7R2C
====

Overview
--------

7R2C HVAC model aligned with VDI 6007 multi‑node transient method.

Purpose and scope:

- Represents a thermal zone with two thermal masses and multiple heat‑transfer paths to capture phase shifts and damping effects beyond 1R1C/5R1C models. Suitable for envelope studies, solar‑gain interactions, and scenarios where interior vs exterior mass coupling matters.

Conceptual structure:

- Two capacitances: exterior mass (AW = Außenwände) and interior mass (IW = Innenwände).

- Seven resistances connect masses, surfaces, indoor air, and outside, including parallel window/opaque paths on the AW side and a radiative “star” network that couples surfaces and air.

- Three principal temperature states are resolved per step: • theta_m_aw (exterior mass), • theta_m_iw (interior mass), • theta_air (indoor air).

- Internal and solar gains are split into convective (air) and radiant parts distributed to AW/IW surfaces via σ parameters (sigma_aw, sigma_iw; remainder convective).

- Ventilation is split into mechanical and infiltration parts and applied to the air node.

Notes:

- Windows are modeled as a parallel conductance in the AW branch, affecting the equivalent resistance split between opaque and transparent parts.

- The equivalent outdoor temperature T_eq for the AW path is a conductance‑weighted blend of sol‑air temperature (opaque) and ambient dry‑bulb (windows).

- Includes a stabilization of initial states to reduce sensitivity to initial conditions.

- For lighter‑weight simulations with fewer states consider 5R1C (ISO 13790); for quicker control‑oriented studies consider 1R1C.

Reference:

- VDI 6007: Calculation of transient thermal response of rooms and buildings (7R2C concept).


Key facts
---------

- Method key: ``7R2C``

- Supported types:


  - ``hvac``



Requirements
------------

Required keys (specify in objects)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


- ``R_1_AW[K W-1]``
- ``C_1_AW[J K-1]``
- ``R_1_IW[K W-1]``
- ``C_1_IW[J K-1]``
- ``R_alpha_star_IL[K W-1]``
- ``R_alpha_star_AW[K W-1]``
- ``R_alpha_star_IW[K W-1]``
- ``R_rest_AW[K W-1]``
- ``weather``



Optional keys (specify in objects)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


- ``power_heating[W]``
- ``power_cooling[W]``
- ``active_heating``
- ``active_cooling``
- ``active_gains_internal``
- ``active_gains_solar``
- ``active_ventilation``
- ``init_temperature[C]``
- ``min_temperature[C]``
- ``max_temperature[C]``
- ``deadband[K]``
- ``target_humidity_rel[1]``
- ``gains_internal_latent[W]``
- ``supply_temperature[C]``
- ``area[m2]``
- ``height[m]``
- ``fraction_conv_internal``
- ``fraction_rad_AW``
- ``sigma_7R2C_AW``
- ``sigma_7R2C_IW``
- ``H_ve[W K-1]``
- ``ventilation_split``
- ``T_eq[C]``
- ``T_eq[C]_column``
- ``T_eq_alpha_SW[1]``
- ``T_eq_h_o[W m-2 K-1]``
- ``gains_internal[W]``
- ``gains_solar[W]``
- ``ventilation[W K-1]``



Required data (specify in data)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


- ``weather``



Optional data (specify in data)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


- ``windows``
- ``gains_internal[W]``
- ``gains_solar[W]``
- ``H_ve[W K-1]``
- ``T_eq[C]``



Outputs
-------

Summary metrics
~~~~~~~~~~~~~~~


.. list-table::
   :widths: auto
   :header-rows: 1

   * - Key
     - Description

   * - ``heating:demand[Wh]``
     - total heating demand

   * - ``heating:load_max[W]``
     - maximum heating load

   * - ``cooling:demand[Wh]``
     - total cooling demand (sensible + latent)

   * - ``cooling:load_max[W]``
     - maximum cooling load (sensible + latent)



Timeseries columns
~~~~~~~~~~~~~~~~~~


.. list-table::
   :widths: auto
   :header-rows: 1

   * - Column
     - Description

   * - ``indoor_temperature[C]``
     - indoor air temperature

   * - ``heating:load[W]``
     - heating load

   * - ``cooling:load[W]``
     - total cooling load (sensible + latent)

   * - ``cooling:sensible_load[W]``
     - sensible cooling load

   * - ``cooling:latent_load[W]``
     - latent cooling load



Public methods
--------------


- generate

  .. code-block:: python

         def generate(
           self, obj: dict = None, data: dict = None, results: dict = None, ts_type: str = Types.HVAC, **kwargs
       ) -> dict:
           obj, data = self._process_kwargs(obj, data, **kwargs)
           obj, data = self._get_input_data(obj, data, ts_type)
           data = self._prepare_inputs(obj, data)

           temp_in, p_heat, p_cool = calculate_timeseries_7r2c(**data)

           meta = data["meta"]
           p_cool_sensible, p_cool_latent = self._apply_latent_cooling(obj, data, p_cool)
           return self._format_output(
               temp_in.round(3),
               p_heat.round().astype(int),
               p_cool_sensible.round().astype(int),
               p_cool_latent.round().astype(int),
               meta["index"],
               meta["dt_s"],
           )