Kevin J. Kircher’s research while affiliated with Purdue University West Lafayette and other places

Over the last two decades, research and development efforts have shown that advanced control of heating, ventilation, and air conditioning (HVAC) equipment in commercial buildings can improve energy efficiency, reduce emissions, and turn buildings into active participants in the power grid. Despite these efforts, advanced commercial HVAC control has not yet seen widespread adoption. In this paper, we argue that the research community can help companies deploy advanced HVAC control at speed and scale by reorienting research efforts toward clearly demonstrating the business case for adoption. To support this argument, we draw on findings from the 2023 Intelligent Building Operations Workshop, which brought together researchers, entrepreneurs, and representatives from industry and government to discuss current business offerings, state-of-the-art field demonstrations, barriers to adoption, and future directions.

Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can raise electricity demand beyond the safe limits of electrical infrastructure, increasing the risk of blackouts or requiring grid reinforcement that can be slow and expensive. Here, we estimate the physical and economic impacts on distribution grids of electrifying all housing and personal vehicles in each county of the lower 48 United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to three times higher than today's peaks. Accommodating electrification of all housing and personal vehicles could require up to 312 GW of distribution grid reinforcement nationally, at a cost of $183 to$415 billion, or $1,500 to$3,400 per household (95% confidence intervals). However, demand-side management can mitigate demand peaks, reducing grid reinforcement costs by up to 92%.

The advent of smart connected thermostats and their capability of data collection have spurred many studies exploring how occupants adjust thermostat setpoints to achieve comfort, along with the various factors influencing their setpoint preferences. The objective of this study is to investigate thermostat-adjustment behavioral patterns in households with single-stage heat pumps coupled with backup heaters and smart thermostats. A field study was carried out in 30 houses within a newly constructed residential community. The experiments consist of two parts: 1) a baseline mode featuring a heat pump paired with an auxiliary heater controlled by default thermostat heuristic rules, and 2) a comparison mode where the auxiliary heater is activated to provide the majority of heating. The findings from the field study suggest that several occupants exhibit lower setpoint preferences during the winter season for the comparison mode that has higher supply air temperatures. Furthermore, four distinct setpoint-increasing behaviors are identified, contributing to the setpoint differences between the two modes. Among these, the behavior associated with staging the auxiliary heater and lower heat pump capacity during cold weather conditions is the primary difference between the two operation modes. A noticeable decrease in this behavior is observed in the comparison mode.

Residential electrification - replacing fossil-fueled appliances and vehicles with electric machines - can significantly reduce greenhouse gas emissions and air pollution. However, installing electric appliances or vehicle charging in a residential building can sharply increase its current draws. In older housing, high current draws can jeopardize electrical infrastructure, such as circuit breaker panels or electrical service (the wires that connect a building to the distribution grid). Upgrading electrical infrastructure can entail long delays and high costs, so poses a significant barrier to electrification. This paper develops and field-tests a control system that avoids the need for electrical upgrades by keeping an electrified home's total current draw within the safe limits of its panel and service. In the proposed control architecture, a high-level controller plans device set-points over a rolling prediction horizon. A low-level controller monitors real-time conditions and ramps down devices if necessary. The control system was tested in an occupied, electrified single-family house with code-minimum insulation, an air-to-air heat pump and backup resistance heat, a resistance water heater, and a plug-in hybrid electric vehicle with Level I charging. The field tests spanned 31 winter days with outdoor temperatures as low as -20 C. 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 with Level II 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.

Heat pump water heaters (HPWHs) could significantly reduce energy costs and greenhouse gas emissions from water heating, the second largest energy use in residential buildings. Today, most HPWHs use electric resistance heating elements to maintain comfortable water temperatures even during large water draws. Unfortunately, heating elements significantly decrease energy efficiency, and their current and voltage requirements may necessitate costly electrical work in older homes. This paper develops and field-tests a model predictive control (MPC) system that enables a HPWH with no heating elements to maintain comfort at high efficiency. By contrast to most prior experimental studies on water heater MPC, which often use perfectly-forecasted water draws in controlled laboratory settings, this paper reports field tests from a real home with three full-time occupants. The occupants' water draws are forecasted using a machine learning model and a scalable training methodology. This paper also presents occupant feedback on thermal comfort, as well as an Internet of Things infrastructure that enables real-time data acquisition and control. In the MPC formulation, the energy savings were 11% with the same thermal comfort as the manufacturer's constant set-point control. An adjusted MPC formulation substantially improved thermal comfort while modestly increasing energy costs.

Many simulation studies have suggested that advanced control strategies for heating, ventilation, and air-conditioning (HVAC) equipment in commercial buildings can help reduce energy costs and greenhouse gas emissions. However, despite these potential benefits, adoption of advanced HVAC control remains limited, due in part to a lack of confidence in the technology among decision-makers in business and government. Field demonstrations of advanced HVAC control help build confidence in the technology by demonstrating its effectiveness and economic value in the real world. This paper accordingly reviews field demonstrations of advanced commercial HVAC control strategies, such as Model Predictive Control (MPC) and Reinforcement Learning Control (RLC). This paper discusses building types, control methods, test durations, measurement and verification procedures, control objectives, and reported benefits. It further provides a critical assessment of the state of the technology and highlights research opportunities that could accelerate real-world adoption of advanced commercial HVAC control strategies. The literature review confirms that advanced HVAC controls can significantly enhance energy efficiency and occupant comfort. However, most field studies cover relatively short durations and control small spaces within larger buildings. Longer-duration studies frequently report lower savings, suggesting that short-duration studies may overestimate potential benefits. Similarly, whole-building control studies typically report lower savings than smaller-scale studies, likely because the latter tend to overlook thermal coupling between controlled zones and adjacent zones. Finally, data and discussions concerning deployment costs and challenges are almost nonexistent. This suggests an important area for future research, as achieving adoption at scale will require demonstrating not only reliable benefits but also manageable deployment costs.

The rapid electrification of residential buildings has created rising concerns about the ability of the power grid to deal with seasonal electric space conditioning peaks. Better control of home heating and cooling systems can help solve this problem. However, unlike other control areas where extensive data are often available, each home presents a unique plant for which data are limited. This complicates the development of advanced control systems. This paper presents an open-source toolbox that researchers and industry can use as a plug-and-play tool for identifying the thermal dynamics of detached single-family homes. The tool supports identification of 3R2C and 2R1C thermal circuit models and can be trained using either thermal or electrical data alongside temperature measurements. The 2R1C method uses a computationally inexpensive constrained least squares formulation, while the 3R2C used a meta-heuristic genetic algorithm. The thermal mass temperature is treated as a hidden state with a Kalman Filter in place. The system is tested on a real-world building for prediction accuracy of indoor temperature and heat supply. In these tests, the root-mean-square hour-ahead temperature prediction errors are 0.8 ℃ and 0.57 ℃ for the 2R1C and 3R2C models, respectively. Under highly transient indoor conditions, such as fast indoor temperature set-point changes, the 3R2C performs better, while under more steady conditions the two model structures perform similarly. 1. DIFFERENT METHODS OF MODEL IDENTIFICATION FOR BUILDINGS The residential sector is responsible for greater than 20 % of U.S. CO2 emissions, 20 % of the energy consumption, and 40 % of total electricity use, in 2020 alone, residential buildings in the U.S. consumed nearly 21 quadrillion BTUs of primary energy, of which space conditioning (heating and cooling) and water heating accounted for approximately 56% (EIA 2021). Increased efforts to promote electrification create tremendous opportunities for efficiency improvements while also great challenges for grid reliability and robustness (González-Torres et al. 2022). Optimal controls can significantly aid in this effort, however, the paradigm of the simple P, PI, or on-off thermostat has not changed in almost 60 years. As such, simulation based optimal controls have become a trendy research field, with countless simulation-based optimal controls of building studies (MPC, or otherwise) being written every year (Drgona et al. 2020, Zhang et al. 2022). One could categorize optimal studies based on the model used, whether black-box, grey-box, or white-box. White-box models usually involve creating an EnergyPlus model of the building at hand, after which, some model reduction technique can be used to generate a lower-order RC model. Similarly, most often than not, grey-box or black-box based models also involve the incorporation of some white-box model, either for the generation of training sets (black-box) or to model internal and solar gains (grey-box) (Khabbazi et al. 2024). Although these methods are acceptable for commercial buildings, where information on material properties and detailed engineering schematics (floor plan, zoning, etc.) are readily available, for residential buildings, they can result in poor performance (Dong and Lam 2013, Kim et al. 2022). Therefore, for the optimal control of residential building methods that can obtain a model from limited data are to be preferred. Black-box approaches face a significant limitation in this front due to low data availability as well as white-box models due to their need for extensive building specific information. In this work, a novel method is presented whereupon low-order building grey-box models can be trained using limited sensor data by combining parameter estimation methods (regression, multiparameter optimization, GA) with the thermal powers arising from unknown thermal gains from occupants, devices, envelope non-linearities, heat pump non-linearities, etc. being obtained from a Support Vector Machine (SVM). Although the potential of grey-box methods for training with smaller datasets is widely known, limited open-source resources exist for training RC models