Fig. 2: The process of converting from temperature to BAIT, to degree days, to energy demand.
From: A global model of hourly space heating and cooling demand at multiple spatial scales
Measured electricity demand covering all end uses in New York State (NYISO) from ref. 77 is used as a case study. Supplementary Figs. 1–42 give plots for all other regions. a, The daily temperature averaged across the state (weighted by population density) and the corresponding BAIT. BAIT follows temperature but has lower daily variability due to temporal smoothing, and is generally higher than temperature during spring and lower in autumn months due to differences in solar irradiance relative to temperature. b, The corresponding HDDs and CDDs using the optimal temperature thresholds derived from metered electricity demand. c,d, The relationship between these degree days and daily metered electricity demand, with points showing demand from individual weekdays and lines showing the derived linear regressions (n = 1,392) used to model energy demand. Panel headings give the threshold temperatures and power coefficients for heating and cooling. e, The relationship between metered daily electricity demand and BAIT, with the tightness of the fit indicating the model’s capability to predict demand. f, Comparison of historical daily electricity demand with the model estimate covering the entire span of metered data. Demand is measured in terms of average power demand through the day in gigawatts. Gaps are shown where metered data are either missing or unrepresentative due to national holidays (for example, Christmas). c,d,f focus only on working days to remove noise from demand being lower on weekends and holidays for socioeconomic reasons. g,h, Comparison of historical and modelled demand at hourly resolution over two fortnights covering winter (g) and summer (h) months. Inset letters denote days of the week. Legends in f and h give statistical measures of the fit quality covering the entire multi-year period.
