The Impact of Climate Change: An Empirical Analysis of Smart Thermostat Data

As climate change accelerates, extreme weather events are causing unprecedented fluctuations in residential heating and cooling needs. Even modest demand reductions could help grid operators avoid power crises, but doing so requires understanding how households use thermostats and respond to ambient weather conditions. To this end, we analyze high-frequency, micro-level time-series data from smart thermostat users to examine how households adjust their thermostat operations (i.e., HVAC modes and temperature setpoints) in response to outdoor temperature. Using our predictive models, we conduct a set of simulations to estimate household energy consumption under different climate scenarios and the subsequent implications on energy supply operations. Our simulations reveal that by 2050, total cooling energy usage could rise by at least 67% and as high as 300%, depending on climate scenario and geographical location, with peak usage and demand variability increasing by over 50%. While heating consumption is reduced, this decrease is dominated by the increase in cooling energy usage. Comparing households within a city, we find that households who automate their HVAC mode decisions exhibit reduced responsiveness to temperature shocks. In particular, their heating energy consumption remains elevated, even in the face of a changing climate, compared to households who rely on less automation. With respect to temperature setpoints, households that frequently override pre-programmed schedules show inertia, remaining at the overridden setpoints for long periods and only partially revert to their pre-programmed setpoints. This can lead to higher energy use compared with those who stay in their pre-programmed schedules. As a case study, we project total hourly loads and generation costs for Texas’ electricity grid in 2050. The increased heating and cooling demand alone could raise annual grid operating costs by 20–25% and require 21,700MW of additional capacity to maintain grid reliability.