We define trajectory predictive control (TPC) as a class of indirect data-driven predictive control (DDPC) methods that represent future outputs as linear in past inputs/outputs and future inputs. TPC unifies many DDPC variants with different predictor structures. We introduce a predictor with a state-space representation and show that with it, TPC inherits the mature theory of linear model predictive control. In numerical experiments, the state-space predictor outperforms existing predictors, especially for small training datasets.

Advanced control of heating and cooling systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control (MPC) and reinforcement learning (RL), remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require at most two thermal parameters. In representative heating simulations, controller performance is robust to large parameter specification errors, suggesting potential for deployment with no tuning. The controllers maintain good occupant comfort while achieving 43 to 98

Electric heat-pump water heaters (HPWHs) could reduce the energy costs, emissions, and power grid impacts associated with water heating, the second-largest energy use in United States housing. However, most HPWHs today require 240 V circuits to power the backup resistance heating elements they use to maintain comfort during large water draws. Installing a 240 V circuit can increase the up-front cost of a HPWH by half or more. This paper develops and field-tests the first control system that enables a 120 V HPWH to efficiently maintain comfort without resistance heating elements. The novel model predictive control (MPC) system enables pre-heating in anticipation of large water draws, which it forecasts using an ensemble of machine learning predictors. By shifting electrical load over time, MPC also reduces energy costs on average by 23% and 28% under time-of-use pricing and hourly pricing, respectively, relative to a 240 V HPWH with standard controls. Compared to the increasingly common practice in 120 V HPWHs of storing water at a constant, high temperature (60 degrees C) to ensure comfort, MPC saves 37% energy on average. In addition to demonstrating MPC's benefits in a real, occupied house, this paper discusses implementation challenges and costs. A simple payback analysis suggests that a 120 V HPWH, operated by the MPC system developed here, would be economically attractive in most installation scenarios.

How much energy, money, and emissions can advanced control of heating and cooling equipment save in real buildings? To address this question, researchers sometimes control a small number of thermal zones within a larger multi-zone building, then report savings for the controlled zones only. That approach can overestimate savings by neglecting heat transfer between controlled zones and adjacent zones. This paper mathematically characterizes the overestimation error when the dynamics are linear and the objectives are linear in the thermal load, as usually holds when optimizing energy efficiency, energy costs, or emissions. Overestimation errors can be large even in seemingly innocuous situations. For example, when controlling only interior zones that have no direct thermal contact with the outdoors, all perceived savings are fictitious. This paper provides an alternative estimation method based on the controlled and adjacent zones' temperature measurements. The new method does not require estimating how much energy the building would have used under baseline operations, so it removes the additional measurement and verification challenge of accurate baseline estimation.

Residential buildings are increasingly integrating large devices that run natively on direct current (DC), such as solar photovoltaics, electric vehicles, stationary batteries, and DC motors that drive heat pumps and other major appliances. Today, these natively-DC devices typically connect within buildings through alternating current (AC) distribution systems, entailing significant energy losses due to conversions between AC and DC. This paper investigates the alternative of connecting DC devices through DC distribution. Specifically, this paper shows through laboratory and field experiments that an off-the-shelf residential heat pump designed for conventional AC systems can be powered directly on DC with few hardware modifications and little change in performance. Supporting simulations of a DC nanogrid including historical heat pump and rest-of-house load measurements, a solar photovoltaic array, and a stationary battery suggest that connecting these devices through DC distribution could decrease annual electricity bills by 12.5% with an aftermarket AC-to-DC heat pump retrofit and by 16.7% with a heat pump designed to run on DC. The associated savings in gross nanogrid energy are 8% and 9.2%, respectively.

Supervisory predictive control of heat pumps has gained significant traction as a potential technology that can reduce the cost of electrification and improve operation efficiency. However, most scientific literature on this topic is focused on simulations, with limited experiments. Additionally, heat pumps in cold climates suffer from the frequent need to perform defrost cycles. This is a costly process that requires the use of resistive backup heat to meet the indoor heating load. In a previous demonstration of a smart model predictive control scheme in a single-family home in a cold-climate location, with temperatures as low as −20 °C, it was observed that during relatively similar ambient conditions, a controller performing load regulation by adjusting the indoor setpoint achieved significantly fewer defrost cycles. The hypothesis is that this is an unexpected benefit of load regulation. During high frost times, adjusting the indoor setpoint would lower the heating load, reduce the refrigerant flow rate, and result in the evaporator operating at a higher temperature with respect to the no-smart control baseline. Consequently, this reduces moisture transfer and, therefore, lowers frost build-up. The hypothesis is validated through a detailed analysis of on-site data from the test unit. This highlights the important potential of night-time setbacks and early morning load regulation to significantly reduce frost growth and use backup heat during defrost cycles.

Advanced control strategies like Model Predictive Control (MPC) offer significant energy savings for HVAC systems but often require substantial engineering effort, limiting scalability. Reinforcement Learning (RL) promises greater automation and adaptability, yet its practical application in real-world residential settings remains largely undemonstrated, facing challenges related to safety, interpretability, and sample efficiency. To investigate these practical issues, we performed a direct comparison of an MPC and a model-based RL controller, with each controller deployed for a one-month period in an occupied house with a heat pump system in West Lafayette, Indiana. This investigation aimed to explore scalability of the chosen RL and MPC implementations while ensuring safety and comparability. The advanced controllers were evaluated against each other and against the existing controller. RL achieved substantial energy savings (22\% relative to the existing controller), slightly exceeding MPC's savings (20\%), albeit with modestly higher occupant discomfort. However, when energy savings were normalized for the level of comfort provided, MPC demonstrated superior performance. This study's empirical results show that while RL reduces engineering overhead, it introduces practical trade-offs in model accuracy and operational robustness. The key lessons learned concern the difficulties of safe controller initialization, navigating the mismatch between control actions and their practical implementation, and maintaining the integrity of online learning in a live environment. These insights pinpoint the essential research directions needed to advance RL from a promising concept to a truly scalable HVAC control solution.

As the United States transitions to greener power generation, the ability for the grid to handle complex daily demand profiles is becoming an increasingly hard problem to solve. Solar and wind power is intermittent, and often greatly out of sync with demand curves. Demand for electrified services (space heating/cooling, and electric vehicles) is also increasing, which the distribution grid is currently not able to support. Energy storage can help to smooth out these demand curves, especially for residential building energy systems with a high penetration of renewable energy generation. Being able to self-consume or store renewable energy generated can therefore help aid grid decarbonization. Typically, most energy storage applications for residential use are electrical. However, there is still an opportunity for the integration of Thermal Energy Storage (TES) since it can directly supply heating or cooling without an electrical conversion. However, TES hasn't achieved practicality for residential use from a techno-economic perspective since the return on investment is often noted as anywhere from 10-30 years depending on the technology chosen. This paper will present a convex model of a fully electrified residential building, the DC Nanogrid House, with PV production and electric storage to optimally size a TES-integrated HP system. Simulation results from several climate zones across the US are presented. A techno-economic assessment of multiple different TES technologies is performed to help unlock the design space of residential TES systems, to highlight their contribution in net zero energy operation, and to understand the context in which they are a viable option. This study concludes that a key benefit to TES utilization in residential buildings is grid flexibility, allowing for more complex day-ahead grid signals to be used by space conditioning systems.

Electrification of appliances, water heating, and space conditioning in homes can significantly reduce emissions and is crucial for achieving decarbonization goals [1, 2]. However, adding more electrical devices increases demand and requires major equipment upgrades on both the distribution and the residential side. One key upgrade is the electrical panel. Raising the panel rating can cost thousands for homeowners and millions for distribution providers, posing an obstacle to rapid electrification. This upgrade challenge impedes fast progress toward widespread electrification. In previous work, we introduced a novel multi-layered control strategy that coordinates heat pump and water heater set-points to keep a building's net current draw below a safety threshold. Although the results were promising, that study was limited to a single site with specific envelope characteristics and appliance ratings. In this work, a multi-layer RC-based emulator is designed that can be applied at scale to model the feasibility of current limiting control. Preliminary simulations incorporating a heat pump, an electric charger, and an electric water heater for a validated site highlight that in low ambient conditions, satisfactory electric vehicle charging might not be feasible without the risk of the breaker panel tripping unless the heat pump schedule is altered to accommodate the EV charging schedule.

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.

Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, electrification can also raise electricity demand beyond the safe limits of electrical infrastructure. This can increase the risk of blackouts or may require grid reinforcement that is often 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 states of the United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to five times higher than today's peaks. Accommodating electrification of all housing and personal vehicles is estimated to require 600 GW of distribution grid reinforcement nationally, at a cost of $350-$790 billion, or $2,800-$6,400 per household (95% confidence intervals). However, demand-side management could eliminate over two-thirds of grid reinforcement costs.

A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information – such as experiment scopes, locations, and durations – this paper analyzes experiment protocols and their influence on performance estimates. We find that 71

Dynamic tariff adoption is considered to be an important driver of demand response, enabling more sustainable and reliable power systems. However, first studies have shown that a high share of households subscribing to dynamic tariffs can lead to so-called "avalanche effects" on the distribution grid level, wherein load profiles align across households. Avalanche effects can create new demand peaks that necessitate costly grid reinforcement measures. Here, we analyze the impacts of policy options for grid charge and solar photovoltaic (PV) feed-in remuneration on grid reinforcement costs given increasing shares of dynamic tariff adoption. The analysis framework is open-source and uses empirical data from real households. We find that the widely proliferated regulatory scenario with volumetric grid charges and PV feed-in-tariffs leads to heavy reinforcement needs. We show that novel policy options, such as rotating or segmented grid charges, can alleviate grid reinforcement needs.

This paper investigates, for the first-time in the research literature, how the operational characteristics of air-source heat pumps in cold climates influence occupants’ thermostat adjustment behaviors. It presents a field study that monitors disaggregated energy use and occupant thermostat interactions under different heating system operational scenarios in 21 nearly identical single-family homes within a newly constructed residential community. Despite typical heterogeneous occupant behavior that exists in residential buildings, the results from our field study showed that in 62 % of the homes, the occupants selected lower temperature setpoints when the auxiliary heater was the primary source compared to the baseline heat pump priority mode that had higher supply air temperatures. Subsequently, to further investigate differences in setpoint preferences and the motivations behind setpoint adjustments, controlled laboratory experiments were conducted with 32 participants. The experiments emulated the operational characteristics of a single-stage heat pump with auxiliary heating observed in the field, and included a variable-speed heat pump test case with enhanced comfort as a baseline for comparison. According to the results, 19 out of 32 participants increased their setpoints even though the emulated single-stage heat pump had sufficient capacity to warm the indoor space. Cold air movement and indoor temperature fluctuations due to the heat pump cycling on/off were the main reasons participants reported increasing their setpoints. The laboratory study documented that these triggers can be mitigated by using variable-speed equipment, which provides better indoor temperature control in cold conditions.

Heat pump water heaters offer a promising way to reduce energy costs and greenhouse gas emissions by delivering more heat per unit of electricity than traditional water heaters. However, the high installation costs of hybrid heat pump water heaters have limited their adoption. This is largely due to their reliance on electric resistance backup elements, which may necessitate costly electrical upgrades if no 240 V outlet is available. Heat pump-only water heaters that plug into 120 V outlets reduce installation costs but face challenges in maintaining comfortable water temperatures, particularly in cold weather, due to the limited heat transfer rate of the heat pump. To ensure comfort, the current practice is to store water at higher temperatures, resulting in increased conductive tank losses and reduced heat pump cycle efficiencies. In this paper, we present a novel model predictive control architecture that requires only a single additional temperature sensor on the water inlet line. Our approach estimates water use from temperature changes and forecasts demand with a hybrid machine learning model. A field test in a cold climate demonstrated that our controller effectively pre-heats before water draws to maintain comfort and improve efficiency. This scalable, low-cost solution could accelerate the adoption of energy-efficient water heating by reducing installation barriers while contributing to lower peak electricity demand and greater demand flexibility.

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 m2 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.

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 versions can significantly reduce emissions. However, electric heating and vehicle charging can cause peaks in electricity demand that stress infrastructure in buildings and power grids, jeopardizing reliability or forcing costly infrastructure upgrades. This paper presents the open-source EDGIE (Emulating the Distribution Grid Impacts of Electrification) toolbox. EDGIE matches experiment data from an all-electric home in cold weather. It can simulate many locations and levels of technology adoption, and supports optimization and network power flow simulation. In simulations of a fully electrified neighborhood during the coldest week of 2019 in New York City, demand peaks at quadruple today's summer peak. Peaks are particularly sensitive to the use of overnight thermostat set-point reductions and to the efficiencies of heat pumps and building envelopes. Optimal vehicle-to-home coordination with flexible space and water heating reduces peak demand by 35% and transformer degradation by 99%.