Load data

GHEtool supports different types of load data (and more are coming, check our project for more information: https://github.com/users/wouterpeere/projects/2) Currently you can use:

Geothermal loads with a monthly resolution for one year (so it repeats itself every year)
Geothermal loads with an hourly resolution for one year (so it repeats itself every year)
Geothermal loads with an hourly resolution but multiple years (it does not repeat itself)

All of the load classes are based children of the abstract _LoadData class.

class GHEtool.VariableClasses.LoadData._LoadData._LoadData(hourly_resolution: bool, simulation_period: int = 20)

Bases: BaseClass, ABC

This class contains information w.r.t. load data for the borefield sizing.

Parameters

hourly_resolutionbool: True if the load class uses an hourly resolution
simulation_periodint: Length of the simulation period in years

property UPM: ndarray

Depending on whether all months are assumed to have equal length, the UPM are either constant or vary during the year.

Returns

Hours per monthnp.ndarray

_calculate_first_year_params(HC: bool) → tuple

This function calculates the parameters for the sizing based on the first year of operation. This is needed for the L2 sizing.

Parameters

HCbool: True if the borefield is limited by extraction load

Returns

th, tpm, tcm, qh, qpm, qcmfloat: Peak duration [s], cumulative time passed at the start of the month [s], cumulative time passed at the end of the month [s], peak load [W], average cumulative load of the past months [W avg], average load of the current month [W avg]

_calculate_last_year_params(HC: bool) → tuple

This function calculates the parameters for the sizing based on the last year of operation. This is needed for the L2 sizing.

Parameters

HCbool: True if the borefield is limited by extraction load

Returns

th, qh, qm, qafloat: Peak length [s], peak load [W], corresponding monthly load [W], yearly imbalance [W]

abstract _check_input(input: Union[ndarray, list, tuple]) → bool

This function checks whether the input is valid or not.

Parameters

inputnp.ndarray, list, tuple: Thermal load input

Returns

bool: True if the input is correct for the load class

add_dhw(dhw: float) → None

This function adds the domestic hot water (dhw). An error is raies is the dhw is not positive.

Parameters

dhwfloat: Yearly consumption of domestic hot water [kWh/year]

Returns

None

property all_months_equal: bool

Returns the attribute all months are equal

Returns

bool: True if the months are assumed to be of equal length (i.e. 730 hours/month). False if the correct number of hours is used.

abstract baseload_cooling() → ndarray

This function returns the baseload cooling in kWh/month.

Returns

baseload coolingnp.ndarray

property baseload_cooling_power: ndarray

This function returns the baseload cooling in kW avg/month.

Returns

baseload coolingnp.ndarray

property baseload_cooling_power_simulation_period: ndarray

This function returns the average cooling power in kW avg/month for a whole simulation period.

Returns

average cooling powernp.ndarray: average cooling for the whole simulation period

property baseload_cooling_simulation_period: ndarray

This function returns the baseload cooling in kWh/month for a whole simulation period.

Returns

baseload coolingnp.ndarray: baseload cooling for the whole simulation period

abstract baseload_heating() → ndarray

This function returns the baseload heating in kWh/month.

Returns

baseload heatingnp.ndarray

property baseload_heating_power: ndarray

This function returns the baseload heating in kW avg/month.

Returns

baseload heatingnp.ndarray

property baseload_heating_power_simulation_period: ndarray

This function returns the average heating power in kW avg/month for a whole simulation period.

Returns

average heating powernp.ndarray: average heating power for the whole simulation period

property baseload_heating_simulation_period: ndarray

This function returns the baseload heating in kWh/month for a whole simulation period.

Returns

baseload heatingnp.ndarray: baseload heating for the whole simulation period

abstract correct_for_start_month(array: ndarray) → ndarray

This function corrects the load for the correct start month. If the simulation starts in september, the start month is 9 and hence the array should start at index 9.

Parameters

arraynp.ndarray: Load array

Returns

loadnp.ndarray

property dhw: float

This function returns the yearly domestic hot water consumption.

Returns

dhwfloat: Yearly domestic hot water consumption [kWh/year]

property dhw_power: float

This function returns the power related to the dhw production.

Returns

dhw powerfloat

static get_month_index(peak_load, avg_load) → int

This function calculates and returns the month index (i.e. the index of the month in which the field should be sized). It does so by taking 1) the month with the highest peak load. 2) if all the peak loads are the same, it takes the month with the highest average load 3) if all average loads are the same, it takes the last month

Parameters

peak_loadnp.ndarray: array with the peak loads [kW]
avg_loadnp.ndarray: array with the monthly average loads [kW]

Returns

month_indexint: 0 = jan, 1 = feb …

property imbalance: float

This function calculates the ground imbalance. A positive imbalance means that the field is injection dominated, i.e. it heats up every year.

Returns

imbalancefloat

property max_peak_cooling: float

This returns the max peak cooling in kW.

Returns

max peak coolingfloat

property max_peak_heating: float

This returns the max peak heating in kW.

Returns

max peak heatingfloat

property monthly_average_load: ndarray

This function calculates the average monthly load in kW.

Returns

monthly average loadnp.ndarray

property monthly_average_load_simulation_period: ndarray

This function calculates the average monthly load in kW for the whole simulation period.

Returns

monthly average loadnp.ndarray

abstract peak_cooling() → ndarray

This function returns the peak cooling load in kW/month.

Returns

peak coolingnp.ndarray

property peak_cooling_duration: float

Duration of the peak in cooling.

Returns

Duration of the peak in cooling [s]

property peak_cooling_simulation_period: ndarray

This function returns the peak cooling in kW/month for a whole simulation period.

Returns

peak coolingnp.ndarray: peak cooling for the whole simulation period

property peak_duration: None

Dummy object to set the length peak for both heating and cooling.

Returns

None

abstract peak_heating() → ndarray

This function returns the peak heating load in kW/month.

Returns

peak heatingnp.ndarray

property peak_heating_duration: float

Length of the peak in heating.

Returns

Length peak in heating [s]

property peak_heating_simulation_period: ndarray

This function returns the peak heating in kW/month for a whole simulation period.

Returns

peak heatingnp.ndarray: peak heating for the whole simulation period

property start_month: int

This function returns the start month.

Returns

float: Start month

property time_L3: ndarray

Time for L3 sizing, i.e. an array with monthly the cumulative seconds that have passed. [744, 1416 …] * 3600

Returns

Times for the L3 sizingnp.ndarray

property time_L4: ndarray

Times for the L4 sizing, i.e. an array with hourly the cumulative seconds that have passed. [1, 2, 3, 4 …] * 3600

Returns

Times for the L4 sizingnp.ndarray

property ty: float

Simulation period in seconds.

Returns

Simulation period in seconds

property yearly_cooling_load: float

This function returns the yearly cooling load in kWh/year.

Returns

float: Yearly cooling load kWh/year

property yearly_heating_load: float

This function returns the yearly heating load in kWh/year.

Returns

float: Yearly heating load kWh/year

This class contains all the information for geothermal load data with a monthly resolution and absolute input. This means that the inputs are both in kWh/month and kW/month.