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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Individualized empirical baselines for evaluating the energy performance of existing buildings

The evaluation of building energy performance requires a baseline for comparison. Common empirical baselines are usually used for existing buildings since they are fast and convenient. However, the same type of building at the same location will receive the same baseline despite their difference in usage. Individualized baselines by creating building energy models are possible solutions, but it is labor intensive and time-consuming. To fill the gap, this study is to develop individualized empirical baselines for existing buildings in a fast way. First, common empirical baselines are created based on survey data. Then, to get training samples, building energy models for large-scale existing buildings are created and simulated. So finally, based on simulation results, mathematical models to get individualized empirical baselines in a fast way are created. U.S. medium office buildings were used as an example to demonstrate the method. We developed 30 mathematical models for medium office buildings in two vintages (constructed before 1980 and after 1980) and 15 climate zones. The mean absolute percentage errors (MAPE) between the individualized empirical baselines and the modeled baselines for those 30 mathematical models are all lower than 5.5%. An engineer can obtain the individualized empirical baseline for an existing building in a few seconds by using the open-source tool we developed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Learning-Based Building Flexibility Estimation and Control to Improve Microgrid Economics and Resilience: Preprint

This paper proposes a learning-based building flexibility estimation and control framework to improve system economics and resilience. A data-driven building load flexibility model consisting of weather forecasting and estimating load consumption is proposed to quantify building heating, ventilation, and air conditioning (HVAC) load flexibility. A reinforcement learning-based microgrid controller is proposed to dispatch distributed generators, distributed energy resources, and build HVAC loads while taking flexibility information as one of the inputs. Simulation analysis is conducted on the model of a real microgrid in California. The effectiveness of the proposed learning-based building flexibility estimation and control in reducing microgrid energy costs and improving the sustainability of critical loads is demonstrated.

building load flexibility↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

big data↗
DOE OSTI2021

A physics-informed data-driven low order model for the wind velocity deficit at the wake of isolated buildings

Detailed simulations of the Atmospheric boundary layer (ABL) provides information on wind velocity, direction and turbulence for optimizing the design of wind turbines and wind farms and their operations. Distributed wind turbines have been considered for use in urban or suburban areas for covering energy needs using a sustainable energy solution. Simulations of high Reynolds number turbulent ABL flows past obstacles can be used for the prediction of the velocity deficit and turbulence characteristics in the leeward side of building-like obstacles, allowing for the estimation of power losses and fatigue loads in wind turbines. However, fully three-dimensional simulations of high Reynolds number ABL flows using Large Eddy Simulation (LES) or Reynolds Average Navier-Stokes Simulations (RANS) continue to require large computational/ execution times. As a result, these 3D simulations may not be ideal tools for the rapid assessment of wind energy potential. In the present study, 3D numerical results from wall resolved RANS simulations were used for the development of a low order model that allows the rapid assessment of wind velocity and turbulence in the leeward side of an isolated building.

17 WIND ENERGY↗
NASA NTRS1979

Analysis of building heating and cooling

Energy Conservation Program (ECP) gives design engineer methodology and easy-to-use computer program for simulating hourly thermal characteristics over full year for individually characterized zones within building. Inexpensive system can be used to develop thermal model of building to aid selection of most suitable and economical heating and cooling system for building

Chai, V. W.↗
DOE OSTI2022

Commercial Building Planning and Retrofitting Strategy for Grid Services

The increasing integration of distributed energy resources (DERs) plays an important role in improving energy consumption efficiency. In September 2020, the Federal Energy Regulatory Commission (FERC) approved Order 2222 which opens wholesale electricity markets to small capacity DERs. The benefit of this new FERC Order 2222 is that DERs, such as rooftop solar panels and batteries, will be able to participate in regional electricity markets and provide grid services. Meanwhile, the planning and operation strategies of DERs are facing new challenges to account for the impact of the wholesale market with numerous uncertainty factors. Therefore, in this paper, we propose a new planning and retrofitting model for long-term commercial buildings that considers both DER investment and market participation. Specifically, we explore the capability of implementing DERs for grid services. The effectiveness of the proposed model is validated using real-world data. Simulation results also validate that participating in grid services can significantly increase revenues through appropriate building energy management and shorten the payback period of DER investments.

building energy management↗
NASA NTRS1974

Flat plate collector performance determined experimentally with a solar simulator

The NASA is constructing a new office building at Langley Research Center that will utilize solar energy for heating and cooling. A collector technology program being conducted at Lewis will provide the basis for selecting collectors for use at Langley. The technology program includes testing collectors in an indoor facility under simulated solar radiation. Tests have been conducted on five collectors to date and performance data are presented herein.

Vernon, R. W.↗
DOE OSTI2020

Dynamic metrics of natural ventilation cooling effectiveness for interactive modeling

The evaluation of natural ventilation potential for cooling indoor spaces during the early design phases is of great interest to researchers and practitioners. Among various definitions and usages for natural ventilation potential (NVP) in early design evaluation, this paper reviews and identifies the key performance indicators, and proposes two new dynamic metrics—natural ventilation cooling effectiveness (NVCE) and climate potential utilization ratio (CPUR). The metrics are dynamically responsive to various design options, in both steady and transient states, allowing consideration of thermal mass. Assisting in design development processes, the metrics quantify how well indoor spaces make use of natural ventilation’s cooling capacity. Case studies are presented to demonstrate how NVCE and CPUR enable designers to evaluate the predicted performance and how to apply the information to improve building design. Finally, the results of the design iterations showed that the relationship among various design parameters should be dynamically understood in order to evaluate the performance of natural ventilation, confirming that “the more the airflow, the greater the potential,” and “the heavier the thermal mass, the greater the energy saving” were not always true.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Regional suitability of heating tower heat pumps based on technical and economic performance maps

The heating tower heat pump (HTHP) technology is evolving rapidly due to its high performance in summer and frost-free characteristic in winter. However, lacking comprehensive comparisons between the HTHP and conventional methods, such as air-source heat pump (ASHP), and chiller and boiler, greatly limits its applications. To address this problem, this paper carries out technological and economic performance analyses of the three systems in 353 locations over China by the processes of building load calculation, system sizing, annual simulation, performance comparison, and demonstration. Energy saving principle and potential of the HTHP are presented and explained. Based on that, economic performance comparison is conducted by considering the energy conversion efficiency and prices. The results show that the HTHP has advantage over the ASHP in most locations in China. The improvement in annual coefficient of performance (COP) is about 10% to 20%. For the locations in southwest China like Chongqing, the improvement is also less than 10% due to the large regeneration penalization in winter and small difference between dry-bulb and wet-bulb temperature in summer. The annual COP of the HTHP is 10% to 23% higher than that of the chiller and boiler in hot summer and cold winter zone, and cold zone. However, the HTHP shows no advantage over the chiller and boiler in severe cold zone because of the low efficiency of heat pump in winter, neither in hot summer and warm winter zone due to the small heating load. Based on the results, technical and economic performance maps are developed to guide the application of the HTHP in different climate regions.

heating tower heat pump, air-source heat pump, chi↗
DOE OSTI2025

PETSc/TAO developments for GPU-based early exascale systems

The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization via the Toolkit for Advanced Optimization (TAO). PETSc is used in dozens of scientific fields and is an important building block for many simulation codes. During the U.S. Department of Energy’s Exascale Computing Project, the PETSc team has made substantial efforts to enable efficient utilization of the massive fine-grain parallelism present within exascale compute nodes and to enable performance portability across exascale architectures. We recap some of the challenges that designers of numerical libraries face in such an endeavor, and then discuss the many developments we have made, which include the addition of new GPU backends, features supporting efficient on-device matrix assembly, better support for asynchronicity and GPU kernel concurrency, and new communication infrastructure. In conclusion, we evaluate the performance of these developments on some pre-exascale systems as well as the early exascale systems Frontier and Aurora, using compute kernel, communication layer, solver, and mini-application benchmark studies, and then close with a few observations drawn from our experiences on the tension between portable performance and other goals of numerical libraries.

Exascale Computing Project (ECP)↗
DOE OSTI2025

Method of Test for Evaluating Building Performance Simulation Software

ANSI/ASHRAE Standard 140 Method of Test for Evaluating Building Performance Simulation Software specifies the method to test the core competency of building performance simulation (BPS) software, a broad class of software which includes building energy modeling (BEM) software. Standard 140 has been widely used by BEM software vendors to help diagnose and compare the results from the program with other modeling programs. The Standard is also referenced by codes, standards, government programs, and other incentive programs as a part of minimum requirements for qualifying BEM software. The explanatory information, summary tables and figures in this 140 User’s Manual (referred to as “this Manual”) are provided to help users in implementing various suites of tests specified in Standard 140-2023 (referred to in this manual as “Standard 140” or “the Standard”).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES‐CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open‐source platform for optimization of process flowsheets utilizing state‐of‐the‐art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure‐based conceptual design and optimization under uncertainty.

42 ENGINEERING↗
DOE OSTI2022

Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School

By using the same occupant schedule for all spaces, a building level occupancy schedule in building energy modelling can reduce the cost and time of data collection, especially for large-scale simulations or when detailed occupancy data cannot be obtained. However, by describing the unique occupancy status in each space, a space level schedule can better reflect real-world scenarios. This research investigates the energy prediction impact of a space level occupancy schedule for primary school modelling in 16 ASHRAE climate zones. The results show that when switched from a building level to a space level occupancy schedule, the energy prediction difference is between -1.0% and 0.8%. Generally, the predicted building energy consumption using a space level occupancy schedule is higher than when using a building level occupancy schedule in hot and warm climate zones, but lower in other climate zones.

building energy model↗
DOE OSTI2020

A Simplified Building Modeling Approach for Identifying Whole Building Retrofits

Building Energy Modeling (BEM) is an effective strategy for optimizing new and retrofit building design, evaluating its energy savings potential and rating its energy performance. However, because of the complexity and expense of modeling, only a small percentage of buildings are simulated. Over 80% of buildings are <25,000 ft2, and the cost associated with BEM can be a big deterrent for reaching this subsector, which usually ends up complying with energy codes and qualifying for incentives using prescriptive measures or generalized design guides. Similarly, efficiency considerations for small building retrofits are often identified through deemed measures or Technical Reference Manuals, which though a scalable and convenient approach, can limit innovation in design and optimization of measures. Simplification of the modeling process, where appropriate, can lead to increased energy savings and more informed decision making. This paper will discuss the development of a ruleset for a simplified approach based on the Performance Rating Method (PRM) contained in ASHRAE Standard 90.1. The approach aims to simplify and lower the cost of the energy modeling process in a controlled and documented manner. An anticipated outcome is an increased use of BEM and its effectiveness in the design, retrofit and operation of commercial buildings. In addition, simplification of the modeling process will result in fewer errors from misinterpretations of both program requirements and program intent in simulation software. The paper will discuss the need and advantages of a simplified modeling approach for whole building retrofits and the technical process for achieving the same.

Goel, Supriya↗
DOE OSTI2025

Transforming Windows from Energy Liabilities to Zero-Energy Assets: Next-Generation Solutions for Buildings

Windows have traditionally contributed to a building's HVAC load, but they can also become a source of net energy gain or even operate as zero-energy components. For heating applications, highly insulating windows can harness more solar heat than the energy lost through them, transforming windows from energy liabilities to assets. Dynamic glazings provide further benefits by regulating solar heat gain, reducing cooling loads in summer and heating demands in winter. This simulation study focuses on developing the next generation of zero-energy windows (ZEW) for residential new construction. Through annual energy simulations across climate zones 1-8, ZEW performance benchmarks were established based on current code-level buildings, and we've identified the regions where meeting ZEW standards are most achievable. This work evaluates both static and dynamic window technologies, assessing their effects on annual energy use and cost. Key findings demonstrate that ZEW performance is achievable across diverse climate zones, with specific regional requirements. Most climate zones from 3-8 can achieve ZEW with specific configurations, while some warm climates (1-2) appear challenging for ZEW implementation. Climate zones 4-6 consistently allow for zero energy window implementation, offering multiple pathways through either static or dynamic window technologies. Colder climate zones (7-8) ZEW products allow for higher SHGC values while requiring low U-values.

Yu, Lili↗