). EnerGis: a geographical information based system for the evaluation of integrated energy conversion systems in urban areas, Energy.

..[C. Montagud et al., 2014] C. Montagud, J.M. Corberan, A. Montero (2014). In situ optimization methodology for the water circulation pump frequency of ground source heat pump systems. Energy and Buildings

cea.technologies.heatpumps.HPLake_op_cost(mdot, tsup, tret, tlake, gV)[source]¶

For the operation of lake heat pump supplying DHN

:type mdot : float :param mdot: supply mass flow rate to the DHN :type tsup : float :param tsup: supply temperature to the DHN (hot) :type tret : float :param tret: return temeprature from the DHN (cold) :type tlake : float :param tlake: lake temperature :param gV: globalvar.py

Rtype C_HPL_el:	float
Returns C_HPL_el:
	electricity cost of Lake HP operation
Rtype wdot:	float
Returns wdot:	electricty required for Lake HP operation
Rtype Q_cold_primary:
	float
Returns Q_cold_primary:
	cold power requirement
Rtype Q_therm:	float
Returns Q_therm:
	thermal energy supplied to DHN

cea.technologies.heatpumps.HPSew_op_cost(mdot, tsup, tret, tsupsew, gV)[source]¶

Operation cost of sewage water HP supplying DHN

:type mdot : float :param mdot: supply mass flow rate to the DHN :type tsup : float :param tsup: supply temperature to the DHN (hot) :type tret : float :param tret: return temeprature from the DHN (cold) :type tsupsew : float :param tsupsew: sewage supply temperature :param gV: globalvar.py

Rtype C_HPSew_el_pure:
	float
Returns C_HPSew_el_pure:
	electricity cost of sewage water HP operation
Rtype C_HPSew_per_kWh_th_pure:
	float
Returns C_HPSew_per_kWh_th_pure:
	electricity cost per kWh thermal energy produced from sewage water HP
Rtype qcoldot:	float
Returns qcoldot:
	cold power requirement
Rtype q_therm:	float
Returns q_therm:
	thermal energy supplied to DHN
Rtype wdot:	float
Returns wdot:	electricty required for sewage water HP operation

..[L. Girardin et al., 2010] L. Girardin, F. Marechal, M. Dubuis, N. Calame-Darbellay, D. Favrat (2010). EnerGis: a geographical information based system for the evaluation of integrated energy conversion systems in urban areas, Energy.

cea.technologies.heatpumps.calc_Cinv_GHP(GHP_Size, gV)[source]¶

Calculates the annualized investment costs for the geothermal heat pump

:type GHP_Size : float :param GHP_Size: Design electrical size of the heat pump in [Wel]

:type InvCa : float :returns InvCa: annualized investment costs in [EUROS/a]

..[D. Bochatay et al., 2005] D. Bochatay, I. Blanc, O. Jolliet, F. Marechal, T. Manasse-Ratmandresy (2005). Project PACOGEN Evaluation economique et environmentale de systemes energetiques a usage residentiel., EPFL.

cea.technologies.heatpumps.calc_Cinv_HP(HP_Size, gV)[source]¶

Calculates the annualized investment costs for the heat pump

:type HP_Size : float :param HP_Size: Design thermal size of the heat pump in [W]

:rtype InvCa : float :returns InvCa: annualized investment costs in [CHF/a]

..[C. Weber, 2008] C.Weber, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies., PhD Thesis, EPFL

cea.technologies.heatpumps.calc_Cop_GHP(mdot, tsup, tret, tground, gV)[source]¶

For the operation of a Geothermal heat pump (GSHP) supplying DHN.

:type mdot : float :param mdot: supply mass flow rate to the DHN :type tsup : float :param tsup: supply temperature to the DHN (hot) :type tret : float :param tret: return temeprature from the DHN (cold) :type tground : float :param tground: ground temperature :param gV: globalvar.py

:rtype wdot_el : float :returns wdot_el: total electric power requirement for compressor and auxiliary el. :rtype qcolddot : float :returns qcolddot: cold power requirement :rtype qhotdot_missing : float :returns qhotdot_missing: deficit heating energy from GSHP :rtype tsup2 : :returns tsup2: supply temperature after HP (to DHN)

..[O. Ozgener et al., 2005] O. Ozgener, A. Hepbasli (2005). Experimental performance analysis of a solar assisted ground-source heat pump greenhouse heating system, Energy Build. ..[C. Montagud et al., 2014] C. Montagud, J.M. Corberan, A. Montero (2014). In situ optimization methodology for the water circulation pump frequency of ground source heat pump systems. Energy and Buildings

cea.technologies.photovoltaic module¶

photovoltaic

cea.technologies.photovoltaic.Calc_PV_power(S, Tcell, eff_nom, areagroup, Bref, misc_losses)[source]¶

cea.technologies.photovoltaic.Calc_Sm_PV(te, I_sol, I_direct, I_diffuse, tilt, Sz, teta, tetad, tetaeg, n, Pg, K, NOCT, a0, a1, a2, a3, a4, L)[source]¶

cea.technologies.photovoltaic.Calc_diffuseground_comp(tilt_radians)[source]¶

cea.technologies.photovoltaic.calc_Cinv_pv(P_peak)[source]¶

P_peak in kW result in CHF Lifetime 20 y

cea.technologies.photovoltaic.calc_Crem_pv(E_nom)[source]¶

Calculates KEV (Kostendeckende Einspeise - Verguetung) for solar PV and PVT. Therefore, input the nominal capacity of EACH installation and get the according KEV as return in Rp/kWh

Parameters:	E_nom – Nominal Capacity of solar panels (PV or PVT) in Wh
Returns:	KEV_obtained_in_RpPerkWh : float KEV remuneration in Rp / kWh

cea.technologies.photovoltaic.calc_properties_PV(type_PVpanel)[source]¶

cea.technologies.photovoltaic.calc_pv_generation(type_panel, hourly_radiation, Number_groups, number_points, prop_observers, weather_data, g, Sz, Az, ha, latitude, misc_losses)[source]¶

cea.technologies.photovoltaic.calc_pv_main(locator, sensors_data, radiation, latitude, longitude, year, gv, weather_path)[source]¶

cea.technologies.photovoltaic.test_photovoltaic()[source]¶

cea.technologies.photovoltaic_thermal module¶

Photovoltaic thermal panels

cea.technologies.photovoltaic_thermal.Calc_PVT_module(tilt_angle, IAM_b_vector, I_direct_vector, I_diffuse_vector, Te_vector, n0, c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max, IAM_d, Area_a, dP1, dP2, dP3, dP4, Tin, Leq, Le, Nseg, eff_nom, Bref, Sm_pv, Tcell_pv, misc_losses, areagroup)[source]¶

cea.technologies.photovoltaic_thermal.calc_Cinv_PVT(P_peak)[source]¶

P_peak in kW result in CHF

cea.technologies.photovoltaic_thermal.calc_PVT(locator, sensors_data, radiation, latitude, longitude, year, gv, weather_path)[source]¶

cea.technologies.photovoltaic_thermal.calc_PVT_generation(type_panel, hourly_radiation, Number_groups, number_points, prop_observers, weather_data, g, Sz, Az, ha, latitude, misc_losses, type_SCpanel, Tin, height)[source]¶

cea.technologies.photovoltaic_thermal.test_PVT()[source]¶

cea.technologies.pumps module¶

pumps

cea.technologies.pumps.Pump_Cost(deltaP, mdot, eta_pumping, gV)[source]¶

Calculates the cost of a pumping device. if the nominal load (electric) > 375kW, a new pump is installed if the nominal load (electric) < 500W, a pump with Pel_design = 500W is assumed

Investment costs are calculated upon the life time of a GHP (20y) and a GHP- related interest rate of 6%

:type deltaP : float :param deltaP: nominal pressure drop that has to be overcome with the pump

:type mdot : float :param mdot: nominal mass flow

:type eta_pumping : float :param pump efficiency: (set 0.8 as standard value, eta = E_pumping / E_elec)

:rtype InvCa : float :returns InvCa: annualized investment costs in CHF/year

cea.technologies.pumps.Pump_operation(P_design)[source]¶

Modeled after:

05_merkblatt_wirtschaftlichkeit_14.pdf 23_merkblatt_pumpen_web.pdf ER_2010_11_Heizungspumpen.pdf MerkblattPreiseFU2010_2011.pdf MerkblattPreiseMotoren2010_2011.pdf

P_design : float

Load of time step

eta_el : float

electric efficiency of Pumping operation in abs. numbers (e.g. 0.93)

cea.technologies.pumps.calc_Cinv_pump(deltaP, mdot, eta_pumping, gV)[source]¶

Calculates the cost of a pumping device. if the nominal load (electric) > 375kW, a new pump is installed if the nominal load (electric) < 500W, a pump with Pel_design = 500W is assumed

Investement costs are calculated upon the life time of a GHP (20y) and a GHP- related interest rate of 6%

:type deltaP : float :param deltaP: nominal pressure drop that has to be overcome with the pump

:type mdot : float :param mdot: nominal mass flow

:type eta_pumping : float :param pump efficiency: (set 0.8 as standard value, eta = E_pumping / E_elec)

:rtype InvC_return : float :returns InvC_return: total investment Cost in CHF

:rtype InvCa : float :returns InvCa: annualized investment costs in CHF/year

cea.technologies.pumps.calc_Ctot_pump(dicoSupply, buildList, network_results_folder, ntwFeat, gV)[source]¶

Computes the total pump investment cost

:type dicoSupply : class context :type buildList : list :param buildList: list of buildings in the district :type network_results_folder : string :param network_results_folder: path to network results folder :type ntwFeat : class ntwFeatures

:rtype pumpCosts : float :returns pumpCosts: pumping cost

cea.technologies.radiators module¶

heating radiators

cea.technologies.radiators.calc_radiator(Qh, tair, Qh0, tair0, tsh0, trh0)[source]¶

cea.technologies.radiators.fh(x, mCw0, k2, Qh0, tair, LMRT, nh)[source]¶

cea.technologies.radiators.lmrt(tair0, trh0, tsh0)[source]¶

cea.technologies.sewage_heat_exchanger module¶

Sewage source heat exchanger

cea.technologies.sewage_heat_exchanger.calc_Sewagetemperature(Qwwf, Qww, tsww, trww, totwater, mcpww, cp, density, SW_ratio)[source]¶

Calculate sewage temperature and flow rate released from DHW usages and Fresh Water (FW) in buildings.

Parameters:	Qwwf (float) – final DHW heat requirement Qww (float) – DHW heat requirement tsww (float) – DHW supply temperature trww (float) – DHW return temperature totwater (float) – fresh water flow rate mcpww (float) – DHW heat capacity cp (float) – water specific heat capacity density – water density SW_ratio (float) – ratio of waste water to fresh water production.
Returns mcp_combi:
	sewage water heat capacity [kWh/K]
Rtype mcp_combi:
	float
Returns t_to_sewage:
	sewage water temperature
Rtype t_to_sewage:
	float

cea.technologies.sewage_heat_exchanger.calc_sewage_heat_exchanger(locator, Length_HEX_available, gv)[source]¶

Calaculate the heat extracted from the sewage HEX.

Parameters:	locator – an InputLocator instance set to the scenario to work on Length_HEX_available (float) – HEX length available gv – globalvar.py

Save the results to SWP.csv

cea.technologies.sewage_heat_exchanger.calc_sewageheat(mcp, tin, w_HEX, Vf, cp, h0, min_m, L_HEX, tmin, ATmin)[source]¶

Calculates the operation of sewage heat exchanger.

Parameters:	mcp (float) – heat capacity of total sewage in a zone tin (float) – sewage inlet temperature of a zone w_HEX (float) – width of the sewage HEX Vf (float) – sewage flow rate [m/s] cp (float) – water specific heat capacity h0 (float) – sewage heat transfer coefficient min_m (float) – sewage minimum flow rate in [lps] L_HEX (float) – HEX length available tmin (float) – minimum temperature of extraction ATmin (float) – minimum area of heat exchange
Returns Q_source:
	heat supplied by sewage
Return type:	float
Returns t_source:
	sewage heat supply temperature
Rtype t_source:	float
Returns tb2:	sewage return temperature
Rtype tbs:	float
Returns ta1:	temperature inlet of the cold stream (from the HP)
Rtype ta1:	float
Returns ta2:	temperature outlet of the cold stream (to the HP)
Rtype ta2:	float

..[J.A. Fonseca et al., 2016] J.A. Fonseca, Thuy-An Nguyen, Arno Schlueter, Francois Marechal (2016). City Enegy Analyst (CEA): Integrated framework for analysis and optimization of building energy systems in neighborhoods and city districts. Energy and Buildings.

cea.technologies.sewage_heat_exchanger.test_ss_heatpump()[source]¶

cea.technologies.solar_collector module¶

solar collectors

cea.technologies.solar_collector.Calc_SC_module2(radiation, tilt_angle, IAM_b_vector, I_direct_vector, I_diffuse_vector, Te_vector, n0, c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max, IAM_d, Area_a, dP1, dP2, dP3, dP4, Tin, Leq, Le, Nseg)[source]¶

cea.technologies.solar_collector.Calc_incidenteangleB(g, lat, ha, tilt, teta_z)[source]¶

cea.technologies.solar_collector.Calc_qloss_net(Mfl, Le, Area_a, Tm, Te, maxmsc)[source]¶

cea.technologies.solar_collector.SC_generation(type_SCpanel, group_radiation, prop_observers, number_points, weather_data, g, Sz, Az, ha, latitude, Tin, height)[source]¶

cea.technologies.solar_collector.Selectminimumenergy2(m, q, dp)[source]¶

cea.technologies.solar_collector.SelectminimumenergySc(q1, q2, q3, q4, E1, E2, E3, E4, m1, m2, m3, m4, dP1, dP2, dP3, dP4, Area_a)[source]¶

cea.technologies.solar_collector.calc_Cinv_SC(Area)[source]¶

Lifetime 35 years

cea.technologies.solar_collector.calc_Eaux_SC(qV_des, Dp_collector, Leq, Aa)[source]¶

auxiliary electricity solar collectort

Parameters:	qV_des – Dp_collector – Leq – Aa –
Returns:

cea.technologies.solar_collector.calc_SC(locator, sensors_data, radiation, latitude, longitude, year, gv, weather_path)[source]¶

cea.technologies.solar_collector.calc_anglemodifierSC(Az_vector, g_vector, ha_vector, teta_z, tilt_angle, type_SCpanel, latitude, Sz_vector)[source]¶

cea.technologies.solar_collector.calc_groups(Clean_hourly, observers_fin)[source]¶

cea.technologies.solar_collector.calc_properties_SC(type_SCpanel)[source]¶

properties of module :param type_SCpanel: :return:

cea.technologies.solar_collector.calc_qgain(Tfl, Tabs, qrad, DT, TinSub, Tout, Aseg, c1, c2, Mfl, delts, Cpwg, C_eff, Te)[source]¶

cea.technologies.solar_collector.calc_qrad(n0, IAM_b, I_direct, IAM_d, I_diffuse, tilt)[source]¶

cea.technologies.solar_collector.optimal_angle_and_tilt(observers_all, latitude, worst_sh, worst_Az, transmittivity, grid_side, module_lenght, angle_north, Min_Isol, Max_Isol)[source]¶

cea.technologies.solar_collector.test_solar_collector()[source]¶

cea.technologies.storagetank module¶

Sensible Heat Storage - Fully Mixed tank

cea.technologies.storagetank.calc_Qww_ls_st(ta, te, Tww_st, V, Qww, Qww_ls_r, Qww_ls_nr, gv)[source]¶

This algorithm calculates the heat flows within a fully mixed water storage tank. Heat flows include sensible heat loss to the environment (ql), heat charged into the tank (qc), and heat discharged from the tank (qd).

Parameters:	Tww_st (float) – tank temperature in [C] Tww_setpoint (float) – DHW temperature set point in [C] ta (float) – room temperature in [C] te (float) – ambient temperature in [C] V (float) – DHW tank size in [m3] Qww (float) – DHW demand in [W] Qww_ls_r – recoverable loss in distribution in [W] Qww_ls_nr (float) – non-recoverable loss in distribution in [W] gv – globalvar.py
Return ql:	storage sensible heat loss in [W].
Return qd:	heat discharged from the tank in [W], including dhw heating demand and distribution heat loss.
Return qc:	heat charged into the tank in [W].
Rtype ql:	float
Rtype qd:	float
Rtype qc:	float

cea.technologies.storagetank.ode(y, t, ql, qd, qc, Pwater, Cpw, Vtank)[source]¶

This algorithm describe the energy balance of the dhw tank with a differential equation.

Parameters:	y (float) – storage sensible temperature in K. t (float) – time steps. ql (float) – storage tank sensible heat loss in W. qd (float) – heat discharged from the tank in W. qc (float) – heat charged into the tank in W.
Return dydt:	change in temperature at each time step.

cea.technologies.storagetank.solve_ode_storage(Tww_st_0, ql, qd, qc, Vtank, gv)[source]¶

This algorithm solves the differential equation, ode.

Parameters:	Tww_st_0 (float) – initial tank temperature in [C] ql (float) – storage tank sensible heat loss in W. qd (float) – heat discharged from the tank in W. qc (float) – heat charged into the tank in W. Vtank (float) – DHW tank size in [m3] gv – globalvar.py
Returns y[1]:	solution of the ode
Rtype y[1]:	float

cea.technologies.substation module¶

Substation Model

cea.technologies.substation.calc_DC_supply(t_0, t_1)[source]¶

This function calculates the temperature of the district cooling network according to the minimum observed (different to zero) in all buildings connected to the grid.

Parameters:	t_0 – last minimum temperature t_1 – current minimum temperature to evaluate
Returns:	tmin, new minimum temperature

cea.technologies.substation.calc_DH_supply(t_0, t_1)[source]¶

This function calculates the temperature of the district heating network according to the maximum observed in all buildings connected to the grid. :param t_0: last maximum temperature :param t_1: current maximum temperature :return: tmax, new maximum temperature

cea.technologies.substation.calc_HEX_cooling(Q, UA, thi, tho, tci, ch)[source]¶

This function calculates the mass flow rate, temperature of return (secondary side) and heat exchanger area for a plate heat exchanger. Method of Number of Transfer Units (NTU)

Parameters:	Q – cooling load UA – coefficient representing the area of heat exchanger times the coefficient of transmittance of the heat exchanger thi – in temperature of primary side tho – out temperature of primary side tci – in temperature of secondary side ch – capacity mass flow rate primary side
Returns:	tco, out temperature of secondary side (district cooling network) cc, capacity mass flow rate secondary side

cea.technologies.substation.calc_HEX_heating(Q, UA, thi, tco, tci, cc)[source]¶

This function calculates the mass flow rate, temperature of return (secondary side) and heat exchanger area for a shell-tube pleat exchanger in the heating case.

Method of Number of Transfer Units (NTU)

Parameters:	Q – load UA – coefficient representing the area of heat exchanger times the coefficient of transmittance of the heat exchanger thi – in temperature of secondary side tco – out temperature of primary side tci – in temperature of primary side cc – capacity mass flow rate primary side
Returns:	tho, out temperature of secondary side (district cooling network) ch, capacity mass flow rate secondary side

cea.technologies.substation.calc_HEX_mix(Q1, Q2, t1, m1, t2, m2)[source]¶

This function computes the average temperature between two vectors of heating demand. In this case, domestic hotwater and space heating.

Parameters:	Q1 – load heating Q2 – load domestic hot water t1 – out temperature of heat exchanger for space heating m1 – mas flow rate secondary side of heat exchanger for space heating t2 – out temperature of heat exchanger for domestic hot water m2 – mas flow rate secondary side of heat exchanger for domestic hot water
Returns:	tavg: average out temperature.

cea.technologies.substation.calc_area_HEX(Qnom, dTm_0, U)[source]¶

Thi function calculates the area of a het exchanger at nominal conditions.

Parameters:	Qnom – nominal load dTm_0 – nominal logarithmic temperature difference U – coeffiicent of transmissivity
Returns:	area, area of heat exchange UA, coefficient representing the area of heat exchanger times the coefficient of transmittance of the heat exchanger

cea.technologies.substation.calc_dTm_HEX(thi, tho, tci, tco, flag)[source]¶

This function estimates the logarithmic temperature difference between two streams

Parameters:	thi – in temperature hot stream tho – out temperature hot stream tci – in temperature cold stream tco – out temperature cold stream flag – heat: when using for the heating case, ‘cool’ otherwise
Returns:	dtm = logaritimic temperature difference

cea.technologies.substation.calc_plate_HEX(NTU, cr)[source]¶

This function calculates the efficiency of exchange for a plate heat exchanger according to the NTU method of AShRAE 90.1

Parameters:	NTU – number of transfer units cr – ratio between min and max capacity mass flow rates
Returns:	eff: efficiency of heat exchange

cea.technologies.substation.calc_shell_HEX(NTU, cr)[source]¶

This function calculates the efficiency of exchange for a tube-shell heat exchanger according to the NTU method of AShRAE 90.1

Parameters:	NTU – number of transfer units cr – ratio between min and max capacity mass flow rates
Returns:	eff: efficiency of heat exchange

cea.technologies.substation.calc_substation_cooling(Q, thi, tho, tci, ch, ch_0, Qnom, thi_0, tci_0, tho_0, gv)[source]¶

this function calculates the state of the heat exchanger at the substation of every customer with cooling needs

Parameters:

Q – cooling laad thi – in temperature of primary side tho – out temperature of primary side tci – in temperature of secondary side ch – capacity mass flow rate primary side ch_0 – nominal capacity mass flow rate primary side Qnom – nominal cooling load thi_0 – nominal in temperature of primary side tci_0 – nominal in temperature of secondary side tho_0 – nominal out temperature of primary side gv – path to global variables class

Returns:

tco = out temperature of secondary side (district cooling network) cc = capacity mass flow rate secondary side Area_HEX_cooling = are of heat excahnger.

cea.technologies.substation.calc_substation_heating(Q, thi, tco, tci, cc, cc_0, Qnom, thi_0, tci_0, tco_0, gv)[source]¶

This function calculates the mass flow rate, temperature of return (secondary side) and heat exchanger area of every substation.

Parameters:

Q – heating load thi – in temperature of secondary side tco – out temperature of primary side tci – in temperature of primary side cc – capacity mass flow rate primary side cc_0 – nominal capacity mass flow rate primary side Qnom – nominal cooling load thi_0 – nominal in temperature of secondary side tci_0 – nominal in temperature of primary side tco_0 – nominal out temperature of primary side gv – path to global variables class

Returns:

tho = out temperature of secondary side (district cooling network) ch = capacity mass flow rate secondary side Area_HEX_heating = are of heat excahnger.

cea.technologies.substation.substation_main(locator, total_demand, building_names, gv, Flag)[source]¶

this function calculates the temperatures and mass flow rates of the district heating network at every costumer. Based on this, the script calculates the hourly temperature of the network at the plant. This temperature needs to be equal to that of the customer with the highest temperature requirement plus thermal losses in the network.

Parameters:	locator – path to locator function total_demand – dataframe with total demand and names of all building in the area building_names – dataframe with names of all buildings in the area gv – path to global variables class Flag – boolean, True if the function is called by the master optimizaiton. False if the fucntion is called during preprocessing

cea.technologies.substation.substation_model(locator, gv, building, t_DH, t_DH_supply, t_DC_supply, t_HS, t_WW)[source]¶

Parameters:

locator – path to locator function gv – path to global variables class building – dataframe with consumption data per building t_DH – vector with hourly temperature of the district heating network without losses t_DH_supply – vector with hourly temperature of the district heating netowork with losses t_DC_supply – vector with hourly temperature of the district coolig network with losses t_HS – maximum hourly temperature for all buildings connected due to space heating t_WW – maximum hourly temperature for all buildings connected due to domestic hot water

Returns:

Dataframe stored for every building with the mass flow rates and temperatures district heating and cooling side in: where fName_result: ID of the building accounting for the individual at which it belongs to.

cea.technologies.tabs module¶

Termoactivated building surfaces (TABS)

cea.technologies.tabs.calc_floorheating(Qh, tm, Qh0, tsh0, trh0, Af)[source]¶

Calculates the operating conditions of the TABS system based on existing radiator model, replacing the radiator equation with the simple calculation for TABS from SIA 2044, which in turn is based on Koschenz & Lehmann “Thermoaktive Bauteilsysteme (TABS)”.

Parameters:	Qh – heating demand tm – Temperature of the thermal mass Qh0 – nominal heating power of the heating system tsh0 – nominal supply temperature to the TABS system trh0 – nominal return temperature from the TABS system Af – heated area
Returns:	tsh, supply temperature to the TABS system trh, return temperature from the TABS system mCw, flow rate in the TABS system

cea.technologies.thermal_network module¶

hydraulic network

cea.technologies.thermal_network.calc_Cinv_network_linear(LengthNetwork, gV)[source]¶

calculate annualised network investment cost with a linearized function.

Parameters:	LengthNetwork – total length of the network in [m]
Pram gV:	globalvar.py
Returns InvCa:	annualised investment cost of the thermal network
Rtype InvCa:	float