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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 1,063 records · Page 59

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Maximizing the Benefits of On-Site Renewable Energy Generation Using On-Site Energy Storage

The commercial and industrial building sectors have a major role to play in a U.S. low-carbon future. Building owners can make a difference by deploying on-site renewable generation and storage technologies, which facilitates future-proofing their buildings as well as more effectively meeting the service requirements of the building. This resource provides an overview of common generation, storage, and load management technologies in behind-the-meter systems, and serves as a primer to demonstrate the impact on net building loads required from the grid for different sizes of renewable generation along with on-site electrical and thermal storage. These discussions can help commercial building owners communicate the value of these systems to project teams and develop innovative behind-the-meter solutions to achieve their energy goals.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2024

Lessons Learned from the Clean Energy to Communities (C2C) Peer-Learning Cohort Evaluating and Prioritizing Municipal Buildings for Energy Efficiency and Decarbonization Investment

The National Renewable Energy Laboratory (NREL), with support from World Resources Institute (WRI), designed and led a six-month peer-learning cohort from January through July 2024 on "Enhancing Resilience at Critical Facilities through Solar, Storage, and Microgrids" as part of the U.S Department of Energy's Clean Energy to Communities (nrel.gov/c2c) (C2C) program. Representatives from 15 municipalities, municipal utilities, colleges, and Tribes from across the United States participated in monthly workshops covering best practices for planning and deploying local resilience projects. This document shares key takeaways, lessons learned, and resources from the cohort.

buildings↗

DOE OSTI2025

Reduced Contracting Time To Accelerate Construction: A Streamlined Approach for Speed and Scale

In the fast-paced construction world, efficiency isn't just an advantage - it's a necessity. By implementing the Cooperative Construction Contracting Agreement (CCCA), we are transforming how we approach major projects, replacing traditional multistep contracting with a streamlined, one-step process. This innovative contracting approach saves valuable time and resources, enabling us to accelerate delivery across our portfolio of projects. Under the old model, each construction phase required separate competitive solicitations for design, build, and other services. With the CCCA, we've consolidated these steps, working with a trusted partner from start to finish. This results in significant time savings that translate into faster project execution without compromising quality or safety.

collaborative partnerships↗

DOE OSTI2025

Building a Clean Energy Workforce: Best Practices for State Energy Offices

As federal investments into the clean energy economy are underway, State Energy Offices (SEOs) are engaging in efforts to grow the workforce through the creation and/or funding of workforce initiatives that recruit, train and support pathways into energy efficiency and clean energy jobs. This report provides a summary of clean energy workforce development best practices, supplemented by case studies and lessons learned from several SEOs that showcase these strategies in practice. SEOs can reference this resource as they implement, manage, oversee, and/or evaluate efforts their offices are undertaking through various federally funded programs.

clean energy↗

DOE OSTI2023

Working with Trusted Local Organizations to Address Impediments to Cost-Effective and Holistic Building Performance Improvements

Commercial buildings are at the heart of any community. Investments towards the health and performance of community buildings can support a wide range of critical community and individual benefits; however, many buildings have been left behind in these investments due to a spectrum of barriers, especially those located in underserved communities. This portfolio of work stands with the intent that if we can create solutions to support the hardest-to-reach commercial buildings in gaining equitable access to energy efficiency, zero energy, and zero carbon solutions, we can support all commercial buildings with these goals. To understand success and barriers, and identify new, innovative, and impactful solutions, NREL is taking a four-part approach to: 1. Develop a methodology for defining affordability in commercial buildings and understand its impact on underserved communities, 2. Work with trusted, local organizations to address barriers that impede access to cost-effective, holistic, building performance improvements, 3. Support minority-focused developer incubator programs for, and within, underserved communities, and 4. Identify overlapping value streams that support underserved community and utility needs.

barriers↗

DOE OSTI2023

A Methodology for Defining Affordability in Commercial Buildings and its Impact on Underserved Communities

building electrification↗

DOE OSTI2026

Validating Simulated Models of Energy Consumption by a Battery Electric Motorcoach: A real-world deployment in a harsh climate.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

DOE OSTI2026

Building the Business Case with JUSTIFI: Quantifying Multiple Benefits in the Automotive Industry

The Michigan State University Industrial Training and Assessment Center (MSU ITAC) conducted a pilot study at an automotive parts manufacturer in Michigan. The study identified energy-productivity enhancements through the application of the JUSTIFI software. Key recommendations included replacing six inefficient rooftop units (RTUs) with a new air rotational unit. By quantifying operational savings for this project, the expected payback period went from 6.6 years to 1.4 years. Additionally, the installation of variable frequency drives (VFDs) on condenser tower motors was suggested. By including all operational benefits, the payback period was reduced from 8.2 years to 1.3 years. This comprehensive analysis aims to bolster the manufacturer's goals of reducing energy while enhancing overall operational efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1975

Citizen's actions

The various ways in which energy may be conserved by individual citizens as consumers were explored. The following barriers against citizens implementing an effective conservation program were described: credibility gap between producers and consumers, consumptive lifestyles, inverted rate structure, low fuel costs, and initial costs compared to life cycle costs. The following indices for saving energy were identified: time to develop alternatives, scarcity of fuels, reduction of dependence on imports, and decreasing environmental pollution. The various approaches to encourage energy conservation by individuals were described, followed by specific conclusions and recommendations.

Source record↗

NASA NTRS2004

Numerical Modeling of Conjugate Heat Transfer in Fluid Network

Fluid network modeling with conjugate heat transfer has many applications in Aerospace engineering. In modeling unsteady flow with heat transfer, it is important to know the variation of wall temperature in time and space to calculate heat transfer between solid to fluid. Since wall temperature is a function of flow, a coupled analysis of temperature of solid and fluid is necessary. In cryogenic applications, modeling of conjugate heat transfer is of great importance to correctly predict boil-off rate in propellant tanks and chill down of transfer lines. In TFAWS 2003, the present author delivered a paper to describe a general-purpose computer program, GFSSP (Generalized Fluid System Simulation Program). GFSSP calculates flow distribution in complex flow circuit for compressible/incompressible, with or without heat transfer or phase change in all real fluids or mixtures. The flow circuit constitutes of fluid nodes and branches. The mass, energy and specie conservation equations are solved at the nodes where as momentum conservation equations are solved at the branches. The proposed paper describes the extension of GFSSP to model conjugate heat transfer. The network also includes solid nodes and conductors in addition to fluid nodes and branches. The energy conservation equations for solid nodes solves to determine the temperatures of the solid nodes simultaneously with all conservation equations governing fluid flow. The numerical scheme accounts for conduction, convection and radiation heat transfer. The paper will also describe the applications of the code to predict chill down of cryogenic transfer line and boil-off rate of cryogenic propellant storage tank.

Majumdar, Alok↗

DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Ecosystem-Level Biomimicry for the Built Environment: Adopting Systems Ecology Principles for the Control of Heterogeneous Energy Systems

This paper presents, to our knowledge, the first system-level engineering study to bio-mimic the cybernetics and flow dynamics of energy resources in natural ecosystems for the control of heterogeneous energy infrastructures in the built environment. To this end, we introduce a novel Biomimetic Pulsing State (BPS) control that functionally mimics mature ecosystems. A preliminary Modelica-based case study features a single-family residential building with electrical and HVAC subsystems. The BPS control objective is to minimize the energy exchange between the building and the grid for the purposes of future self-supporting buildings and grid stability. The building contains PV, a wind turbine, a battery storage system, and a fan coil/heat pump HVAC system served by an ambient district energy network. Evaluating the control performance (BPS vs. constant setpoint) over several renewable energy scenarios (net importer, net zero, net exporter), simulation results show how the building's HVAC system can dynamically adjust its electrical load and temperatures to the electrical system's net energy status in real-time with BPS control. As a net importer, the heat pump consumed 29% less energy and its peak power reduced by 15% with BPS control compared to the constant setpoint case, with the zone air temperature 1 degree C lower on average. As a net exporter, the heat pump effectively consumed the same energy, but the peak power increased by 34% with BPS control, while the zone air temperature was 1 degree C higher when renewable energy was abundant, preheating the home. BPS and constant setpoint control produced comparable results under a net zero scenario. While further evaluation is essential, BPS control may help communities meet their sustainability and resiliency targets as they transition towards fully distributed and renewable energy grids.

biomimicry↗

DOE OSTI2022

Developing occupant archetypes within urban low-income housing: A case study in Mumbai, India

Rapid urbanization pressure and poverty have created a push for affordable housing within the global south. The design of affordable housing can have consequences on the thermal (dis)comfort and behaviour of the occupants, hence requiring an occupant-centric approach to ensure sustainability. This paper investigates occupant behaviour within the urban poor households of Mumbai, India and its impact on their thermal comfort and energy use. This study is a first-of-its-kind attempt to explore the socio-demographic characteristics and energy-related behaviour of low-income occupants within Indian context. Three occupant archetypes, Indifferent Consumers; Considerate Savers; and Conscious Conventionals, were identified from the behavioural and psychographic characteristics gathered through a transverse field survey. A two-step clustering approach was adopted for occupant segmentation that highlighted considerable diversity in occupants’ adaptation measures, energy knowledge, energy habits, and their pro-environmental behaviour within similar socio-economic group. Building energy simulation of the representative archetype behaviour estimated up to 37% variations for air-conditioned and up to 8% variation for fan-assisted naturally ventilated housing units during peak summer months. The results from this study establish the significance of occupant factors in shaping energy demand and thermal comfort within low-income housing and pave way for developing occupant-centric building design strategies to serve this marginalized population. The developed low-income occupant archetypes would be useful for architects and energy modelers to generate realistic energy use profiles and improve building performance simulation results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Challenges and opportunities of machine learning control in building operations

Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. Here this paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings

Building-level loads and load profiles prescribed by current modeling rules save modelers time and avoid gaming during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load profiles for different space types. As a solution to this issue, this paper develops a database of building-space-specific loads and load profiles used in code compliance modeling. The existing sets of loads and load profiles are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load profiles is introduced. After that, the database for these building-space-specific loads and load profiles is presented. In addition, one case is studied to demonstrate the applications of these loads and load profiles. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). Finally, the energy results simulated by using these three methods are compared, which show building-level methods can produce energy results up to 20% different than the space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Data-driven evaluation of HVAC operation and savings in commercial buildings

Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air conditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Simulation-based Performance Evaluation of Model Predictive Control for Building Energy Systems

The performance of model predictive control (MPC) can be significantly affected by different choices of controller parameters such as the time intervals for model discretization and control sampling. Due to the lack of a systematic understanding on how these parameters affect control performance, they are usually selected arbitrarily in practice.In this paper, the combined impacts of selected time intervals for model discretization and control sampling on the performance of MPC are comprehensively investigated for the first time through detailed simulations. Specifically, a typical MPC strategy is first designed to improve building operations based on a reduced-order model of building dynamics. Then, the performance of the designed MPC is evaluated against different choices of time intervals for model discretization and control sampling on a simulated office building. The detailed simulation results reveal that the time interval for model discretization has a much greater influence on the performance of MPC than the time interval for control sampling. Although the time interval for control sampling usually receives more attentions in practice, it turns out that the time interval for model discretization affects the prediction performance, cost saving, and computation time simultaneously and more significantly. Therefore, the simulation-based performance evaluation presented here sheds light on the impacts of different time intervals and facilitates their selection for practical applications of MPC to building operations

Huang, Sen↗

DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗