Field demonstration of predictive heating control for an all-electric house in a cold climate

TL;DR

This paper demonstrates a predictive heating control system for an electric house in a cold climate, reducing energy use, costs, and grid stress while maintaining comfort through real-world field tests.

Contribution

It introduces and validates a novel predictive control system for electric heat pumps, optimizing energy use and costs in cold climates based on weather and occupancy data.

Findings

19% reduction in daily heating energy use

38% decrease in backup heat energy

83% less use of highest backup heat stage

Abstract

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.