Fig. 1: Illustration of the Demand.ninja method.
From: A global model of hourly space heating and cooling demand at multiple spatial scales
a, The nine stages of the model’s workflow. Raw data inputs (labelled i–iii) are shown in blue, derived or user-specified inputs (labelled iv–vi) in turquoise, calculation stages (labelled 1–9) in yellow and outputs from the model in red. b, How solar irradiance and wind speed affect the relationship between raw temperature and BAIT in Great Britain from 2015 to 2019, relating to step (1) and input (iv) in the model’s workflow. Higher wind speeds reduce BAIT below air temperature, and higher solar irradiance increases BAIT above air temperature. A diagonal line with a 1:1 relationship is shown for reference. c, The relationship between BAIT and electricity demand using an exemplary home in Austin, Texas, United States, relating to steps (6) and (7) and inputs (v) and (vi) in the workflow. Individual data points show daily-average metered demand in the home, with colours relating to how they are classified by the model. The black line shows the Demand.ninja regression (that is, modelled demand) with change points for when heating and cooling are required (n = 364). The lines labelled Theat and Tcool refer to balance point temperature thresholds (°C), line Pbase refers to baseline or temperature-independent energy demand (kWh) and slopes Pheat and Pcool are the coefficients for heating and cooling demand (kWh d−1 °C−1), which show how much demand increases with falling/rising BAIT.
