<p dir="ltr">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.</p><p dir="ltr">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.</p><p dir="ltr">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.</p><p dir="ltr">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. </p><p dir="ltr">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.</p>

<p>With rising temperatures, urbanization, population growth, improving economic wellbeing, decarbonization and electrification efforts, the demand for space cooling and heating equipment is continuously increasing around the world. To counteract the effect of rising demand for air conditioners and heat pumps on total energy consumption, peak electricity demand, and emissions, it is crucial to promote the development and market penetration of energy-efficient systems. Establishing minimum energy performance standards (MEPS), energy labeling and utility programs are some of the effective and tested methods for achieving this goal. The technical basis for these energy efficiency standards is a testing and rating procedure for estimating equipment seasonal performance from laboratory tests. Although the current rating procedures provide standardized metrics to compare different equipment performances, they fail to appropriately characterize the field representative performance of systems by not considering the effects of: 1) test unit embedded controls, thermostat, and realistic interactions with the building load and dynamics; 2) different climate zones and building types; and 3) and other integrated accessories for improving energy efficiency such as economizer for rooftop units (RTUs). Therefore, current approaches for performance ratings neither incentives the development and implementation of improved system and control designs nor consumers with a metric that represents the advanced systems' actual energy savings. To address this, a load-based testing methodology that enables dynamic performance evaluation of equipment with its integrated controls, thermostat, and other accessories was recently proposed. The test methodology is based on the concept of emulating the response of a representative building conditioned by the test unit in a test lab using a virtual building model. </p> <p>In this work, the proposed load-based testing methodology was further extended, evaluated, and validated for residential heat pumps to integrate it into next-generation energy efficiency testing and rating procedures and to serve as a tool for engineers to develop and validate improved control algorithms in a laboratory setting. Further, a load-based testing method for evaluating the dynamic performance of RTUs with integrated economizers was also developed and demonstrated.</p> <p>A load-based testing approach previously developed for residential cooling equipment is extended for heat pump heating-mode and demonstrated for a variable-speed system. The heat pump's typical dynamic behaviors are captured along with controller imperfections that aren't reflected in current testing approaches. Further, a comprehensive comparison was performed between the proposed load-based testing approach to the current steady-state testing approach in the U.S., AHRI 210/240, based on performance evaluation of three residential variable-speed heat pumps to understand the differences and their significance for the next-generation rating procedure. For cooling mode, steady-state testing estimates higher seasonal performance, but for heating mode, the steady-state testing approach estimates higher seasonal performance for warmer climates and is comparable for colder climates. The load-based testing methodology was validated by comparing the laboratory performance of a heat pump to that of a residential building in a controlled environment. The virtual building modeling approach for building loads and thermal dynamics effectively captured these characteristics of the house. The heat pump's cycling rate response with run-time fraction, which represents the unit's overall dynamic response, matched well between lab load-based tests and house tests. The test unit's COP difference for cooling and heating tests was within 3% between the two facilities, except for 9% in 95°F and 6% in 104°F cooling dry-coil test intervals. To evaluate the applicability of the developed load-based testing methodology as next-generation rating standards, its repeatability and reproducibility were assessed based on multiple heat pump round-robin tests conducted in two labs. Overall, reasonable to good repeatability was observed in load-based test results in both labs, however, poor reproducibility was observed except for one heat pump heating mode results. A root cause analysis of the observed differences along with recommendations for a next-generation rating approach are presented. This work aided in the development of a CSA (Canadian Standards Association) standard EXP07:19 and its subsequent revision for equipment rating based on load-based testing.</p> <p>The application of the load-based testing methodology as a tool for the development and evaluation of a residential heat pump controller design was demonstrated. Further, a load-based testing methodology was developed and demonstrated for the dynamic performance evaluation of RTUs with integrated economizers in a test laboratory setting. Recommendations for future work to further develop and improve the repeatability, reproducibility, and representativeness of the load-based testing and rating approach for residential and commercial air conditioners and heat pumps are summarized at the end of the dissertation. </p>