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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Investigation of pre-cooling as a recommended measure to improve residential buildings’ thermal resilience during heat waves

More intense heat waves are expected to occur more frequently in the twenty-first century. During severe heat waves, cooling capacity shortfall and overheating are likely to occur in residential buildings, and this will adversely affect occupant's thermal comfort and productivity. We propose a strategy of pre-cooling the house during off-peak hours to mitigate overheating during heat waves. Additionally, simulation results of a prototype single-family house show that adopting the rule-based control (RBC) of pre-cooling thermostat setpoint schedule is effective in reducing thermal discomfort, and that the efficacy of pre-cooling depends upon several building characteristics. An optimized control (OC) of the thermostat setpoint schedule was developed based on the simulation of a prototype building. A simplified yet improved RBC (IRBC) pre-cooling schedule was then extracted from the OC schedule for practical implementation at a larger scale. The effects of the RBC schedule and IRBC schedule were evaluated in the King District of Fresno, which contains 814 residential buildings. Results show that both thermostat setpoint schedules can reduce overheating effectively and that IRBC is slightly better than RBC for most buildings. The findings support the California government's recommendation on pre-cooling to mitigate overheating, which can be further improved with an optimized thermostat setpoint schedule broadcast to residents through early alert messages before a heat wave.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Long-term impact of electrification and retrofits of the U.S residential building in diverse locations

The U.S. buildings sector contributes 30% of operational carbon emissions, with residential buildings accounting for 56%. Reducing residential carbon emissions is crucial for achieving net-zero carbon goal. While many studies examine energy efficiency retrofit (EER) and electrification, few explore their long-term impacts across diverse climates and dynamic grid clean energy penetrations, as well as their economic effects on households. Here, this study proposes a method to assess how EER and electrification affect long-term decarbonization and economics across different climates, focusing on carbon emissions, energy burden (the percentage of household income spent on energy), and payback period in four locations: Tampa, San Diego, Denver, and Great Falls. The study also introduces the concept of implicit energy burden by considering investment costs. Results show that while electrification can reduce long-term emissions with increased clean energy penetration, it may not always achieve decarbonization due to mismatches between clean energy availability and demand. In cooling-dominant locations, electrification lowers energy burden and peak demand, but in heating-dominant locations, it increases energy burden to 8.24%, raises peak demand by 632.78%, and shifts it from summer to winter. After integrating investment costs, the implicit energy burden can reach 8.35% in cold climates. For already highly electrified buildings in Denver and Great Falls, the payback period of EER measures can be shortened by up to 48.98%. The study highlights a tradeoff between decarbonization and energy burden alleviation, showing that while EER measures can reduce the energy burden, they only achieve one-quarter of the carbon emission reduction of electrification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Probabilistic reliability assessment and case studies for predicted energy savings in residential buildings

This study aims to (1) investigate the key influential parameters (KIPs) in estimating the uncertainty of energy savings using a residential building energy simulation model and (2) perform uncertainty quantification for energy savings for several different scenarios. The proposed methodology was successfully applied to the calculation of uncertainties associated with residential energy retrofits using two test houses designed for pre- and post-retrofit cases. Uncertainties were determined using basic parameters that might be supplied to an energy model and then reevaluated based on an audit of the KIPs identified, resulting in substantially reduced uncertainty. Of four different scenarios, the most uncertain scenario estimated the annual energy savings from the retrofit would be between 18% and 51% at a 95% confidence level, and the least uncertain scenario estimated the annual savings would be between 26% and 40% at a 95% confidence level. The actual measured annual savings from the two test houses was 28%, which shows an agreement with the uncertainty analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The role of above-code labeling programs in reducing CO 2 e emissions in residential buildings

Residential buildings account for 15 % of total U.S. carbon dioxide (CO 2 ) emissions. Voluntary, above-code labeling programs, such as the ENERGY STAR and Zero Energy Ready Home programs, can play a crucial role in reducing the carbon footprint of the new construction residential building sector by encouraging adoption of more energy-efficient and lower emissions building practices and technologies. It's important for the industry to understand the carbon dioxide equivalent (CO 2 e) and energy cost savings potential of these programs. Here, to estimate the potential CO 2 e emissions savings for homes built to above-code labeling program levels, it is imperative to have reliable and accurate tools for estimating emissions from buildings. The open-source OpenStudio-ERI workflow employs detailed physics-based building energy modeling (BEM) to estimate energy usage and operational CO 2 e emissions. In this study we use the OpenStudio-ERI workflow and regional, time-varying emissions factors from the Cambium database to compare the CO 2 e emissions and energy costs of homes built to performance levels required by these above-code labeling programs relative to homes built to model energy codes. We present the results of our analysis and provide a range of emissions and annual energy cost savings for the above-code labeling programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and field demonstration of residential air source integrated heat pump using a three-stage compressor

To promote decarbonization and all electrification at residential sectors, it is necessary to use air source heat pumps (ASHPs) to replace natural gas for space heating and water heating. ASHPs are widely utilized for residential space cooling, heating, and water heating due to their simplicity and cost-effectiveness. However, their performance can be compromised in cold climates, where they may experience reduced heating capacity. A multi-functional heat pump, using a single compressor, to meet all home space conditioning and water heating demands, is an emerging technology. To address this limitation, we have developed and demonstrated an air source integrated heat pump to fulfill comprehensive home comfort requirements. This system employs a three-stage compressor and a single set of heat exchangers and valves, optimizing functionality while minimizing costs. The performance of the developed system was rigorously evaluated in both laboratory and field settings. In laboratory conditions, the system achieved a Seasonal Energy Efficiency Ratio of 17.0 (average COP of 4.98) and a Heating Seasonal Performance Factor of 11.0 (3.22). Additionally, in its most efficient operational mode—combining space cooling and water heating—the unit attained a total energy efficiency exceeding 7.0 seasonal COP in the field and could heat a 189-liter tank of water in just 25 min. The field study corroborated the laboratory findings, validating the system’s performance in real-world conditions. Here, this integrated heat pump represents an ideal solution for decarbonizing homes in northern climates by providing efficient space heating and water heating, thereby replacing the need for natural gas.

Integrated heat pump↗
DOE OSTI2026

From models to reality: a systematic review on simulated and measured residential heat pump energy savings

High-performance HVAC solutions are central to residential energy management. A substantial share of these are electric, reversible-cycle systems, with heat pumps representing the largest portion of current and near-term adoption. This review synthesizes peer-reviewed and grey literature on residential space heating and cooling heat pumps. The academic literature is dominated by modeling (73.8%), with limited field measurement (13.1%). Grey literature from United States serve as a supplemental resource providing measured savings. Conversions from electric-resistance heating consistently show the largest site energy reductions, while oil/propane baselines yield moderate savings, and gas baseline scenario often deliver small and region-dependent savings. This study cross-checks the grey literature measured data with simulation data filtered from the ResStock dataset. The comparison indicates a discrepancy between simulations and measured data: simulated site EUIs are typically lower than measured EUIs, but percentage energy savings fall in similar ranges, implying simulations capture directional effects while underestimating energy use. Factors associated with variability and model–measurement differences include system characterization and control representation (e.g., backup heat engagement, thermostat/setpoint strategies, commissioning/installation quality), occupant behavior, weather normalization, metering scope, and envelope characterization. This paper also outlines the proposed methodology for comparing simulation and measured data for heat pumps. It emphasizes the metrics used for comparison and units harmonization, building characteristics matching, and compact metadata are needed for simulations to match measured data. The proposed methodology is expected to improve the credibility of simulated savings as measured evidence grows.

Yu, Lili↗
DOE OSTI2026

Field-based AFDD for refrigerant undercharge in residential HVAC systems: enhancing reliability through false alarm mitigation

This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.

False Alarm↗
DOE OSTI2013

Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

We propose a method to examine structural and behavioral determinants of residential electricity consumption, by developing separate models for daily maximum (peak) and minimum (idle) consumption. We apply our method on a data set of 1628 households' electricity consumption. The results show that weather, location and floor area are among the most important determinants of residential electricity consumption. In addition to these variables, number of refrigerators and entertainment devices (e.g., VCRs) are among the most important determinants of daily minimum consumption, while number of occupants and high-consumption appliances such as electric water heaters are the most significant determinants of daily maximum consumption. Installing double-pane windows and energy-efficient lights helped to reduce consumption, as did the energy-conscious use of electric heater. Acknowledging climate change as a motivation to save energy showed correlation with lower electricity consumption. Households with individuals over 55 or between 19 and 35 years old recorded lower electricity consumption, while pet owners showed higher consumption. Contrary to some previous studies, we observed no significant correlation between electricity consumption and income level, home ownership, or building age. Some otherwise energy-efficient features such as energy-efficient appliances, programmable thermostats, and insulation were correlated with slight increase in electricity consumption. (C) 2013 Elsevier Ltd. All rights reserved.

Kavousian, A↗
DOE OSTI2022

Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis

Electrical energy storage systems offer a wide range of options to efficiently manage our power supply infrastructure. Energy and environmental security via renewable resources and energy storage devices will also play a critical role in addressing the supply-demand challenges. A novel energy efficient storage system based on near isothermal compressed air energy storage concept, named as Ground-Level Integrated Diverse Energy Storage (GLIDES) is analyzed for integration with residential and commercial buildings. Additionally, the influence of different configurational aspects on key performance and cost attributes is presented in this study. GLIDES modules in the range of 0.5 kWh–15 kWh configurations were considered for residential buildings whereas 25 kW h–500 kW h modules were considered for commercial buildings. Detailed cost and performance analysis of the GLIDES module proved them to be an effective resource in lowering the peak energy demand and corresponding utility bills.

25 ENERGY STORAGE↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Association of residential greenness with obesity and physical activity in a US cohort of women

Highlights: • Women living in greener neighborhoods were less obese, and had higher activity levels. • Similar association observed in urban and rural areas. • Physical activity was an important mediator of association between greenness and obesity. There is evidence of several health benefits associated with neighborhood greenness, but reasons for this are unclear. Studies have found that those who live in greener neighborhoods are more physically active, and have lower rates of obesity. Relatively few studies have attempted to characterize associations between greenness and both obesity and physical activity concurrently, or among women who are at higher risk of developing cancer and for whom physical activity may be important for primary prevention. To address these gaps, we undertook a cross-sectional analysis of data from 50,884 women who enrolled in the Sister Study between 2003 and 2009. This cohort includes women aged 35–74 whose sister had been diagnosed with breast cancer. Residential measures of greenness were determined using the US National Land Cover database. Logistic regression was used to characterize associations between greenness, obesity, and physical activity. Adjustments were made for other possible confounders. Women who lived in areas with the highest tertile of greenness (based on a 500 m buffer) had a reduced risk of obesity (body mass index (BMI) ≥ 30) relative to those in the lowest tertile (odds ratio (OR) = 0.83, 95% CI = 0.79–0.87). We also found that those the upper tertile of greenness were 17% more likely to expend more than 67.1 metabolic equivalent (MET) hours per week when compared to those in the lowest tertile (OR = 1.17, 95% CI = 1.10–1.23). Beneficial associations between greenness and both obesity and physical activity were observed in urban and rural areas, and regionally, stronger associations were observed in the western census region in the US. Mediation analyses indicated that physical activity attenuated the association between greenness and obesity by 32%. Our findings indicate that, amongst US adult women at higher risks of breast cancer, residential proximity to greenness may help mitigate against sedentary behaviors that increase the risk of chronic disease.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Are policy incentives for solar power effective? Evidence from residential installations in the Northeast

State incentives for solar power have grown significantly in the past several years. This paper examines the effectiveness of policy incentives to increase residential solar photovoltaic (PV) capacity. We use county-level panel data and control for demographic characteristics, solar resources, and pro-environmental preferences. Results show that among financial incentives, rebates have the most impact with an additional $1 per watt rebate increasing annual PV capacity additions by close to 50%. Factors that affect financial returns to solar PV such as electricity price and solar insolation are also found to be significant. Results also point to a significant positive relationship between hybrid vehicle sales and residential PV capacity growth, indicating the importance of pro-environmental preferences as a predictor of solar PV demand. Back of the envelope calculations suggest that the cost of carbon mitigation through rebates is around $184 per ton of CO2.

14 SOLAR ENERGY↗
DOE OSTI2021

Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: Evidence from a randomized-control trial in the United States

Regulators, policymakers, and stakeholders in several states have raised concerns about the price elasticity of residential customers’ electricity demand, especially as related to different customer subpopulations, management of critical end-uses, and enrollment approaches. This analysis sought to quantify the diversity of residential customer price elasticity in a time-of-use rate across these different dimensions using data generated from a utility pricing experiment. Finally, customers were found to be more elastic during the peak period of critical peak days, who were predicted to own and use air conditioning, and who volunteered for time-of-use (TOU) rates (but those defaulted on were 25% as elastic).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Customer economics of residential photovoltaic systems: Sensitivities to changes in wholesale market design and rate structures

We report that the customer economics of U.S. residential photovoltaics (PV) often depend on retail electricity rates, because most utilities compensate customer-sited PV generation via net metering. The future bill savings from net metering are uncertain and dependent on retail rate structures, wholesale market design, and renewable penetration levels, among other factors. We explore the impact of the following assumptions on the bill savings from residential PV: a wholesale electricity market design with a price cap (as opposed to an energy-only market); a retail rate with a fixed customer charge (as opposed to a fully volumetric rate); and increasing-block pricing (as opposed to a non-varying flat rate). A wholesale price cap can dampen the expected bill-savings erosion due to moving from a low to a high renewables scenario for customers with time-varying rates and net metering. Moving from a fully volumetric rate to a two-part tariff rate with a fixed customer charge could severely Erode the bill savings under net metering, because PV generation could only displace the (reduced) volumetric portion of the rate. Lastly, increasing-block pricing might have an even greater impact on the bill savings from behind-the-meter PV than the other uncertainties explored in this paper.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A review of regulatory standard test methods for residential wood heaters and recommendations for their advancement

In many regions, residential wood heaters are a leading source of harmful air pollution but only satisfy a small portion of local heating demands. In response, standardized laboratory test methods have been developed to characterize and limit wood heater emissions. While these test methods are a key tool for advancing both wood heater technology and environmental regulations, many of the experimental procedures are outdated and provide few actionable insights for improving heater performance. Furthermore, these test methods vary widely around the world and may not adequately capture the performance of wood heaters operating in residences. This study presents a comprehensive review of standardized wood heater test methods to identify fundamental experimental objectives and regulated performance metrics. Using the results of this review, recommendations are provided to make the test methods more accessible and representative of residential performance, while generating actionable data to motivate heater design improvements. This study elucidates the current state of standard test methods, and the developments needed to advance clean wood heater technologies and public policies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Inequalities in global residential cooling energy use to 2050

Intersecting socio-demographic transformations and warming climates portend increasing worldwide heat exposures and health sequelae. Cooling adaptation via air conditioning (AC) is effective, but energy-intensive and constrained by household-level differences in income and adaptive capacity. Using statistical models trained on a large multi-country household survey dataset (n = 673,215), we project AC adoption and energy use to mid-century at fine spatial resolution worldwide. Globally, the share of households with residential AC could grow from 27% to 41% (range of scenarios assessed: 33-48%), implying up to a doubling of residential cooling electricity consumption, from 1220 to 1940 (scenarios range: 1590-2377) terawatt-hours yr. –1 , emitting between 590 and 1,365 million tons of carbon dioxide equivalent (MtCO 2 e). AC access and utilization will remain highly unequal within and across countries and income groups, with significant regressive impacts. Up to 4 billion people may lack air-conditioning in 2050. Our global gridded projections facilitate incorporation of AC’s vulnerability, health, and decarbonization effects into integrated assessments of climate change.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗