UrbanHeatPro.Classes.Building

Building.py A. Molar-Cruz @ TUM ENS

Module Contents

Classes

Building

Initialize the Building object with the given parameters.

class UrbanHeatPro.Classes.Building.Building(b, building_stock_stats, dt_vectors, resolution, number_of_typ_days, weights, Tamb, I, _space_heating, _hot_water, _energy_only, Tb0_str, dTset, dT_per_hour, eta, thermal_inertia, _active_population, _workday_weekend, sh_prob, _solar_gains, _internal_gains, _night_set_back, schedule_nsb, T_nsb, power_reduction, Tw, dhw_prob, hw_tank_limit, hw_flow, day_vector, seasonal_vector, min_vector, result_dir, plot, save, debug)

Initialize the Building object with the given parameters.

Parameters:

• b – Building dataframe

• building_stock_stats – Building stock statistics

• dt_vectors – Vector of time steps as datetime objects

• resolution – Temporal resolution in min

• number_of_typ_days – Number of typical days

• weights – Weights of typical days

• Tamb – Ambient temperature vector in degC

• I – Solar radiation vector in W/m2 [I_Gh, I_Dh, I_ex, hs]

• _space_heating – Calculate space heating demand?

• _hot_water – Calculate hot water demand?

• _energy_only – Calculate only aggregated demand?

• Tb0_str – Initial building temperature as string: ‘ambient’ or ‘Tset’

• dTset – Delta temperature (for Tset_min, Tset_max)

• dT_per_hour – Maximum dT allowed in building per hour [degC]

• eta – Heating process efficiency

• thermal_inertia – Thermal inertia of the heating system

• _active_population – Consider active population for occupancy vector

• _workday_weekend – Consider dif between workdays and weekends

• sh_prob – Probability vector of using space heating

• _solar_gains – Consider solar gains?

• _internal_gains – Consider internal gains?

• _night_set_back – Share of buildings with nsb

• schedule_nsb – [start, end] of nsb in h

• T_nsb – Night set-back temperature in degC

• power_reduction – Percentage of power reduced (as decimal)

• Tw – Hot water temperature in [degC]

• dhw_prob – Probabilities for dhw-loads

• hw_tank_limit – Hot water tank limit as perc (decimal)

• hw_flow – Flow to refill hot water tank in L/min

• day_vector – Vector of days in simulation time frame

• seasonal_vector – Sinusoidal function for seasonal variations of DHW consumption

• min_vector – Vector of simulation time steps in minutes

• result_dir – Directory where results are stored

• plot – Whether to plot the results or not

• save – Whether to save the results or not

• debug – Whether to print debug information or not

calculate_space_heating_demand()

Calculates building space heating demand as timeseries [W] and aggregated value [Wh]

calculate_hot_water_demand(save_debug=False)

Calculates building hot water demand as timeseries [W] and [m3] and aggregated value [Wh].

Only for residential buildings.

Parameters:

save_debug (boolean) – Is debug file saved?

Returns:

self.hot_water_m3 self.hot_water_power self.dhw_energy

calculate_total_heat_demand()

Agrregate the space heating and/or hot water demand time series. The total time series is delayed depending on the distance to the heat plant.

calculate_delayed_timeseries(flow_vel=1.0)

Delays vector of heat demand depending on the distance of the building centroid to the (geothermal) heat plant. A flow velocity of 1 m/s in the district heating network is considered.

parametrize_building()

Calculates missing building properties necessary for the heat demand calculation.

update_building_refurbishment_level(ref_matrix_res, ref_matrix_nres)

Updates building refurbishment level based on desired refurbishment level from scenario and max percentage of refurbished buildings in region (MAX_REF_RES and MAX_REF_NRES).

Refurbishment levels according to TABULA typology:

• 1 National minimum requirement

• 2 Improved standard

• 3 Ambitious standard

categorize_building_residential()

Probabilistic categorization building according to TABULA typologies. Construction year class and building type are calculated by comparing the residential building gross floor area (footprint_area) with the FOOTPRINT of typical buildings (from TABULA). Values are adapted to fit building stock statistics.

categorize_building_non_residential()

Probabilistic categorization of non-residential buildings according to the building statistics and the following construction year classes:

int	construction year class
0	< 1918
1	1919 - 1976
2	1977 - 1983
3	1984 - 1994
4	> 1995

>> Source for building stock missing

compute_current_refurbishment_level_residential()

Computes the refurbishment level for the different building elements [roof, wall, floor, window] according to the current refurbishment statistics.

Refurbishment levels according to TABULA typology:

• 1 National minimum requirement

• 2 Improved standard

• 3 Ambitious standard

compute_current_refurbishment_level_non_residential()

Computes the refurbishment level for the different building components [roof, wall, floor, window] according to the refurbishment statistics.

Refurbishment levels according to TABULA typology:

• 1 National minimum requirement

• 2 Improved standard

• 3 Ambitious standard

>> Statistics on refurbishment in non-residential buildings missing

compute_scenario_refurbishment_level_residential(ref_matrix_res)

Computes the refurbishment level for the different building elements [roof, wall, floor, window] according to the scenario refurbishment level per typology and the maximum share of refurbished buildings (MAX_REF_RES).

Refurbishment levels according to TABULA typology:

• 1 National minimum requirement

• 2 Improved standard

• 3 Ambitious standard

compute_scenario_refurbishment_level_non_residential(ref_matrix_nres)

Computes the refurbishment level for the different building elements [roof, wall, floor, window] according to the scenario refurbishment level per typology and the maximum share of refurbished buildings (MAX_REF_NRES).

Refurbishment levels according to TABULA typology:

• 1 National minimum requirement

• 2 Improved standard

• 3 Ambitious standard

calculate_areas_residential()

Calculate storey area and heated/conditioned area based on definitions from VDI 3807.

calculate_areas_non_residential()

Calculate storey area and heated/conditioned area based on definitions from VDI 3807.

calculate_number_of_floors_residential()

Calculates number of floors based on the TABULA typology. The number of floors calculated from TABULA are referenced to the conditioned or heated area but the number of floors are calculated using the storey area.

calculate_number_of_floors_non_residential(left=1, mode=2, right=3)

Calculates number of floors as random sample number from the triangular distribution with lower limit left, peak at mode and upper limit right.

>> Non-residential buildings are assumed to have two floors as mode and

a maximum of three floors Source missing

calculate_number_of_dwellings()

Calculates number of dwellings based on the building living area and mean dwelling size. It is assumed that SFH and TH have only 1 or 2 dwellings which is determined using the single-dwelling buildings statistics. For MFH and AB, the number of dweelings is calculated based on the average dwelling size.

determine_dwelling_size_category()

Determine dwelling size category based on statistics https://ergebnisse.zensus2011.de/#StaticContent:091840148148,GWZ_4_3_2,m,table

calculate_number_of_occupants_residential()

Calculates number of occupants based on household size and number of dwellings statistics.

calculate_number_of_occupants_non_residential(capacity=0.1)

Calculates random number of occupants in the building based on the recommended area per person for different building types from https://www.engineeringtoolbox.com/number-persons-buildings-d_118.html.

get_building_thermal_properties_per_unit_area_residential()

Gets building thermal properties from TABULA Web Tool data based on the building typology [year_class, btype]

Sets the attributes:

• list: u: [u_roof, u_wall, u_floor, u_window] in W/(K m2)

• list: v: [v_usage, v_infiltration] in 1/h

• list: c: [c_roof, c_wall, c_floor] in J/(K m2)

get_building_thermal_properties_per_unit_area_non_residential()

Gets building thermal properties based on: >> source missing

Sets the following attributes:

• u list: [u_roof, u_wall, u_floor, u_window] in W/(K m2)

• v list: [v_usage, v_infiltration] in 1/h

• c list: [c_roof, c_wall, c_floor] in J/(K m2)

calculate_building_envelope_areas_residential()

Calculates building envelope areas (wall, roof and window). Residential: areas are calculated according to building typologies in TABULA. Only the heated area is considered.

calculate_building_envelope_areas_non_residential()

Calculates building envelope areas (wall, roof and window).

Non-residential: number of floors and window-to-wall ratio are derived from statistics and used to calculate the building areas. Only the heated area is considered.

calculate_building_window_areas()

Calculate window areas in each direction to calculate solar gains.

calculate_building_thermal_properties()

Calculates equivalent U-value, thermal capacitance (C) and time constant (Tau) for the building. These properties are used in the first order thermal model.

adjust_building_thermal_properties()

Empirical adjustment of U-values to match TABULA results

calculate_building_Tset()

Derives a target temperature by choosing a random temperature from Tset_mean +/- dT. Values differ for different building types. From http://tc76.org/spc100/docs/IBP%2018599/18599-10.pdf

calculate_building_active_hours()

Assigns random start and end hours for building active hours. Values differ for different building types.

Sets the following attributes:

self.active_hours list: [(start0, end0), (start1, end1)] in h

calculate_daily_hot_water_demand()

Returns the daily hot water demand by getting a random value from the cdf function based on the statistics from VDI 3807-3 (specific dhw demand in m3/m2 of living area)

parametrize_hot_water_tank(X=1.5)

Calculates size and initial state of hot water tank. Size is X times the calculated daily demand.

set_hot_water_tank_initial_state()

calculate_building_activity_occupancy_vector()

Calculate vector of activity in building, i.e. percentage of occupied dwellings (for space heating) Active_hours (scheduled), building occupancy and weekends are considered

calculate_occupants_schedule()

A schedule is assigned to every occupant based on studying/working schedule.

Sets the following attributes:

occupant_vector: list = [dwelling, [occupant, [schedule]]] for occupant and dwelling in building

calculate_annual_demand(data)

Calculate the annual energy demand by weighting the heating demand of typical days

plot_timeseries(space_heating=True, Tb=False, hot_water=True, total=True, xticks=('month', 3))

Plots heat demand timeseries

save_csv()

Saves key building parameters and heat demand (space heating, hot water and total) as timeseries.

save_load_duration_curve()

Save sorted demand

save_dhw_debug_csv()

Saves debug values for dhw demand.