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

TL;DR

This work addresses the challenge of reducing electricity peaks and costs from residential heating using predictive heating control in a real all-electric, cold-climate home. It develops a supervisory MPC framework that optimizes indoor set-points and heater usage by integrating a convexified heating-power model, a data-driven exogenous-thermal-power predictor, and weather-aware COPs, guided by forecasts and occupancy signals. In a 59-day field test, the approach achieves an average daily heating-energy reduction of 19% (95% CI: 13–24%), 38% less energy for backup heat, and an 83% reduction in the highest-stage 19 kW backup events, while maintaining occupant comfort (mean PPD ≈ 10%). Seasonal analysis suggests winter heating-cost savings of roughly $291 (CI $239–$363), implying substantial potential for emissions reductions, bill savings, and grid impact mitigation at scale if deployed broadly with appropriate deployment strategies and costs considerations.

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.

Figures (14)

• Figure 1: A heat pump control architecture. This paper develops the supervisory control system (red block).
• Figure 2: The DC Nanogrid House is a 208 m$^2$, 1920s-era house with all-electric appliances in West Lafayette, Indiana, USA.
• Figure 3: The DC Nanogrid House's HVAC equipment in heating mode. The colors indicate cold, cool, warm, and hot.
• Figure 4: Information flow in the control system.
• Figure 5: Thermal circuit model of the DC Nanogrid House.