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

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

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

  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"],
    )