Model

Main simulation engine: initialization, calibration, and scenario execution.

When to use this module

Use project.model when you need to:

  • prepare scenario configurations before execution

  • initialize model state from parsed inputs

  • run a full simulation programmatically

Common entrypoints include prepare_config and res_irf.

Main module of the model.

This module contains the main functions to run the model. The main function is res_irf that runs the model for a given scenario.

Functions

res_irf

Res-IRF model.

calibration_res_irf

Calibrate Res-IRF and returns calibrated parameters.

get_inputs

Initialize thermal buildings object based on input dictionary.

config2inputs

Create main Python object from configuration file.

initialize

Create main Python objects read by model.

stock_turnover

Update stock vintage due to renovation, demolition and construction.

select_post_inputs

Inputs used during post-treatment but not used during the iteration.

get_config

Get the configuration file.

prepare_config

Prepare the configuration file.

project.model.calibration_res_irf(path, config=None, level_logger='DEBUG')[source]

Calibrate Res-IRF and returns calibrated parameters. Function is useful for running multiple scenarios with the same calibration. Typical example is for sensitivity analysis or elasticity calculation. :param config: :param path: :param level_logger:

project.model.config2inputs(config=None, scenario=None)[source]

Create main Python object from configuration file.

Parameters:

config (dict) –

project.model.get_config(scenario=None) → dict[source]
project.model.get_inputs(path=None, config=None, variables=None, scenario=None)[source]

Initialize thermal buildings object based on input dictionary.

Parameters:

  • path (str, optional) – If None do not write output.

  • config (dict, optional) – If config is None use configuration file of Reference scenario

  • variables (list, optional) – ‘buildings’, ‘energy_prices’, ‘cost_insulation’, ‘carbon_emission’, ‘carbon_value_kwh’, ‘health_cost’

Return type:

dict

project.model.initialize(inputs, stock, year, taxes, path=None, config=None, logger=None, level_logger='DEBUG')[source]

Create main Python objects read by model.

Parameters:

  • inputs –

  • stock –

  • year –

  • taxes –

  • config –

  • path –

  • logger –

project.model.prepare_config(config)[source]
project.model.res_irf(config, path, level_logger='DEBUG')[source]

Res-IRF model.

Parameters:

  • config (dict) – Scenario-specific input

  • path (str) – Scenario-specific output path

  • level_logger (str) –

Returns:

  • str – Scenario name

  • pd.DataFrame – Detailed results

project.model.select_post_inputs(parsed_inputs)[source]

Inputs used during post-treatment but not used during the iteration.

Parameters:

parsed_inputs (dict) –

Return type:

dict

project.model.stock_turnover(buildings, prices, taxes, cost_heater, cost_insulation, frequency_insulation, p_heater, p_insulation, flow_built, year, post_inputs, calib_heater=None, calib_renovation=None, financing_cost=None, prices_before=None, climate=None, district_heating=None, step=1, demolition_rate=None, memory=False, exogenous_social=None, output_options='full', premature_replacement=None, supply=None, carbon_content=None, carbon_content_before=None, default_quality=None, credit_constraint=True)[source]

Update stock vintage due to renovation, demolition and construction.

Returns:

  • buildings (AgentBuildings) – Updated AgentBuildings object.

  • stock (: Series)

  • output (Series)