  Efficient electric heat pumps that replace fossil-fueled heating systems
could significantly reduce greenhouse gas emissions. However, electric heat
pumps can sharply increase electricity demand, causing high utility bills and
stressing the power grid. Residential neighborhoods could see particularly high
electricity demand during cold weather, when heat demand rises and heat pump
efficiencies fall. This paper presents the development and field demonstration
of a predictive control system for an air-to-air heat pump with backup electric
resistance heat. The control system adjusts indoor temperature set-points based
on weather forecasts, occupancy conditions, and data-driven models of the
building and heating equipment. Field tests from January to March of 2023 in an
occupied, all-electric, 208 m^2 detached single-family house in Indiana, USA,
included outdoor temperatures as low as -15 C. On average over these tests, the
control system reduced daily heating energy use by 19% (95% confidence
interval: 13--24%), energy used for backup heat by 38%, and the frequency of
using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of
residents showed that the control system maintained satisfactory thermal
comfort. The control system could reduce the house's total annual heating costs
by about $300 (95% confidence interval: 23--34%). These real-world results
could strengthen the case for deploying predictive home heating control,
bringing the technology one step closer to reducing emissions, utility bills,
and power grid impacts at scale.
