FIELD DEMONSTRATION OF PREDICTIVE HOME ENERGY MANAGEMENT

Supervisory predictive control of residential building heating, ventilation, and air conditioning (HVAC) systems could protect electrical infrastructure, enhance occupants’ thermal comfort, reduce energy costs, and minimize emissions. However, there are few experimental demonstrations, with most of the work focusing on simulation studies. To convince stakeholders of the benefits of supervisory predictive controls for residential HVAC systems, it is important to demonstrate practical systems in real buildings. Practical demonstrations also further our understanding of the field performance of these systems. This thesis presents the first comprehensive review of supervisory predictive control experiments in residential buildings, drawing critical insights on the estimated energy savings, the types of equipment controlled, the objectives and problem formulations considered, and other practical considerations. To address limitations in the existing body of experimental work, a series of field demonstrations were performed in a real house with student occupants near the Purdue campus in West Lafayette, Indiana, U.S.A.

The first field demonstration involved supervisory predictive control of an air-to-air heat pump with backup electric resistance heat. This was the first experiment to consider this equipment configuration, which is common in North America. A simple data-driven method is presented for learning a model of the temperature dynamics of a detached residential building. Using this model, the control system adjusts indoor temperature set points based on weather forecasts, occupancy conditions, and data-driven models of the heating equipment. Field tests from January to March of 2023 included outdoor temperatures as low as −15 ℃. During these tests, the control system reduced total heating energy costs by 19% on average (95% confidence interval: 13–24%) and energy used for backup heat by 38%. The control system also reduced the frequency of using high-stage (19 kW) backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. 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.

The second field demonstration advanced the state of the art of predictive residential cooling control, wherein past experimental demonstrations relied on “sensible” models of building thermal dynamics and neglected humidity effects. In this thesis, a model-free machine learning method is introduced to predict the indoor wet-bulb temperature and the sensible heat ratio in a “latent” model formulation, with the aim to increase the accuracy of the real electrical power prediction. The latent and sensible formulations are tested in two separate model predictive controller (MPC) schemes in an on-off fashion. One MPCscheme aims to reduce energy costs while enhancing comfort. The other is a power-limiting controller that aims to keep the power of the HVAC equipment below 2.5 kW between 4 PM and 8 PM. The two MPC schemes and the two load models are assessed through 38 days of testing. It is found that across both economic MPC and power-limiting MPC, the energy savings across the latent and sensible formulations are similar. Through a normalized Cooling Degrees Days analysis, the energy savings to the baseline controller in the house are found to be 16 to 32% for economic MPC (95% confidence interval) and -5 to 10% for power-limiting MPC, with 7 to 21% savings across both controllers (14% mean). For power limiting, the latent formulation reduced the total duration of constraint violation by 88% and the sensible formulation by 40%, with respect to the non-MPC baseline. Additionally, the latent formulation reduced the peak power demand by 13% relative to the baseline, a behavior not observed in the sensible formulation.

The third field experiment investigated the problem of protecting home electrical infrastructure in the context of electrification retrofits. Installing electric appliances or vehicle charging in a residential building can sharply increase the electric current draws. In older housing, high current draws can jeopardize circuit breaker panels or electrical service (the wires that connect a building to the distribution grid). Upgrading electrical panels or service often entails long delays and high costs, and thus it poses a significant barrier to electrification. This thesis develops and field tests a novel control system that avoids the need for electrical upgrades by maintaining an electrified home’s total current draw within the safe limits of its existing panel and service. In the proposed control architecture, a high-level controller plans device set-points over a rolling prediction horizon, while a low-level controller monitors real-time conditions and ramps down devices if necessary. The control system was tested for 31 consecutive winter days with outdoor temperatures as low as -20 ℃. The control system maintained the whole-home current within the safe limits of electrical panels and service rated at 100 A, a common rating for older houses in North America, by adjusting only the temperature set-points of the heat pump and water heater. Simulations suggest that the same 100 A limit could accommodate a second electric vehicle (EV) with Level II (11.5 kW) charging. The proposed control system could allow older homes to safely electrify without upgrading electrical panels or service, saving a typical household on the order of $2,000 to $10,000.

These three field experiments demonstrate that low-cost predictive control systems can serve multiple objectives, improving the efficiency of heat pumps and water heaters while maintaining comfort and protecting electrical infrastructure. Future work will be directed toward improving the scalability of these proposed controllers through the incorporation of data-driven methodologies such as data-enabled predictive control, as well as understanding the application of these algorithms with different systems, including batteries, on-site solar photovoltaics, and electrical vehicle charging.