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DOE OSTI2020

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL’s end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordi- nate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs’ local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.

demand response↗

DOE OSTI2024

Continuous thermostat setpoint monitoring and correction (Thermostat setpoint correction) v1.0

The Continuous Thermostat Setpoint Monitoring and Correction software is a set of fault detection and correction algorithms that can be implemented in thermostats with two-way OpenAPIs. It is written in the Python language. The algorithms aim to detect the most common and impactful efficiency problems associated with thermostat setpoints - overly aggressive heating or cooling setpoints, incorrect schedules/setbacks, and overly narrow deadbands. These algorithms can automatically detect faults, and implement associated corrective actions to bring the system back to a state of efficient operation. The algorithms can run remotely in the cloud, and directly implemented by connected thermostat manufacturers, or by third party service providers. The software enables a lightweight cost-effective energy management strategy for HVAC systems. The solution is specially viable for small and medium sized commercial buildings, where a full scale building automation system and fault detection and diagnostic tools are often unavailable.

Granderson, Jessica↗

DOE OSTI2015

A new thermostat for real-time price demand response: Cost, comfort and energy impacts of discrete-time control without deadband

Not Available

Chassin, David P.↗

DOE OSTI2024

Individual Data Sparsity in Smart Thermostat Big Data: Impacts on Modeling Thermostat Use Behavior Dynamics

This study explores the impacts of the sparsity of individual thermostat interaction data on modeling thermostat use behavior dynamics using a dataset of over 100,000 smart thermostats. In developing a data-driven model of Thermal Frustration Theory (TFT), we investigate the challenges and trade-offs in clustering occupant data to enhance predictive accuracy. Our findings reveal that a single, aggregated model fails to capture the diversity of occupant behaviors, resulting in extremely poor prediction performance. Conversely, excessive clustering exacerbates data sparsity, undermining model reliability. By identifying an optimal clustering strategy, we achieve a balance that significantly improves the prediction of manual setpoint changes during demand response (DR) events, enhancing energy management and occupant comfort

Fannon, David↗

DOE OSTI2023

The Uniform Methods Project: Smart Thermostat Evaluation Protocol

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2012

Field Evaluation of Programmable Thermostats

Prior research suggests that poor programmable thermostats usability may prevent their effective use to save energy. We hypothesized that home occupants with a high-usability thermostats would be more likely to use them to save energy than people with a basic thermostat. We randomly installed a high-usability thermostat in half the 77 apartments of an affordable housing complex, installing a basic thermostat in the other half. During the heating season, we collected space temperature and furnace on-off data to evaluate occupant interaction with the thermostats, foremost nighttime setbacks. We found that thermostat usability did not influence energy-saving behaviors, finding no significant difference in temperature maintained among apartments with high- and low-usability thermostats.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2011

Facilitating Energy Savings through Enhanced Usability of Thermostats

Residential thermostats play a key role in controlling heating and cooling systems. Occupants often find the controls of programmable thermostats confusing, sometimes leading to higher heating consumption than when the buildings are controlled manually. A high degree of usability is vital to a programmable thermostat's effectiveness because, unlike a more efficient heating system, occupants must engage in specific actions after installation to obtain energy savings. We developed a procedure for measuring the usability of thermostats and tested this methodology with 31 subjects on five thermostats. The procedure requires first identifying representative tasks associated with the device and then testing the subjects ability to accomplish those tasks. The procedure was able to demonstrate the subjects wide ability to accomplish tasks and the influence of a device's usability on success rates. A metric based on the time to accomplish the tasks and the fraction of subjects actually completing the tasks captured the key aspects of each thermostat's usability. The procedure was recently adopted by the Energy Star Program for its thermostat specification. The approach appears suitable for quantifying usability of controls in other products, such as heat pump water heaters and commercial lighting.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Connected Thermostat Alternatives for Room Air Conditioners and Minisplit Heat Pumps

The availability of smart, connected thermostats has improved climate control, energy efficiency, and grid demand-response programs for central HVAC systems. However, a significant gap exists in addressing integrated control systems for point-source heating and cooling systems such as window air-conditioners (window ACs) and mini-split heat pumps (MSHPs). This report examines the emerging market of third-party connected thermostats tailored for these systems, focusing on their effectiveness, reliability, and potential barriers to adoption.This study evaluates several commercially available products designed for room ACs and MSHPs through a series of laboratory tests. While these infrared-based (IR-based) thermostats offer remote temperature control and scheduling via mobile apps, our findings reveal that none are seamless, with reliability of basic functions being a critical factor. Promising features include integration of indoor air quality metrics and time-of-use pricing, but the latter are not yet available in the U.S. Barriers to broad user acceptance include non-seamless setup processes, challenges in thermostat placement, and unclear product differentiation. There is a pressing need for research and development in enabling MSHPs and central thermostats to coordinate, enhancing energy savings and comfort in retrofit applications. This study underscores the importance of further innovation in connected thermostat technology to address the diverse needs of single-zone HVAC systems and promote efficient energy management in households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

A new and effective method for thermostatting confined fluids

We present a simple thermostatting method suitable for nanoconfined fluid systems. Two conventional strategies involve thermostatting the fluid directly or employing a thermal wall that couples only the wall atoms with the thermostat. When only a thermal wall is implemented, the temperature control of the fluid is true to the actual experiment and the heat is transferred from the fluid to the walls. However, for large or complex systems it can often be computationally prohibitive to employ thermal walls. To overcome this limitation many researchers choose to freeze wall atoms and instead apply a synthetic thermostat to the fluid directly through the equations of motion. This, however, can have serious consequences for the mechanical, thermodynamic, and dynamical properties of the fluid by introducing unphysical behaviour into the system [Bernardi et al., J. Chem. Phys. 132, 244706 (2010)]. In this paper, we propose a simple scheme which enables working with both frozen walls and naturally thermostatted liquids. This is done by superimposing the walls with oscillating particles, which vibrate on the edge of the fluid control volume. These particles exchange energy with the fluid molecules, but do not interact with wall atoms or each other, thus behaving as virtual particles. Their displacements violate the Lindemann criterion for melting, in such a way that the net effect would not amount to an additional confining surface. One advantage over standard techniques is the reduced computational cost, particularly for large walls, since they can be kept rigid. Another advantage over accepted strategies is the opportunity to freeze complex charged walls such as β-cristobalite. The method furthermore overcomes the problem with polar fluids such as water, as thermalized charged surfaces require higher spring constants to preserve structural stability, due to the effects of strong Coulomb interactions, thus inevitably degrading the thermostatting efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Energy, Carbon-emission and Financial Savings from Thermostat Control

Among the easiest approaches to energy, and cost, savings for most people is the adjustment of thermostats to save energy. Here we estimate savings of energy, carbon, and money in the United States of America (USA) that would result from adjusting thermostats in residential and commercial buildings by about half a degree Celsius downward during the heating season and upward during the cooling season. To obtain as small a unit as possible, and therefore the least likely to be noticeable by most people, we selected an adjustment of one degree Fahrenheit (0.56 degree Celsius) which is the gradation used almost exclusively on thermostats in the USA and is the smallest unit of temperature that has been used historically. Heating and/or cooling of interior building space for personal comfort is sometimes referred to as space conditioning, a term we will use for convenience throughout this work without consideration of humidity. Thermostat adjustment, as we use the term here, applies to thermostats that control the indoor temperature, and not to other thermostats such as those on water heaters. We track emissions of carbon only, rather than of carbon dioxide, because carbon atoms change atomic partners as they move through the carbon cycle, from atmosphere to biosphere or ocean and, on longer time scales, through the rock cycle. To convert a mass of carbon to an equivalent mass of carbon dioxide (thereby including the mass of the 2 oxygen atoms in each molecule) simply multiply by 3.67.

Carbon Emissions↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗

DOE OSTI2012

Field Evaluation of Programmable Thermostats

Prior research suggests that poor programmable thermostats usability may prevent their effective use to save energy. The Fraunhofer team hypothesized that home occupants with high-usability thermostats would be more likely to use them to save energy than people with a basic thermostats. In this report, the team discusses results of a project in which the team monitored and compared programmable thermostats with basic thermostats in an affordable housing apartment complex.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Long-term trends in connected thermostat performance

Internet-connected thermostats (CTs) control heating and cooling systems in about 30% of North American homes, and capture half of annual thermostat sales. In 2017 the U.S. Environmental Protection Agency created a program to certify the performance of ENERGY STAR® Connected Thermostats. To demonstrate compliance with energy-saving criteria, thermostat vendors must submit performance summaries for a representative sample of up to 1250 homes. Vendors must then re-submit results from a new, representative sample every six months in order to maintain their ENERGY STAR certification. This procedure has created a unique record of each thermostat’s long-term performance in response to changes in weather, customer demographics, building stock, and algorithms. Our analysis covers submissions from 13 different vendors, who submitted data up to 7 times over four years. We found that these semi-annual samples generated relatively stable trends for comfort temperatures and Heating, Ventilating and Air Conditioning (HVAC) runtimes over the study period. However, some vendors achieved consistently more energy-conserving comfort temperatures and shorter HVAC runtimes than others. The most recent submission runtimes averaged 700 hours for heating and 1,300 hours for cooling, but some vendors achieved runtimes as much as 17% below the mean. This implies lower energy consumption is due in large part to more successful algorithms and control strategies used by those vendors.

Meier, Alan↗

DOE OSTI2025

Evaluating Thermostats’ Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Thermostats play a crucial role in energy consumption, user control, and occupant comfort by serving as the interface between the heating, ventilation, and air conditioning (HVAC) system and the building's occupants. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on or off. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different thermostats, tested with a residential heat pump, aiming to identify variations in their deadband settings and implications for energy usage and management. The experimental study was conducted using a HVAC hardware-in-the-loop (HIL) system that allows thermostats to be connected with physical HVAC equipment, driven by conditions in a simulated residential building. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats cycle differently based on their deadband settings. The findings offer valuable insights into how thermostat deadband affects comfort and energy use, as well as the cycling frequency of a heat pump.

Ramaraj, Sugirdhalakshmi [National Renewable Energ↗

DOE OSTI2011

Modified Nose-Hoover thermostat for solid state for constant temperature molecular dynamics simulation

Nose-Hoover (NH) thermostat methods incorporated with molecular dynamics (MD) simulation have been widely used to simulate the instantaneous system temperature and feedback energy in a canonical ensemble. The method simply relates the kinetic energy to the system temperature via the particles' momenta based on the ideal gas law. However, when used in a tightly bound system such as solids, the method may suffer from deriving a lower system temperature and potentially inducing early breaking of atomic bonds at relatively high temperature due to the neglect of the effect of the potential energy of atoms based on solid state physics. In this paper, a modified NH thermostat method is proposed for solid system. The method takes into account the contribution of phonons by virtue of the vibrational energy of lattice and the zero-point energy, derived based on the Debye theory. Proof of the equivalence of the method and the canonical ensemble is first made. The modified NH thermostat is tested on different gold nanocrystals to characterize their melting point and constant volume specific heat, and also their size and temperature dependence. Results show that the modified NH method can give much more comparable results to both the literature experimental and theoretical data than the standard NH. Most importantly, the present model is the only one, among the six thermostat algorithms under comparison, that can accurately reproduce the experimental data and also the T{sup 3}-law at temperature below the Debye temperature, where the specific heat of a solid at constant volume is proportional to the cube of temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters: Theory and application to microfilaments

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗