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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Building envelope anomaly characterization and simulation using drone time-lapse thermography

Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office building

In this study, an artificial intelligence based real-time prediction and control model to optimize condenser water temperature and discharge air temperature (DAT) set-points in water-cooled air handling unit (AHU) system has been developed. EnergyPlus-MATLAB co-simulation has been conducted to analyze the developed model's effectiveness. Here, to develop artificial neural networks (ANN) model, embedded neural network objects in MATLAB was utilized. The developed model could decide an optimal temperature set-points based on outdoor air wet-bulb temperature to reflect the Korean climate context. As a result, the developed ANN prediction model showed the predictive performance of Cv(RMSE) of approximately 21%. Compared to the conventional fixed temperature algorithm, which fixes AHU DAT at 14°C and condenser water temperature at 32°C, the ANN based optimized control showed a 22% total cooling energy reduction. These results show that significant energy savings can be achieved by simultaneously controlling condenser water temperature and AHU DAT set-points considering Korean climatic characteristics using AI technologies such as ANN models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Energy characteristics of multi-chiller load distribution algorithms in a large office building

Here, this study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2024

Coupling thermal energy storage with a thermally anisotropic building envelope for building demand-side management across various US climate conditions

Here, the thermally anisotropic building envelope (TABE) is a novel active building envelope that enhances energy efficiency and thermal comfort in buildings by transferring heat and cold between building envelopes and hydronic loops. When coupled with thermal energy storage (TES) units, the TABE + TES enables the storage of both heat and cold energy captured by the TABE roof or exterior walls. This stored energy can be later released by the TABE floor for indoor heating and cooling, benefiting both the grid and the end user. This paper evaluates the merits of TABE + TES for building demand-side management across various US climate conditions, focusing on peak load shaving, annual energy savings, and cost savings under time-of-use (TOU) electric rate schedules. Simulations were conducted by integrating time-of-day–informed, rule-based control strategies in MATLAB, TABE components and TES units in COMSOL Multiphysics, and whole-building energy analysis in EnergyPlus. A case study using the US Department of Energy’s prototype single-family detached house model in Birmingham, Alabama; Los Angeles, California; Oak Ridge, Tennessee; and Denver, Colorado, showed that the TABE + TES system achieved (1) 70 % peak load shaving in Los Angeles and Denver and 20 % in Birmingham and Oak Ridge; (2) significant peak electricity savings of 351–497 kWh, reducing peak energy consumption by 38 %–78 %; and (3) annual heating cost savings of 0.79 $\$$/m2–1.17 $\$$/m 2 and cooling cost savings of 0.60 $\$$/m 2 –1.17 $\$$/m 2 using a normal utility rate or low-TOU rate. The benefits of employing the TABE + TES system are even more significant under high TOU rates.

25 ENERGY STORAGE↗
DOE OSTI2025

Energy performance of an operational government building retrofitted with ceiling phase change material tiles in a mixed-humid climate

The aging U.S. building stock requires various retrofit measures to enhance their energy efficiency. Here, this study explores the integration of thermal energy storage and advanced building controls as viable retrofit solutions for load flexibility and peak demand response while maintaining the occupants' comfort. A detailed assessment is conducted on the energy use of an administrative building in Sumner County, Kansas, focusing on the implementation of phase change materials (PCMs) in the ceiling of occupied zones. First, a time-resolved, whole-building energy model is developed in EnergyPlus, incorporating complex thermal behaviors such as air exchange between the plenum space and occupied zones, envelope leakage, and operational schedules. The model is then validated using experimental field test data, and subsequently a parametric assessment of key PCM properties and application strategies is performed to evaluate cooling electricity demand benefits. The parametric study shows that the optimal retrofit strategy, comprising a PCM with 23°C peak melting temperature, 0.125 in. (3.17 mm) thickness, and 150 kJ/kg latent heat, combined with active controls that include 8 h of precooling, forced convection under the ceiling, and a 2°C thermostat setback during peak hours, can result in a maximum load shift during the peak period of 99.6 % and the total electricity savings during the peak period of 98.9 % for the optimum case and thus provide significant cost savings under time-of-use pricing scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2025

Experimental validation of a co-simulation architecture for modeling whole-building and detailed electrical distribution performance

This article presents an experimental validation of a co-simulation architecture for simultaneously modeling whole-building energy performance and detailed building electrical distribution system performance. The co-simulation architecture consists of a whole-building energy model (EnergyPlus®) embedded within a Modelica-based building electrical distribution system library called the Building Electrical Efficiency Analysis Model (BEEAM) using the Functional Mock-up Interface standard. We validate the model using experimental data collected at a full-scale test cell within Lawrence Berkeley National Laboratory’s FLEXLAB® facility. In conclusion, we show that the co-simulation model accurately predicts the electrical, mechanical, and thermal performance of the test cell for typical loads with both an AC and a DC electrical distribution topology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

An economic and technical feasibility analysis of a dual-source heat pump using both the air and the ground

The study investigates the economic and technical performance of a novel dual-source heat pump (DSHP) compared with that of air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). The DSHP can use both ambient air and the ground as a heat source or heat sink. It uses ambient air when its temperature is favorable for efficient heat pump operation. When the ambient temperature is too hot or cold, the ground source is used to retain high-efficiency heat pump operation. Since the DSHP can alternately use either the ground heat exchanger (GHE) or ambient air to meet the thermal load, the required size of GHE can be smaller than those of GSHPs. This study models the DSHP using a whole building energy simulation tool (EnergyPlus) coupled with a Python plug-in and Heat Pump Design Model (HPDM) to simulate its heating and cooling performance for a typical single-family home in 15 US climate zones. The required GHE size of the DSHP system is determined through simulations and compared with that of GSHPs. DSHP deployment can reduce electricity use compared to ASHPs, especially in cold climates where it shows a reduction of around 50%. When compared to GSHPs, DSHPs use 20%–40% more electricity in warm climates but consume around the same amount in moderate and colder climates. Since the DSHP can use air source when the ambient temperature is mild, the GHE size needed for the DSHP is about 40% less than that needed for GSHPs in hot climates and about 25% less in cold climates. In conclusion, the life cycle cost analysis shows that the DSHP is economically more feasible than ASHPs in colder regions and economically more feasible than GSHPs in hot and cold regions.

Dual-source heat pumps↗
DOE OSTI2026

A practical control strategy for demand flexibility with ensured occupant comfort in grid-interactive efficient buildings

This study proposes a practical and simplified demand response (DR) control strategy from the perspective of grid-interactive efficient buildings (GEBs), aiming to secure demand flexibility while ensuring occupant thermal comfort. Focusing on summer on-peak periods, a linear demand response (LDR) strategy that integrates cooling setpoint adjustment and lighting dimming was designed, and its performance was quantitatively evaluated. A case study was conducted using EnergyPlus-based simulations for a U.S. DOE small office prototype building under summer on-peak weather conditions. Compared with a conventional rapid demand response (RDR) strategy, the proposed LDR approach gradually reduced electrical loads while maintaining occupant thermal comfort indices, including predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD), within acceptable comfort ranges. Quantitative analysis of demand flexibility using the grid-interactive impact index (GII) and the flexibility strength index (FSI) showed that the LDR strategy provided approximately 12.5% demand flexibility during DR periods and achieved an electricity cost reduction of about 8.8%. In addition, the results of the part-load ratio (PLR)-based cooling system performance analysis showed that, in the on-peak period, the LDR strategy exhibited improved cooling performance compared with the baseline. Overall, this study shows the potential of a practical DR control strategy that can simultaneously achieve occupant comfort and demand flexibility without relying on complex advanced control technologies.

Jung, Dong Eun↗
DOE OSTI2025

The value of integrating a geothermal district heating system into a microgrid

As electrical grids increasingly rely on variable renewable energy, maintaining reliability and cost efficiency becomes more complex. To address these challenges, this study analyzed the integration of geothermal district heating as a grid-responsive thermal resource within a microgrid in Tuttle, Oklahoma. Building energy modeling using EnergyPlus estimated annual district heating demand at 2.9 GWh, with a peak load of 2.8 MW th . Techno-economic analyses were conducted to meet the heating demand under three geothermal scenarios, varying by production depth, flow rate, and thermal output, each supplemented by natural gas peaking boilers. In parallel, equivalent electrical load profiles were developed using typical coefficients of performance (COPs) for air-source heat pumps and electric boilers to establish an electrified baseline scenario. A complete end-use electrical load profile was also developed for the microgrid using Cambium dataset. The modeling results demonstrated reliable and economic operation of the geothermal systems over 30 years, with COPs ranging from 2.6 to 8.9 and the lowest levelized heating cost at $\$$54.6/MWh. Geothermal integration reduced electricity consumption by up to 94.7 % compared to the non-geothermal base case, yielding annual energy savings of up to $\$$803 k. Avoided grid costs ranged from $\$$65 k–$\$$147 k per year, with individual events avoiding up to $\$$4,863 per hour. Grid-responsive operation further reduced wholesale energy costs by 53–56 %. These findings demonstrate geothermal heating, traditionally treated as a non-grid-responsive thermal resource, can be reconfigured to support dynamic grid services, offering a scalable pathway to enhance reliability and reduce costs in renewable-rich microgrids and district heating networks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. Here, a peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

25 ENERGY STORAGE↗
DOE OSTI2024

Electrification of residential and commercial buildings integrated with hybrid renewable energy systems: A techno-economic analysis

Here, this study assesses the techno-economic viability of city-scale building electrification, comparing a base case scenario with conventional HVAC systems to an electrified scenario with heat pump systems. EnergyPlus simulations reveal a 28% increase in annual electricity consumption in the electrified scenario, shifting the peak consumption from summer to winter with a 49% surge. Hybrid renewable energy systems (HRESs) are then evaluated using HOMER Pro, considering renewable penetration from 0% to 100%. The net present cost (NPC) of the base case and electrified scenarios range from $\$10.34$ to $\$24.38$ billion and $\$8.79$ to $\$50.31$ billion, respectively. Sensitivity analysis explores the impact of carbon tax, fuel price, and renewable subsidies on HRESs in the electrified scenario. Results indicate that applying a carbon tax up to $\$120$/tonne of CO 2 increases the optimum renewable fraction from 5% to 26%, while higher fuel prices and renewable subsidies further enhance the feasibility by shifting the optimum renewable fraction to 60% and 54%, respectively, demonstrating the interplay between economic factors and renewable integration in electrified urban contexts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

An approach for fast and accurate simulation of phase change material based thermal energy storage in buildings

Latent heat thermal energy storage (LHTES) has significant potential for mitigating peak electricity demand and enabling load shifting in buildings. Phase Change Material embedded heat exchangers (PCM-HX) can significantly improve energy demand management due to high storage capacity. However, PCM-HX evaluation typically depends on computationally expensive fully transient simulations, posing significant challenges for scalable system- and building-level energy assessments across different climates and system architectures. This paper presents a generalized, accurate, and computationally efficient methodology for simulating building energy systems integrated with LHTES. The PCM-HX transient performance is represented by performance maps generated using a Generalized Resistance-Capacitance Model (GRCM) that enables accurate predictions of arbitrary PCM-HXs at low computational cost. The feasibility of the proposed approach was verified using a case study considering a dual-mode heat pump-thermal energy storage (HP-TES) system simulated in Modelica with Spawn of EnergyPlus™ for a DOE prototype small office building in two locations: Tampa, FL, and International Falls, MN. The PCM-HX performance maps provided accurate predictions of PCM-HX transient behavior, with mean absolute percentage deviations within 2–4% compared to GRCM while also achieving at least 1800× reduction in computational time. Moreover, the HP-TES system achieved energy savings of up to 17.4% in Tampa, FL, and 62.2% in International Falls, MN, demonstrating the broader applicability of the proposed methodology across different climate zones. This work highlights the importance of robust PCM-HX models in enabling accurate and computationally efficient building-level simulations and enabling future research opportunities for investigating optimized HP-TES designs and advanced control strategies for grid-interactive buildings.

Modelica↗
DOE OSTI2015

A comparative study on the environmental impact of supermarket refrigeration systems using low GWP refrigerants

Supermarket refrigeration systems have high environmental impact due to their large refrigerant charge and high leak rates. Consequently, the interest in using low GWP refrigerants such as carbon dioxide (CO 2 ) and new refrigerant blends is increasing. In this study, an open-source Life Cycle Climate Performance (LCCP) framework is presented and used to compare the environmental impact of four supermarket refrigeration systems: a transcritical CO 2 booster system, a cascade CO 2 /N-40 system, a combined secondary circuit with central DX N-40/L-40 system, and a baseline multiplex direct expansion system utilizing R-404A and N-40. The study is performed for different climates within the USA using EnergyPlus to simulate the systems' hourly performance. Finally, further analyses are presented such as parametric, sensitivity, and uncertainty analyses to study the impact of different system parameters on the LCCP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Development and validation of quasi-steady-state heat pump water heater model having stratified water tank and wrapped-tank condenser

Heat pump water heater systems (HPWH) introduce new challenges for design and modeling tools, because they require vapor compression system balanced with a water storage tank. In addition, a wrapped-tank condenser coil has strong coupling with a stratified water tank, which leads HPWH simulation to a transient process. To tackle these challenges and deliver an effective, hardware-based HPWH equipment design tool, a quasi-steady-state HPWH model was developed based on the DOE/ORNL Heat Pump Design Model (HPDM). Two new component models were added via this study. One is a one-dimensional stratified water tank model, an improvement to the open-source EnergyPlus water tank model, by introducing a calibration factor to account for bulk mixing effect due to water draws, circulations, etc. The other is a wrapped-tank condenser coil model, using a segment-to-segment modeling approach. In conclusion, the HPWH system model was validated against available experimental data. After that, the model was used for parametric simulations to determine the effects of various design factors.

42 ENGINEERING↗
DOE OSTI2018

Calibrated simulation analysis for integration of evaporative cooling and radiant cooling system for different Indian climatic zones

Radiant cooling system (RCS) has proven to be an energy efficient system for meeting a building's cooling requirements. RCS is energy efficient and provides better thermal comfort compared to conventional all-air heating ventilation air conditioning (HVAC) system. To further improve the efficiency of RCS, a parallel evaporative cooling system (cooling tower) is coupled with RCS and analyzed for different climatic condition using calibrated model. A statistical analysis of weather files (based on wet bulb temperature), was used to identify the availability of useful water for cooling tower integration with RCS. A comprehensive simulation feasibility study of the application of cooling tower in RCS was performed for different cities to cover every climatic zones of India. It was found that in summer, the wet bulb temperature (WBT) of different climatic zones, except warm-humid, is suitable for the integration of cooling tower with RCS. An experimental setup was designed and developed for integration of parallel chiller and cooling tower with panel-based RCS for cooling and a dedicated outdoor air system (DOAS) for dehumidification and ventilation. Experiments were conducted for chiller and cooling tower operated RCS in Composite climate of Jaipur, Rajasthan, India. Building Energy Models (BEM) were developed for both the chiller-operated RCS and cooling tower-operated RCS in EnergyPlus and calibrated with the measured data. Using the calibrated models, performance of the system was analyzed for different climatic zones of India. A chiller-operated RCS was considered as a baseline to compare the annual energy saving potential and monthly performance of the cooling tower integrated RCS. Here, in the cooling tower–operated RCS, a total annual savings of 7% in hot and dry climates, 11% in composite climates and 20% in temperate climates was achieved compared to the chiller-operated RCS.

54 ENVIRONMENTAL SCIENCES↗