reskit.cooling_heating

reskit.cooling_heating#

Submodules#

Classes#

CoolingHeatingWorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers,

CoolingHeatingWorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers,

Functions#

calculate_relative_humidity(dewpoint_temperature, ...)

Function to calculate the relative humidity from dewpoint temperature and air temperature using the Sonntag formula.

calculate_wet_bulb_temperature(air_temperature, ...)

Function to calculate the wet bulb temperature from air temperature and relative humidity.

evaporative_cooling_wortmann2025(placements, ...[, ...])

Simulate an evaporative-cooling system based on ERA5 weather data.

air_cooling_wenzel2025(placements, era5_path, ...[, ...])

Simulate an air-cooling system based on ERA5 weather data.

air_source_heat_pump(placements, era5_path[, ...])

Simulate an air-source heat pump based on ERA5 weather data.

Package Contents#

class reskit.cooling_heating.CoolingHeatingWorkflowManager(placements)#

Bases: reskit.workflow_manager.WorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers, such as the WindWorkflowManager or the SolarWorkflowManager. In addition to providing the general structure for simulation workflow management, the WorkflowManager also defines functionalities which should be common across all WorkflowManagers.

This includes:

  • Basic initialization

  • Time domain management

  • Reading weather data

  • Adjusting variables by a long-run-average value

  • Applying simple loss factors

  • Saving the state of WorkflowManagers to XArray datasets, either in memory or on disc

Initialization:#

WorkflowManager( placements )

__init_(self, placements)

Initialization of an instance of the generic CoolingHeatingWorkflowManager class.

param placements:

The locations that the simulation should be run for. Columns must include “lon”, “lat” (CRS: 4326) and “capacity” -The capacity is the nominal capacity of the Heating/Cooling System

type placements:

pandas Dataframe

returns:

  • CoolingHeatingWorkflowManager

  • Sources

  • [1] https (//www.engineeringtoolbox.com/air-density-specific-weight-d_600.html)

  • [2] https (//www.engineeringtoolbox.com/air-specific-heat-capacity-d_705.html)

airData#
evaporationCoolingData#
calculate_approach_evaporative_cooling(temperatureCoolant, heatTransferDelta, efficiencyCoolingTower)#

Calculate the approach temperature for an evaporative-cooling system.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int) – Temperature difference required for heat transfer from air to coolant [K]

  • efficiencyCoolingTower (float | int) – Efficiency of the cooling tower system [0, 1]

Returns:

Approach temperature for the specified weather conditions.

Return type:

float or np.ndarray

References

[1] 10.1016/j.ijhydene.2024.11.381

calculate_water_losses_evaporative_cooling(temperatureCoolant, heatTransferDelta, efficiencyCoolingTower, factorDriftLosses=0.001, typical_cycles_blowdown=5)#

Calculate the water losses for an evaporative-cooling system.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int) – Temperature difference required for heat transfer from air to coolant [K]

  • efficiencyCoolingTower (float | int) – Efficiency of the cooling tower system [0, 1]

  • factorDriftLosses (float | int) – Drift losses by small water droplets carried away by the exhaust air. Defaults to 0.001. [1]

  • typical_cycles_blowdown (int) – after how many cycles the blowdown occurs to prevent accumulation of impurities. Defaults to 5. [2]

Returns:

Specific water losses of evaporative cooling towers (per kWh of cooling load) for the specified weather conditions.

Return type:

float or np.ndarray

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] 10.1016/j.enconman.2020.113610

calculate_fan_power_air_cooling(temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, pressureDropAir=261, designTemperature=None)#

Calculate the fan power demand for an air-cooling system.

This method computes the electrical power required by the fan to transfer heat from the air to a coolant, based on the air properties and the cooling load temperature. It can evaluate either for a given design air temperature or for the time series of ambient air temperatures stored in self.sim_data.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • designTemperature (float | int, optional) – If specified, the calculation is only evaluated at this air temperature [°C]. Default is None.

Returns:

Fan power demand in kWh per kWh of cooling. Returns a single value if designTemperature is provided, or a time series array otherwise.

Return type:

float or np.ndarray

Notes

  • Uses linear interpolation of air specific heat (cp) and density (rho) from self.airData.

  • Assigns np.inf to any time step where the temperature difference is insufficient for cooling.

  • Stores the time series result in self.sim_data[“conversion_factor_fan_electricity”] if designTemperature is None.

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] http://hdl.handle.net/1853/55674

calculate_pump_power_air_cooling(temperatureCoolant, heatTransferDelta=5, efficiencyPump=0.7, pressureDropWater=200000, designTemperature=None)#

Calculate the pump power demand for an air-cooling system.

This method computes the electrical power required by the pump to circulate coolant for an air-cooling system, based on the cooling load temperature, pressure drop, and pump efficiency. It can evaluate either for a given design air temperature or for the time series of ambient air temperatures stored in self.sim_data.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit between the heat load and the cooling frame [Pa]. Default is 200000.

  • designTemperature (float | int, optional) – If specified, the calculation is only evaluated at this air temperature [°C]. Default is None.

Returns:

  • float or np.ndarray

  • Pump power demand in kWh per kWh of cooling. Returns a single value if

  • designTemperature is provided, or a time series array otherwise.

Notes

  • Assumes constant water density of 1000 kg/m³ and specific heat capacity of 4.186 kJ/(kg·K) = 0.00116 kWh/(kg·K).

  • Assigns np.inf to any time step where the temperature difference is insufficient for cooling.

  • Stores the time series result in self.sim_data[“conversion_factor_pump_electricity”] if designTemperature is None.

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] 10.1016/j.enconman.2020.113610

calculate_relative_cost_factor_air_cooling(designTemperature, temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, efficiencyPump=0.7, pressureDropAir=261, pressureDropWater=200000)#

Calculate the relative (air temperature dependent) cost factor of an air-cooling system.

The air-cooling system consists of fans, water pumps, and an A-frame heat exchanger that transfers heat from water to air. The A-frame is assumed to always transfer heat with the specified heatTransferDelta. Fan and pump costs depend on the installed nominal power, which varies with ambient air temperature. The relative cost factor is defined as the ratio of cost at the design temperature to the cost at actual ambient temperatures. To cool the same amount of heat at an air temperature higher than the design air temperature, the CAPEX would rise, since pump and fan capacity would increase.

Parameters:

  • designTemperature (float | int) – Design ambient temperature of the cooling system [°C].

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) [°C].

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit from the heat load to the A-frame [Pa]. Default is 200000.

Returns:

  • None

  • The method stores the calculated relative cost factor in self.sim_data[“relative_cost_factor”]

  • and updates the self.units dictionary with air-cooling system units.

Notes

  • Fan and pump CAPEX are calculated based on nominal power at the design temperature and

scaled according to ambient conditions [1]. - A-frame cost is assumed constant and independent of ambient temperature [2]. - The relative cost factor reflects the relative increase in cost at varying ambient conditions compared to the design point.

References

[1] 10.1016/j.energy.2015.05.081 [2] 10.1016/j.enconman.2020.113610

calculate_capacity_factor_air_cooling(designTemperature, temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, efficiencyPump=0.7, pressureDropAir=261, pressureDropWater=200000)#

Calculate the capacity factor of an air-cooling system.

The air-cooling system can only provide the cooling load if the ambient air temperature is lower than the design temperature. If the temperature is above the design temperature, pumps and fans are designed too small to provide the necessary flows and the system can only provide less cooling. The reduction in cooling can be calculated based on the ratio of the design power and the theoretically required power for both, fans and pumps individually. The minimum of these ratios is the capacity factor. If the temperature rises above the coolant temperature minus the heat transfer delta, the system needs to shut off.

Parameters:

  • designTemperature (float | int) – Design ambient temperature of the cooling system [°C].

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) [°C].

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit from the heat load to the A-frame [Pa]. Default is 200000.

Returns:

The method stores the calculated capacity factor in `self.sim_data[“capacity_factor”]’.

Return type:

None

simulate_air_source_heat_pump(targetTemperature=100, secondLawEfficiency=0.5)#

Simulate an air-source heat pump and calculate its coefficient of performance (COP) and conversion factors.

The method computes the COP based on the ambient air temperature and the target supply temperature of the heat pump. It also calculates the electricity conversion factor per unit of thermal energy delivered.

Parameters:

  • targetTemperature (float | int, optional) – Target temperature at which the heat should be supplied [°C]. Default is 100.

  • secondLawEfficiency (float | int, optional) – Second law efficiency of the heat pump [0,1]. Default is 0.5.

Returns:

The calculated COP and conversion factors are stored in self.sim_data. The self.units dictionary is also updated to reflect the units of the heat pump.

Return type:

None

Notes

  • The electricity conversion factor is calculated as -1/COP [kWh_el/kWh_th].

  • Units set include thermal capacity (kW_th), electricity conversion factor (kWh_el/kWh_th),

and electricity input (kWh_el).

class reskit.cooling_heating.CoolingHeatingWorkflowManager(placements)#

Bases: reskit.workflow_manager.WorkflowManager

The WorkflowManager class assists with the construction of more specialized WorkflowManagers, such as the WindWorkflowManager or the SolarWorkflowManager. In addition to providing the general structure for simulation workflow management, the WorkflowManager also defines functionalities which should be common across all WorkflowManagers.

This includes:

  • Basic initialization

  • Time domain management

  • Reading weather data

  • Adjusting variables by a long-run-average value

  • Applying simple loss factors

  • Saving the state of WorkflowManagers to XArray datasets, either in memory or on disc

Initialization:#

WorkflowManager( placements )

__init_(self, placements)

Initialization of an instance of the generic CoolingHeatingWorkflowManager class.

param placements:

The locations that the simulation should be run for. Columns must include “lon”, “lat” (CRS: 4326) and “capacity” -The capacity is the nominal capacity of the Heating/Cooling System

type placements:

pandas Dataframe

returns:

  • CoolingHeatingWorkflowManager

  • Sources

  • [1] https (//www.engineeringtoolbox.com/air-density-specific-weight-d_600.html)

  • [2] https (//www.engineeringtoolbox.com/air-specific-heat-capacity-d_705.html)

airData#
evaporationCoolingData#
calculate_approach_evaporative_cooling(temperatureCoolant, heatTransferDelta, efficiencyCoolingTower)#

Calculate the approach temperature for an evaporative-cooling system.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int) – Temperature difference required for heat transfer from air to coolant [K]

  • efficiencyCoolingTower (float | int) – Efficiency of the cooling tower system [0, 1]

Returns:

Approach temperature for the specified weather conditions.

Return type:

float or np.ndarray

References

[1] 10.1016/j.ijhydene.2024.11.381

calculate_water_losses_evaporative_cooling(temperatureCoolant, heatTransferDelta, efficiencyCoolingTower, factorDriftLosses=0.001, typical_cycles_blowdown=5)#

Calculate the water losses for an evaporative-cooling system.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int) – Temperature difference required for heat transfer from air to coolant [K]

  • efficiencyCoolingTower (float | int) – Efficiency of the cooling tower system [0, 1]

  • factorDriftLosses (float | int) – Drift losses by small water droplets carried away by the exhaust air. Defaults to 0.001. [1]

  • typical_cycles_blowdown (int) – after how many cycles the blowdown occurs to prevent accumulation of impurities. Defaults to 5. [2]

Returns:

Specific water losses of evaporative cooling towers (per kWh of cooling load) for the specified weather conditions.

Return type:

float or np.ndarray

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] 10.1016/j.enconman.2020.113610

calculate_fan_power_air_cooling(temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, pressureDropAir=261, designTemperature=None)#

Calculate the fan power demand for an air-cooling system.

This method computes the electrical power required by the fan to transfer heat from the air to a coolant, based on the air properties and the cooling load temperature. It can evaluate either for a given design air temperature or for the time series of ambient air temperatures stored in self.sim_data.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • designTemperature (float | int, optional) – If specified, the calculation is only evaluated at this air temperature [°C]. Default is None.

Returns:

Fan power demand in kWh per kWh of cooling. Returns a single value if designTemperature is provided, or a time series array otherwise.

Return type:

float or np.ndarray

Notes

  • Uses linear interpolation of air specific heat (cp) and density (rho) from self.airData.

  • Assigns np.inf to any time step where the temperature difference is insufficient for cooling.

  • Stores the time series result in self.sim_data[“conversion_factor_fan_electricity”] if designTemperature is None.

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] http://hdl.handle.net/1853/55674

calculate_pump_power_air_cooling(temperatureCoolant, heatTransferDelta=5, efficiencyPump=0.7, pressureDropWater=200000, designTemperature=None)#

Calculate the pump power demand for an air-cooling system.

This method computes the electrical power required by the pump to circulate coolant for an air-cooling system, based on the cooling load temperature, pressure drop, and pump efficiency. It can evaluate either for a given design air temperature or for the time series of ambient air temperatures stored in self.sim_data.

Parameters:

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit between the heat load and the cooling frame [Pa]. Default is 200000.

  • designTemperature (float | int, optional) – If specified, the calculation is only evaluated at this air temperature [°C]. Default is None.

Returns:

  • float or np.ndarray

  • Pump power demand in kWh per kWh of cooling. Returns a single value if

  • designTemperature is provided, or a time series array otherwise.

Notes

  • Assumes constant water density of 1000 kg/m³ and specific heat capacity of 4.186 kJ/(kg·K) = 0.00116 kWh/(kg·K).

  • Assigns np.inf to any time step where the temperature difference is insufficient for cooling.

  • Stores the time series result in self.sim_data[“conversion_factor_pump_electricity”] if designTemperature is None.

References

[1] 10.1016/j.ijhydene.2024.11.381 [2] 10.1016/j.enconman.2020.113610

calculate_relative_cost_factor_air_cooling(designTemperature, temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, efficiencyPump=0.7, pressureDropAir=261, pressureDropWater=200000)#

Calculate the relative (air temperature dependent) cost factor of an air-cooling system.

The air-cooling system consists of fans, water pumps, and an A-frame heat exchanger that transfers heat from water to air. The A-frame is assumed to always transfer heat with the specified heatTransferDelta. Fan and pump costs depend on the installed nominal power, which varies with ambient air temperature. The relative cost factor is defined as the ratio of cost at the design temperature to the cost at actual ambient temperatures. To cool the same amount of heat at an air temperature higher than the design air temperature, the CAPEX would rise, since pump and fan capacity would increase.

Parameters:

  • designTemperature (float | int) – Design ambient temperature of the cooling system [°C].

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) [°C].

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit from the heat load to the A-frame [Pa]. Default is 200000.

Returns:

  • None

  • The method stores the calculated relative cost factor in self.sim_data[“relative_cost_factor”]

  • and updates the self.units dictionary with air-cooling system units.

Notes

  • Fan and pump CAPEX are calculated based on nominal power at the design temperature and

scaled according to ambient conditions [1]. - A-frame cost is assumed constant and independent of ambient temperature [2]. - The relative cost factor reflects the relative increase in cost at varying ambient conditions compared to the design point.

References

[1] 10.1016/j.energy.2015.05.081 [2] 10.1016/j.enconman.2020.113610

calculate_capacity_factor_air_cooling(designTemperature, temperatureCoolant, heatTransferDelta=5, efficiencyFan=0.7, efficiencyPump=0.7, pressureDropAir=261, pressureDropWater=200000)#

Calculate the capacity factor of an air-cooling system.

The air-cooling system can only provide the cooling load if the ambient air temperature is lower than the design temperature. If the temperature is above the design temperature, pumps and fans are designed too small to provide the necessary flows and the system can only provide less cooling. The reduction in cooling can be calculated based on the ratio of the design power and the theoretically required power for both, fans and pumps individually. The minimum of these ratios is the capacity factor. If the temperature rises above the coolant temperature minus the heat transfer delta, the system needs to shut off.

Parameters:

  • designTemperature (float | int) – Design ambient temperature of the cooling system [°C].

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) [°C].

  • heatTransferDelta (float | int, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float | int, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • efficiencyPump (float | int, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropAir (float | int, optional) – Pressure drop of air through the channels of the cooling frame [Pa]. Default is 261.

  • pressureDropWater (float | int, optional) – Pressure drop of the water circuit from the heat load to the A-frame [Pa]. Default is 200000.

Returns:

The method stores the calculated capacity factor in `self.sim_data[“capacity_factor”]’.

Return type:

None

simulate_air_source_heat_pump(targetTemperature=100, secondLawEfficiency=0.5)#

Simulate an air-source heat pump and calculate its coefficient of performance (COP) and conversion factors.

The method computes the COP based on the ambient air temperature and the target supply temperature of the heat pump. It also calculates the electricity conversion factor per unit of thermal energy delivered.

Parameters:

  • targetTemperature (float | int, optional) – Target temperature at which the heat should be supplied [°C]. Default is 100.

  • secondLawEfficiency (float | int, optional) – Second law efficiency of the heat pump [0,1]. Default is 0.5.

Returns:

The calculated COP and conversion factors are stored in self.sim_data. The self.units dictionary is also updated to reflect the units of the heat pump.

Return type:

None

Notes

  • The electricity conversion factor is calculated as -1/COP [kWh_el/kWh_th].

  • Units set include thermal capacity (kW_th), electricity conversion factor (kWh_el/kWh_th),

and electricity input (kWh_el).

reskit.cooling_heating.calculate_relative_humidity(dewpoint_temperature, air_temperature)#

Function to calculate the relative humidity from dewpoint temperature and air temperature using the Sonntag formula.

Parameters:

  • dewpoint_temperature (float | int) – dewpoint temperature in °C

  • air_temperature (float | int) – air temperature in °C

References

[1] https://www.npl.co.uk/resources/q-a/dew-point-and-relative-humidity

reskit.cooling_heating.calculate_wet_bulb_temperature(air_temperature, relative_humidity)#

Function to calculate the wet bulb temperature from air temperature and relative humidity.

Parameters:

  • air_temperature (float | int) – air temperature in °C

  • relative_humidity (float | int) – relative humidity in %

References

[1] Roland Stull. Wet-bulb temperature from relative humidity and air temperature. Journal of Applied Meteorology and Climatology, 50:2267–2269, 11 2011.

reskit.cooling_heating.evaporative_cooling_wortmann2025(placements, era5_path, temperatureCoolant, heatTransferDelta, efficiencyCoolingTower, factorDriftLosses=0.001, typical_cycles_blowdown=5, output_netcdf_path=None, output_variables=None)#

Simulate an evaporative-cooling system based on ERA5 weather data.

This function calculates the water losses of an evaporative-cooling systems at varying ambient conditions (temperature, humidity). Results can be saved to a NetCDF file.

Parameters:

  • placements (pd.DataFrame) – DataFrame specifying the plant locations and their capacities.

  • era5_path (str) – Path to the ERA5 weather data source.

  • temperatureCoolant (float | int) – Temperature of the cooling load (lower temperature if sensible heat transfer) in °C.

  • heatTransferDelta (float | int) – Temperature difference required for heat transfer from air to coolant [K]

  • efficiencyCoolingTower (float | int) – Efficiency of the cooling tower system [0, 1]

  • factorDriftLosses (float | int) – Drift losses by small water droplets carried away by the exhaust air. Defaults to 0.001.

  • typical_cycles_blowdown (int) – after how many cycles the blowdown occurs to prevent accumulation of impurities. Defaults to 5.

  • output_netcdf_path (str, optional) – Path to save the output NetCDF file. Default is None.

  • output_variables (list of str, optional) – List of simulation variables to save to the NetCDF file. If None, all variables are saved.

Returns:

Simulation results, including water losses. Can be limited to output_variables if specified.

Return type:

xarray.Dataset

Notes

  • Stores all relevant units in wf.units for reference.

Raises:

AssertionError – If input parameters are not of expected type or if efficiency values are not within (0, 1].

Parameters:

  • placements (pandas.DataFrame)

  • era5_path (str)

  • temperatureCoolant (int | float)

  • heatTransferDelta (int | float)

  • efficiencyCoolingTower (int | float)

  • factorDriftLosses (float | int)

  • typical_cycles_blowdown (int)

  • output_netcdf_path (str)

  • output_variables (List[str])

reskit.cooling_heating.air_cooling_wenzel2025(placements, era5_path, temperatureCoolant, designTemperature, heatTransferDelta=5, efficiencyFan=0.7, pressureDropAir=261, efficiencyPump=0.7, pressureDropWater=200000, output_netcdf_path=None, output_variables=None)#

Simulate an air-cooling system based on ERA5 weather data.

This function calculates the fan and pump power requirements, capacity factor, and total electricity demand for air-cooling systems at varying ambient temperatures. Results can be saved to a NetCDF file.

Parameters:

  • placements (pd.DataFrame) – DataFrame specifying the plant locations and their capacities.

  • era5_path (str) – Path to the ERA5 weather data source.

  • temperatureCoolant (float) – Temperature of the heat load to be cooled [°C].

  • designTemperature (float) – Temperature for the nominal design point of the air cooling system [°C].

  • heatTransferDelta (float, optional) – Temperature difference required for heat transfer from air to coolant [K]. Default is 5.

  • efficiencyFan (float, optional) – Efficiency of the fan system [0, 1]. Default is 0.7.

  • pressureDropAir (float, optional) – Pressure drop of air through the cooling frame channels [Pa]. Default is 261.

  • efficiencyPump (float, optional) – Efficiency of the pump system [0, 1]. Default is 0.7.

  • pressureDropWater (float, optional) – Pressure drop of water through the circuit [Pa]. Default is 200000.

  • output_netcdf_path (str, optional) – Path to save the output NetCDF file. Default is None.

  • output_variables (list of str, optional) – List of simulation variables to save to the NetCDF file. If None, all variables are saved.

Returns:

Simulation results, including capacity factor, fan/pump/electricity inputs, and cooling output. Can be limited to output_variables if specified.

Return type:

xarray.Dataset

Notes

  • Calculates fan and pump power using calculate_fan_power_air_cooling and calculate_pump_power_air_cooling.

  • Computes the system capacity factor relative to the design temperature using calculate_capacity_factor_air_cooling.

  • Total electricity input includes contributions from both fan and pump.

  • Stores all relevant units in wf.units for reference.

Raises:

AssertionError – If input parameters are not of expected type or if efficiency values are not within (0, 1].

Parameters:

  • placements (pandas.DataFrame)

  • era5_path (str)

  • temperatureCoolant (int | float)

  • designTemperature (int | float)

  • heatTransferDelta (int | float)

  • efficiencyFan (int | float)

  • pressureDropAir (int | float)

  • efficiencyPump (int | float)

  • pressureDropWater (int | float)

  • output_netcdf_path (str)

  • output_variables (List[str])

reskit.cooling_heating.air_source_heat_pump(placements, era5_path, targetTemperature=100, secondLawEfficiency=0.5, output_netcdf_path=None, output_variables=None)#

Simulate an air-source heat pump based on ERA5 weather data.

This function calculates the coefficient of performance (COP) and electricity demand of an air-source heat pump at varying ambient temperatures. Results can be saved to a NetCDF file.

Parameters:

  • placements (pd.DataFrame) – DataFrame specifying plant locations and capacities.

  • era5_path (str) – Path to the ERA5 weather data source.

  • targetTemperature (float, optional) – Temperature at which the heat pump should supply the heat [°C]. Default is 100.

  • secondLawEfficiency (float, optional) – Second law efficiency of the heat pump [0,1]. Default is 0.5.

  • output_netcdf_path (str, optional) – Path to save the output NetCDF file. Default is None.

  • output_variables (list of str, optional) – List of simulation variables to save to the NetCDF file. If None, all variables are saved.

Returns:

Simulation results including COP, electricity conversion factor, and electricity input. Can be limited to output_variables if specified.

Return type:

xarray.Dataset

Raises:

AssertionError – If targetTemperature or secondLawEfficiency are not numeric or if secondLawEfficiency is not within (0, 1].

Notes

  • The electricity conversion factor is calculated as -1/COP [kWh_el/kWh_th].

  • Electricity input is computed for each plant based on its capacity and the COP.

  • Units are stored in wf.units for reference.