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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Lab Home Testing of Residential Isolation Space Control to Minimize Infectious Disease Transmission in Existing Single-Family Homes

Existing evidence strongly suggests that viral infectious diseases can be transmitted via an airborne route across distances in indoor environments. Accordingly, the risk of airborne transmission within homes should be managed. The public health emergency associated with SARS-CoV-2 makes controlling airborne transmission of respired viruses in indoor environments critical, especially in poorly ventilated indoor environments. The effectiveness of engineering interventions requiring minor efforts that create a negative-pressure isolation zone (IZ) for a contagious person has yet to be tested for existing residential homes. To mitigate the risk of airborne virus transmission and maximize health protection for the population in existing single-family homes, the relative effectiveness of several control strategies are investigated in this report. While very high-efficiency MERV filtration, high ventilation rates, and other controls can help be effective, most occupants are not likely to have the time or means for advanced measures found in hospitals. This project focused on testing relatively simple efforts that utilize existing or easy to acquire materials and simple processes.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock: Preprint

Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.

building stock↗
DOE OSTI2022

Consumer Guide to Residential Renewable Energy

Learn how installing residential renewable energy systems, such as geothermal heat pumps and wind or solar energy systems, can save energy, lower utility bills, and earn homeowners money. This fact sheet from Energy Saver includes information on how to start planning to install renewable energy systems at home.

residential renewable energy, geothermal heat pump↗
DOE OSTI2024

Modeling and Validation of a Residential Multi-Functional Variable Refrigerant Flow Heat Pump System with Heat Recovery

To bridge the existing gap in modeling the variable refrigerant flow heat pump systems with heat recovery (VRFHR), we developed a suite of dynamic VRFHR system models in Modelica. These models are specifically tailored for residential multi-functional VRFHR (MF-VRFHR) applications, including space conditioning and domestic hot water (DHW) heating, utilizing both the TIL library for HVAC equipment and the Buildings library for thermal load calculations. The development comprises essential component models, including the newly developed heat recovery unit (HRU), along with system models that integrate the heat pump system and building envelope. These system models accommodate various operational modes such as heating-only, cooling-only, and heating-recovery (including heating-dominant and cooling-dominant) modes. Furthermore, we propose an efficient optimization-based model calibration method that identifies critical model parameters while utilizing a small amount of data obtained from either real systems or manufacturer's specifications. We demonstrate the effectiveness of these models and the proposed calibration method for a MF-VRFHR system installed in Richland, WA. The developed models are calibrated and validated using data collected under different operational modes during both heating and cooling seasons. The results show that the models capture the system dynamics and achieve high accuracy, with the coefficient of the variation of the root-mean-square-error less than 15% for variables such as outdoor unit power consumption, compressor speed, space temperature and DHW temperature. The validated models serve as a reliable representation of the MF-VRFHR system, facilitating the development and validation of optimized controls needed to realize the full benefits of integrated heat pump systems. Future research will utilize these models to develop advanced controls and optimize system performance for improved energy efficiency and demand flexibility.

Modeling, Variable refrigerant flow (VRF) systems,↗
DOE OSTI2026

Estimating Flexibility Envelopes for Residential Customers From Utility Smart Meter Data: Preprint

Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Characterization of demand response in the commercial, industrial, and residential sectors in the United States

The goal of our study is to provide an overview of demand response (DR) technologies, including standards and end uses, in the United States and describe resource characteristics and the attributes of 14 specific DR resources in the U.S. commercial, residential, and industrial sectors. The attributes reviewed for the end uses being considered are response frequency, response time, the need for and impacts of energy pre- or recharge, the cost of enabling a resource to respond to a load-curtailment signal, and the magnitude of load curtailment in a given resource. We also describe controls and communications technologies that can enable end uses to participate in DR programs. The characterization was initially developed as a foundational work to quantify hourly availability of DR resources from the selected end uses followed by a multi-laboratory effort that quantified DR's value within the Western Interconnection.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Development and validation of a second-order thermal network model for residential buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Furthermore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Analysis of precooling optimization for residential buildings

To reduce peak demand and/or energy cost for residential buildings, optimal precooling strategies are becoming important as an alternative to rule-based precooling strategies that are intuitive but may not be optimal. Since precooling optimization is heavily dependent on a variety of factors such as the home thermal properties, HVAC system, weather, thermal comfort criteria, and utility rate structure, the individual and collective impact of these factors on precooling performance needs to be analyzed. In addition, since the indoor air temperature is affected by heat transfer to and from the interior wall surface, performance analysis in view of the interior wall surface temperature is also essential. Therefore, in this paper, an optimal precooling strategy that accounts for the aforementioned factors and utilizes a second-order thermal network model, is proposed. With this strategy, the HVAC on/off control signal that minimizes 24-hour energy cost while maintaining thermal comfort, is determined. Through extensive simulations, it is found that the proposed optimal precooling strategy is able to adapt to changing conditions and that having a sufficiently low interior wall surface temperature during precooling is critical for avoiding expensive on-peak operation. Here, the reason for the latter is that such a temperature indicates that enough “cooling energy” has been stored. It is also found that weather has the most dominant impact on the precooling performance, followed by home thermal condition, with the rated cooling capacity and utility rate structure having the least impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Performance assessment of active insulation systems in residential buildings for energy savings and peak demand reduction

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ~980 to 2,290 kWh in a single-family home with a floor area of ~220 m 2 compared with an IECC 2018 baseline. We found the energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Design trade-offs for residential retail tariffs and virtual power plants

Retail rate design and virtual power plants (VPPs) have the potential to shift customer electricity demand and provide economic benefits to utility customers. As the adoption of distributed energy resources (DERs) and flexible loads increases, retail tariff and program design can impact Bonbright's rate design principles including affordability, fairness, and economic efficiency. We model the effects of residential retail rates and VPP programs on power system costs in Massachusetts under a potential future system with high renewable energy and DER adoption. We model interactions among retail rate design, demand flexibility, and utility costs and identify trade-offs across different rate designs and VPP programs. We estimate that time-of-use (TOU) rates and VPP programs designed to avoid critical peak rates can lower overall system costs by 3.5 %-4.8 %. These lower costs translate to lower electricity bills for 62 %-91 % of customers, depending on the scenario. Although TOU rates with a critical peak VPP program can benefit all customer segments and are economically efficient, a VPP program with flat rates leads to the lowest overall bills for customers. We find that customers with loads that align with peak demand and who participate in critical peak VPP programs can underpay for their contribution to utility costs and shift costs to other customers. While our assumptions about mandatory TOU and/or critical peak pricing likely impact the magnitude of the results, the results highlight the trade-offs of these tariffs and programs and the importance of tariff and program design as demand becomes more flexible and responsive.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Study of optimal sizing for residential sorption heat pump system

Gas-driven sorption heat pumps (GDSHP) show significant potential to reduce primary energy use, associated emissions and energy costs for space heating and domestic hot water production in residential applications. This study considered a bivalent heating system consisting of a sorption heat pump and a condensing boiler, and focuses on the optimal heating capacity of each of these components relative to each other. Two bivalent systems were considered: one based on a solid chemisorption cycle (GDSHPA), and one based on a resorption cycle (GDSHPB). Simulations of year-round space heating loads for two single-family houses, one in New York and the other Minnesota, were carried out and the seasonal gas coefficient of performance (SGCOP) calculated. The sorption heat pump’s design heating capacity as a fraction of the bivalent system’s total heating capacity was varied from 0 to 100%. Results show that SGCOP was effectively constant for sorption heat pump design capacity greater than 41% of the peak bivalent GDSHPA design capacity in Minnesota, and 32% for GDSHPB. In New York, these values were 42% and 34% for GDSHPA and GDSHPB respectively. The payback period was also evaluated based on postulated sorption heat pump component costs. The fastest payback was achieved with sorption heat pump design capacity between 22 and 44%.

42 ENGINEERING↗
DOE OSTI2026

Virtual refrigerant charge sensing algorithm for residential CO₂ heat pumps

Natural refrigerants are increasingly adopted in next-generation heat pump systems, among which CO₂ heat pumps have attracted significant attention. However, due to their high operating pressures, the leakage risk is higher, resulting in undercharge conditions and degraded heat pump performance. Thus, developing an accurate refrigerant charge level detection technique is necessary to guarantee safe and efficient operation. Although virtual refrigerant charge (VRC) level calculation algorithms for CO₂ heat pumps exist, they typically rely on empirically selected features without a systematic selection framework, leading to multicollinearity and potential overfitting, which limit their prediction accuracy and generalizability. To address these issues, this study proposes a VRC algorithm framework with a systematic feature selection method that identifies physically meaningful and statistically significant features, and is applied using a residential CO₂ heat pump as a case study. The method is extended from previous work on conventional refrigerants to account for charge behavior in CO₂ gas coolers. The selected features include gas cooler outlet density, evaporator pressure, and superheat temperature. The results demonstrate that the proposed feature selection method significantly improves prediction accuracy compared to existing VRC approaches. A relatively small training dataset (∼30 samples) is sufficient for feature identification and model development. The developed algorithm achieves less than 3% prediction error under both undercharge and overcharge conditions, representing reductions of 46.7% and 35.3% compared to two recent reference VRC algorithms for transcritical CO₂ heat pumps reported in the literature. The proposed algorithm and feature selection method enhance leakage detection capability, facilitate the deployment of CO₂ heat pump systems, and contribute to reduced energy waste and maintenance costs.

Guo, Fangzhou [Lawrence Berkeley National Laborato↗
DOE OSTI2020

Performance of low-cost indoor air quality monitors for PM 2.5 and PM 10 from residential sources

Advances in particle sensor design and manufacturing have enabled the development of low-cost air quality monitors (LCMs). Here, the sensors use light scattering to estimate mass concentration and thus require evaluation for aerosols of varied composition and size distribution. We tested the performance of six LCMs designed for home use and having a retail price under US$300 in October 2018. We assessed their performance by comparing their output to reference PM 2.5 and PM 10 measurements from 21 common residential sources and from infiltrated outdoor PM 2.5 . Reference data were obtained by using gravimetric measurements to adjust time-resolved output from an aerosol spectrometer with both electrical mobility and optical particle sensors. Compared by linear regression to reference measurements, LCMs had negative intercepts and slopes of 1–2 for infiltrated outdoor PM 2.5 . Semi-quantitative responses (~50–200% of actual PM 2.5 ) were obtained for varied aerosols including minerals (ultrasonic humidifier, vacuuming, test dust); combustion products (incense, mosquito coil, extinguished candles); microwave popcorn; and cooking involving frying or grilling. LCMs had low or no response to sources for which all mass was in particles smaller than 0.25 μm, including steady candle flames and cooking without frying or grilling. PM 10 data from LCMs was more variable than PM 2.5 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Design and analysis of optimal pre-cooling in residential buildings

Existing pre-cooling strategies provide a means of shifting or reducing the peak demand and/or energy cost in residential buildings. However, majority of them are rule-based and therefore may not be optimal in terms of cost saving, leaving room for improvement. In this paper, an integer linear programming problem that accounts for the thermal properties of a specific home, HVAC system capacity, utility rate structure, and weather conditions and makes use of a home thermal model is formulated. This problem determines the HVAC on/off control signal that minimizes the 24-h energy cost while maintaining thermal comfort and calculates the corresponding optimal indoor air temperature. The model is constructed using home thermal properties identified via data training in real-time. Through simulation, the energy performance of the proposed optimal pre-cooling strategy is investigated and compared with three rule-based operation strategies from the literature. It is found that the optimal strategy requires the least energy consumption without sacrificing thermal comfort. The superb energy performance of the optimal strategy is attributed to a longer runtime of the HVAC system in cool outdoor air conditions and to the elimination of deadband in HVAC operation, which is required by the rule-based strategies, to allow the indoor air temperature to stay near the thermal comfort upper bound as much as possible. In terms of energy cost, the rule-based operation strategies require 3.52, 1.90, and 2.79, respectively, while the optimal strategy only requires 1.52. These figures represent a saving of 56.82%, 20.00%, and 45.52%, respectively. The results suggest that the optimal strategy is indeed significantly more effective than the existing rule-based operation strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. Herein this research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

47 OTHER INSTRUMENTATION↗