Fig. 3: Ability of the Demand.ninja to model UK natural gas demand at daily resolution.
From: A global model of hourly space heating and cooling demand at multiple spatial scales
a–g, The correlation between modelled HDDs and measured gas demand covering all end uses excluding power generation from ref. 78 over the period 2015–2019. Points show demand on individual days and lines show the derived linear regressions (n = 1,827). Each panel shows incrementally more sophisticated models, starting with the most common elements and moving to the most novel. a, A basic degree-day model using the national average temperature. b, Degree days calculated using gridded temperatures that have been population weighted. c, The addition of smoothing temperatures over the preceding two days. d, The addition of wind chill (higher wind speeds reducing the temperature index). e, The addition of humidity effect (greater humidity reducing the temperature index when it is cold or increasing it when it is hot—note that this has a more influential impact in hotter climates). f, The addition of solar gains (greater irradiance increasing the temperature index). g, The superposition of multiple simulations with stochastically varied parameters to reflect the diversity of building construction and occupant behaviour. h, Summary of the improvement in modelling quality when adding each element of the Demand.ninja process. Each panel uses the optimal heating temperature threshold for the given model, so that the improvements shown relate to which elements of the BAIT process are included/excluded, rather than miscalibration of the model. Supplementary Fig. 45 shows the summary of model improvements (h) for electricity demand in four regions for comparison.
