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At least 73 records · Page 4

Dialogue Between Lighting and HVAC Systems: Improving building system integration

Lighting systems have long been capable of sensing when someone enters or exits a room and using that knowledge to turn lights on or off. More recently, connected lighting systems with sensors integrated into every luminaire have become broadly available, facilitating highly granular occupancy detection. Similarly, HVAC systems have long been able to use an understanding of building occupancy to adjust temperature setpoints and reduce energy use without significant impacts to occupant comfort. Energy codes (e.g., ASHRAE/IES Standard 90.1, IECC, Title 24) now require “occupied standby HVAC control,” whereby systems adjust both temperature and ventilation setpoints in zones that are determined to be unoccupied during normal occupancy hours. Here, this article discusses current issues that stymie the integration of Lighting and HVAC systems, and DOE activities focused on addressing them.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗

Assessing thermal comfort and participation in residential demand flexibility programs

Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging due to the variability in household energy use behaviors. Current estimates show that only 50 % of projected savings from DF resources are actualized due to regulatory, technological, and social barriers. From a household perspective, concerns over thermal comfort during space conditioning-based DF events significantly impact participation decisions. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. Here, the proposed method was applied to a heat pump DF field study in Cordova, Alaska. The study’s key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22 °C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available through utility and device-level energy use data. Thus, it can supplement these sources and help program administrators develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.

Demand side management↗

Air, surface, and wastewater surveillance of SARS-CoV-2; a multimodal evaluation of COVID-19 detection in a built environment

Environmental surveillance of infectious organisms holds tremendous promise to reduce human-to-human transmission in indoor spaces through early detection. In this study we determined the applicability and limitations of wastewater, indoor high-touch surfaces, in-room air, and rooftop exhaust air sampling methods for detecting SARS-CoV-2 in a real world building occupied by residents recently diagnosed with COVID-19. We concurrently examined the results of three 24-hour environmental surveillance techniques, indoor surface sampling, exhaust air sampling and wastewater surveillance, to the known daily census fluctuations in a COVID-19 isolation dormitory. Additionally, we assessed the ability of aerosol samplers placed in the large volume lobby to detect SARS-CoV-2 multiple times per day. Our research reveals an increase in the number of individuals confirmed positive with COVID-19 as well as their estimated human viral load to be associated with statistically significant increases in viral loads detected in rooftop exhaust aerosol samples (p = 0.0413), wastewater samples (p = 0.0323,), and indoor high-touch surfaces (p < 0.001)). We also report that the viral load detected in lobby aerosol samples was statistically higher in samples collected during presence of occupants whose COVID-19 diagnostic tests were confirmed positive via qPCR compared to periods when the lobby was occupied by either contact-traced (suspected positive) individuals or during unoccupied periods (p = 0.0314 and <2e–16). We conclude that each daily (24h) surveillance method, rooftop exhaust air, indoor high-touch surfaces, and wastewater, provide useful detection signals for building owner/operator(s). Furthermore, we demonstrate that exhaust air sampling can provide spatially resolved signals based upon ventilation exhaust zones. Additionally, we find that indoor lobby air sampling can provide temporally resolved signals useful during short duration sampling periods (e.g., 2-4 hours) even with intermittent occupancy by occupants diagnosed with COVID-19.

60 APPLIED LIFE SCIENCES↗

Impact of microkinetic modeling assumptions on predicted kinetics and mechanisms over undercoordinated sites

Accurate modeling of catalytic reactions on undercoordinated sites requires accounting for the structural and ensemble-specific nature of the active sites. This study examines how common microkinetic modeling (MKM) assumptions affect predicted kinetics and mechanisms on the stepped Pt(211) facet for the ethane dehydrogenation (EDH) and the ethane hydrogenolysis (EH). Six (211) MKMs were developed, differing in (i) the number of active sites represented, (ii) adsorbate site occupancy treatment, and (iii) inclusion of cross-facet interactions. These models are benchmarked against a particle-based microkinetic model (PB-MKM), which best represents step-edge behavior. MKM assumptions caused deviations in turnover frequencies exceeding ten orders of magnitude and led to contrasting mechanistic and selectivity predictions. Multi-site MKMs overestimate activity by inflating free site availability, single-site models underestimate activity, and uniform occupancy models overpredict coverage of multi-dentate intermediates, leading to reaction-specific artifacts. Overall, the Combined Site Edge Model (CSEM), a single-site MKM accounting for site occupancy and cross-facet interactions, most closely approximates PB-MKM predictions. All models predict similar kinetics when surfaces are clean or primarily occupied by monodentate species. This work provides practical guidance for selecting MKM frameworks for undercoordinated catalytic surfaces and highlights the critical role of modeling assumptions in catalytic predictions.

(211) facet↗

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Performance of CMS muon reconstruction from proton-proton to heavy ion collisions

The performance of muon tracking, identification, triggering, momentum resolution, and momentum scale has been studied with the CMS detector at the LHC using data collected at √(s$_{NN}$) = 5.02 TeV in proton-proton (pp) and lead-lead(PbPb) collisions in 2017 and 2018, respectively, and at √(s$_{NN}$) = 8.16 TeV in proton-lead (pPb) collisions in 2016. Muon efficiencies, momentum resolutions, and momentum scales are compared by focusing on how the muon reconstruction performance varies from relatively small occupancy pp collisions to the larger occupancies of pPb collisions and, finally, to the highest track multiplicity PbPb collisions. We find the efficiencies of muon tracking, identification, and triggering to be above 90% throughout most of the track multiplicity range. The momentum resolution and scale are unaffected by the detector occupancy. The excellent muon reconstruction of the CMS detector enables precision studies across all available collision systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A new constraint on galaxy–halo connections of [O ii ] emitters via HOD modelling with angular clustering and luminosity functions from the Subaru HSC survey

ABSTRACT Establishing a robust connection model between emission-line galaxies (ELGs) and their host dark haloes is of paramount importance in anticipation of upcoming redshift surveys. We propose a novel halo occupation distribution (HOD) framework that incorporates galaxy luminosity, a key observable reflecting ELG star-formation activity, into the galaxy occupation model. This innovation enables prediction of galaxy luminosity functions (LFs) and facilitates joint analyses using both angular correlation functions (ACFs) and LFs. Using physical information from luminosity, our model provides more robust constraints on the ELG–halo connection compared to methods relying solely on ACF and number density constraints. Our model was applied to $\rm [O\, {\small II}]$-emitting galaxies observed at two redshift slices at $z=1.193$ and 1.471 from the Subaru Hyper Suprime-Cam PDR2. Our model effectively reproduces observed ACFs and LFs observed in both redshift slices. Compared to the established Geach et al. HOD model, our approach offers a more nuanced depiction of ELG occupation across halo mass ranges, suggesting a more realistic representation of ELG environments. Our findings suggest that ELGs at $z\sim 1.4$ may evolve into Milky-Way-like galaxies, as their inferred halo masses evolve accordingly based on the extended Press–Schechter formalism, highlighting their role as potential building blocks in galaxy formation scenarios. By incorporating the LF as a constraint linking galaxy luminosity to halo properties, our HOD model provides a more precise understanding of ELG-host halo relationships. Furthermore, this approach facilitates the generation of high-quality ELG mock catalogues for future surveys. As the LF is a fundamental observable, our framework is potentially applicable to diverse galaxy populations, offering a versatile tool for analysing data from next-generation galaxy surveys.

Ishikawa, Shogo (ORCID:0000000221184211)↗

Scattering Processes from Quantum Simulation Algorithms for Scalar Field Theories

We provide practical simulation methods for scalar field theories on a quantum computer that yield improved asymptotics as well as concrete gate estimates for the simulation and physical qubit estimates using the surface code. We achieve these improvements through two optimizations. First, we consider a finite volume approach for estimating the elements of the S-matrix. This approach is appropriate in general for 1+1D and for certain low-energy elastic collisions in higher dimensions. Second, we implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians formulated both in the field occupation basis and field amplitude basis. Our algorithms are based on either second-order Trotterization or qubitization. The cost of Trotterization in occupation basis scales as O ( λ N 7 | Ω | 3 / ( M 5 / 2 ϵ 3 / 2 ) ) where λ is the coupling strength, N is the occupation cutoff, | Ω | is the volume of the spatial lattice, M is the mass of the particles and ϵ is the uncertainty in the energy calculation used for the S -matrix determination. Qubitization in the field basis scales as O ( | Ω | 2 ( k 2 Λ + k M 2 ) / ϵ ) , where k is the cutoff in the field and Λ is a scaled coupling constant. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of 4 × 10 6 physical qubits and 10 12 T -gates which corresponds to roughly one day on a superconducting quantum computer with surface code and a cycle time of 100 ns. This places the simulation of scalar field theory within striking distance of the gate counts for the best available chemistry simulation results.

Hardy, Andrew [Toronto U.] (ORCID:0000000235817382↗

Ecovoltaic solar energy development can promote grassland bird communities

Ecologically informed photovoltaic (PV) developments that co-prioritize PV electricity generation with ecosystem function (‘ecovoltaics’) have emerged as a promising land sharing strategy to minimize ecological conflicts associated with PV solar energy development. While habitat-focused ecovoltaic designs can conceptually benefit biodiversity by offsetting or enhancing impacts of PV development, foundational field research is needed to examine how wildlife respond to these novel ecosystems. We conducted passive acoustic monitoring (PAM) in 2023 and 2024 at 13 solar facilities and paired control sites to investigate avian community responses to ecovoltaic facilities in the Midwestern United States. Compared to control sites (row crop agricultural fields), we found that ecovoltaic sites supported more grassland bird species throughout a 17-week monitoring period between May and September. Grassland bird communities on ecovoltaic sites were also more stable than on agricultural controls, as measured by the Jaccard dissimilarity index. We also used PAM-based weekly species occurrences in an occupancy-modelling framework to investigate the influence of PV development and other landscape variables on grassland bird occupancy. 10 out of 13 modelled grassland bird species had greater predicted occupancy probabilities (ψ) on PV sites than control sites. Synthesis and applications. Our findings suggest that properly sited and developed ecovoltaic solar facilities in human altered landscapes can improve habitat for birds and other wildlife, but further research is needed to understand which species may benefit most from these novel ecosystems.

14 SOLAR ENERGY↗

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗

Thermal Resilience of Buildings and Communities: A Multistakeholder Review of Metrics and Approaches

Increasing temperature-related hazards require a collective effort to assess and enhance the thermal resilience of buildings and communities to protect occupants’ safety and minimize property or infrastructure damage. However, limited coordination across stakeholders and lack of standardized procedures for resilience assessment undermine the effectiveness of extreme temperature mitigation and adaptation strategies across the building life cycle. This review examines the current literature on resilience metrics to address thermal stress and risk due to extreme indoor environments. Stakeholders of thermal resilience include architects and engineers, occupants, property owners, real estate developers, urban planners, and policymakers. Additionally, motivations for measuring thermal resilience are emphasized, such as safeguarding occupant health and survivability, protecting property, and ensuring business continuity during extreme weather events. This review provides actionable insights and identifies future research needs for enhancing resilience through tailored metrics for stakeholders during the planning, design, construction, operation, and retrofitting phases of buildings and communities.

building life cycle↗

Energy impacts of nationwide window upgrades in commercial buildings

This report presents comprehensive estimates of the energy impacts of nationwide commercial building window upgrades in the United States, using a conservative approach. Windows play a substantial role in determining building energy use and occupant experience. Estimates point to commercial building windows impacting loads that represent more than 6 quads (approximately 6%) of annual primary energy use in the U.S. (Harris, 2022). Beyond heating and cooling loads, windows also have effects on lighting and occupant comfort. The fastest route to improving the energy efficiency of windows in U.S. buildings is upgrading or replacing windows in existing buildings. This is due to poor performance of windows in older existing buildings compared to most new construction, low levels of window replacement, and long window service life compared to energy-using building components. Nationwide window upgrades were considered using the following technologies: • Secondary glazing systems • Double pane (clear and tinted) • Triple pane (clear and tinted) • Electrochromic glazing Nationwide upgrades provide on the order of 4%–6% site energy savings in typical buildings, or up to 26% in buildings with the highest savings potential. Electrochromic windows, with their ability to adapt dynamically to environmental conditions, can provide additional benefits, ranging from median savings of 7.2% in buildings with window to wall ratio (WWR) greater than 10% and up to 28% for some buildings. Savings increase substantially for buildings with higher WWR. This study’s approach focused on isolating the direct energy benefits from improvement in window performance, and does not take into account the following additional benefits from window retrofits, which are likely to be substantial: • Managing peak demand and enabling HVAC equipment downsizing. • Energy savings from customizing upgrades to building type and climate. • Energy savings and comfort improvements resulting from post-retrofit reductions in air leakage. • Non-energy benefits, such as occupant comfort and resilience during extreme weather.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaluation of the Energy, Hygrothermal, and Thermal Capacity Performance of Cross-Laminated Timber

Cross-laminated timber (CLT) construction is gaining momentum in the US because it offers multiple advantages over traditional construction methods. Benefits that have received the most attention focus on constructability, the environment, and protection (e.g., blast resistance), although CLT construction is likely to offer other benefits, as well. Still, these have not been studied at length because such evaluations are costly, requiring long-term assessments in an actual building and specialized technical knowledge. Among the possible benefits, CLT construction likely provides a higher-performing building envelope. Using CLT panels to enclose a building means fewer joints in the opaque envelope than what is required in traditional stick-framed construction. Fewer joints mean fewer locations where the air- and water-resistive barrier (WRB) could be compromised; thus, a CLT building enclosure may require less maintenance and have a longer lifespan than a traditionally built structure because of fewer air and water leaks. In addition, CLT’s thermal mass moderates indoor temperatures, allowing the heating, ventilation, and air conditioning (HVAC) system to operate more efficiently during peak hours, reducing operational energy consumption throughout the lifetime of the CLT building (Salonvaara et al., 2022). Furthermore, more stable indoor temperatures can increase occupant comfort. The CLT’s thermal mass can also reduce energy costs by adjusting to utility time-of-use pricing without affecting occupant comfort. The ability of CLT buildings to bridge periods without HVAC operation prepares them for future grid interaction and provides a certain level of resilience against power outages. Researchers have attempted to quantify these benefits; however, their work is based on simplified simulations with numerous assumptions. To correctly understand the benefits, an actual building must be monitored. Therefore, information needs to be gathered on indoor and outdoor temperatures, HVAC energy consumption, thermostat setpoints, temperatures, and thermal transport in CLT components to comprehend how these parameters are affected by the CLT’s thermal mass. These data are needed to reduce the number of assumptions and calibrate simulation models to optimize HVAC controls to minimize overall energy consumption, reduce energy use and higher fees during peak demand, and maintain occupant comfort. Additionally, the calibrated simulation model allows the optimization exercise to be repeated in various US climates. Potential benefits can be tailored to buildings in various locations, and decisions can be made on where CLT construction could be most advantageous. Furthermore, monitoring and simulation results are needed to evaluate the durability of the CLT structures in different climates. This project’s researchers gathered information to help understand and quantify the benefits of CLT buildings concerning operational energy, moderated indoor temperatures, and comfort; the dynamic operation to provide grid services; and resilience in times of power outage. Through the corroboration of simulation models with real-world measurements, this study paves the way for extrapolating findings to other climatic zones and building typologies, thereby broadening the understanding of CLT’s multifaceted benefits and reinforcing its position as a material of choice in sustainable construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Burrowing Owl Survey Report for Sandia National Laboratories: 2025

The Burrowing Owl Survey Report for Sandia National Laboratories: 2025 provides data and analysis on occupancy surveys that were conducted during the 2025 survey season. The surveys were conducted in partnership with Kirtland Air Force Base’s Natural Resource Program and the United States Fish and Wildlife Service to maintain and expand on the long-term dataset on burrowing owls on base. Ecology Program biologists conducted roadside census surveys at survey areas previously identified by Kirtland Air Force Base along with several additional survey areas deemed suitable for occupancy. Remote sensor camera monitoring was also conducted on three nest burrows. In total, 12 breeding pairs of burrowing owls were confirmed in 2025. Several owls were confirmed to have leg bands, indicating previous captures and recurrence of these specific individuals on base. Occupancy surveys on burrowing owls continue to provide valuable information on ecosystem health and can serve to inform future management direction and decisions.

54 ENVIRONMENTAL SCIENCES↗

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

Brick Schema Standardized Plug Load Control Strategies for Load Reduction: Preprint

Plug loads comprise a significant percentage of commercial building energy consumption. Applying intelligent controls to turn off plug loads when unused can provide dynamic load reduction and flexibility, which are key traits of grid-interactive efficient buildings. This capability is important for equitable decarbonization as it can enable disadvantaged communities to electrify buildings without costly upgrades to electrical infrastructure. In this work, we present the effectiveness of various control strategies along with the operational lessons that informed their design. During a three-year period, we operated over 600 smart outlets in 12 university office buildings. The attached plug loads consisted primarily of printers, TVs, water dispensers, and copiers. After recording baseline power measurements for one year, we designed plug load control (PLC) strategies for each plug load type, use, and for different risk tolerance levels because PLC can potentially be disruptive to daily work. We used the Brick Schema to facilitate the management of plug load locations and other metadata. For advanced controls, we integrated the smart plugs with heating, ventilation, and air conditioning (HVAC) systems through the campus building automation system. We found static schedules to be the least disruptive and most predictable for occupants, resulting in 38% and 66% energy savings in two studies. For printers, print server-triggered PLC produced 86% savings, the highest of all strategies with minimal occupant impact. Scheduling of water dispensers and digital signage TVs produced 49% and 70% savings respectively with opportunities to improve performance with the use of HVAC occupancy data.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

Factors Influencing Grid-connected Heat Pump Water Heater Performance in the Southeast U.S.

Grid-connected heat pump water heaters (HPWH) can shift electrical load while minimizing impacts to hot water availability for occupants. This capability provides a flexible grid resource to utilities seeking to manage peak loads Such load control also can feasibly improve renewable utilization within the utility electric production mix, for instance using off-peak generation during periods with high renewable energy generation. It also offers lower electric bills to customers through increased energy efficiency of HPWH and cuts greenhouse gas emissions. In particular, the Southeast U.S. due to its high penetration of electric water heating presents a promising opportunity for grid-connected HPWHs. This paper builds upon the results of an extensive HPWH load shifting field study conducted in 51 occupied homes in Florida using EcoPort technology (Butzbaugh et al, 2022). In 2022, only an initial evaluation was available. Here, long term load results are available as well as examination of various control strategies and influences. Analysis is conducted for HPWH energy use and load shifting performance based on home occupancy (i.e., low and high) and water heater location (i.e., conditioned and unconditioned) across different temperature profiles. An unexpected outcome of this analysis was the poor performance of HPWHs located in conditioned spaces, possibly because of inadequate air volume from improper installation. We did find higher demand reductions from 2-hour load ups and slightly improved for critical peak signals in the afternoon control periods. As expected, higher occupancy households showed great load reductions.

Fenaughty, Karen↗