energy_signature

Proposed Energy Signature (PES) after Eriksson et al. (2020) — iterative determination of balance temperature, heat loss coefficient, domestic hot water circulation (DHWC) and domestic hot water (DHW) demand from hourly time-series data.

See Energy Signature for usage examples. The plotting counterparts live in plots.

API Reference

Proposed Energy Signature (PES) computation.

Implements the iterative algorithm from Eriksson et al. (2020), “Development and validation of energy signature method”, Energy & Buildings 210, 109756.

Determines balance temperature, heat loss coefficient, domestic hot water circulation (DHWC) demand, and domestic hot water (DHW) demand from hourly time-series data.

class pyedautils.energy_signature.PESResult(tb: float, q_tot: float, p_dhwc: float, p_dhw: float, p_ihg: float)

Bases: NamedTuple

Result of the Proposed Energy Signature computation.

tb

Balance temperature [°C].

Type:

float

q_tot

Total heat loss coefficient [kW/K].

Type:

float

p_dhwc

Domestic hot water circulation demand [kW] (standby).

Type:

float

p_dhw

Domestic hot water demand [kW].

Type:

float

p_ihg

Internal heat gains [kW] (input parameter echoed back).

Type:

float

tb: float

Alias for field number 0

q_tot: float

Alias for field number 1

p_dhwc: float

Alias for field number 2

p_dhw: float

Alias for field number 3

p_ihg: float

Alias for field number 4

property p_hw

property p_stby

pyedautils.energy_signature.compute_pes(data: DataFrame, p_ihg: float = 0.0, max_iter: int = 50) → PESResult

Compute the Proposed Energy Signature parameters.

The algorithm iteratively determines the balance temperature and derives the heat loss coefficient, DHWC demand (P_dhwc) and DHW demand (P_dhw) from hourly building data.

Following Eriksson et al. (2020):

• P_dhwc is the mean of daily minimum power on days where at least one hour has T_oa > T_b (Section 3.2.2).

• P_dhw is the mean of all hourly power at T_oa > T_b, minus P_dhwc (Section 3.2.3).

• Q_tot is computed from nighttime hours (0:00–4:59) in December–February using Eq. (7): Q_tot = (P_dh + P_ihg - P_dhwc) / (T_room - T_oa) (Section 3.2.1).

• T_b is scanned from 10–20 °C in 0.1 °C steps; the value where calculated annual energy is closest to measured is chosen (Section 3.2.4, E_tot criterion).

Parameters:

• data – DataFrame with columns [timestamp, outside_temp, power, room_temp]. timestamp must be parseable by pd.to_datetime, outside_temp and room_temp in °C, power in kW.

• p_ihg – Internal heat gains [kW]. Default 0.

• max_iter – Maximum number of iterations. Default 50.

Returns:

PESResult with the computed parameters.

Raises:

ValueError – If convergence is not reached within max_iter.