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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Thermal comfort evaluated for combinations of energy-efficient personal heating and cooling devices

Personal comfort systems (PCS) have potential to fulfill building occupants' personal thermal comfort preferences with great efficiency. But to integrate them into building conditioning, there must be a broader selection of PCS devices available. Design guidance and standards are needed to assure that such devices provide high levels of comfort effectiveness and energy efficiency. This study addresses these needs. A suite of minimum-power PCS devices was built that target body parts significant to alliesthesia—a heated shoe insole, heated/cooled wristpad, small deskfan, and heated/cooled chair. They were tested in a climate chamber under cool and warm conditions using both thermal-manikin and human-subjects. Their efficiency at physically heating/cooling the body is high; the combined suite has a coefficient of performance (COP) of 3.6 for cooling and 0.88 for heating. The subjects' whole-body thermal acceptance and thermal comfort perception were improved by the devices in an additive manner; using the combined suite over 80% of people accepted ambient temperatures of 18°C and 29°C. The PCS ‘corrects’ the ambient temperature towards thermal neutrality by as much as 6.5 K cooling and 3.6 K heating, overcoming building occupants' typical interpersonal thermal differences and making possible large HVAC energy savings in buildings. The idea of temperature corrective power can be the basis of standards for PCS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Affordable Cold Climate Infill Housing with Hybrid Insulation Approach, Wyandotte, Michigan (Fact Sheet)

Even builders who are relatively new to energy-efficient construction can consistently reach a target whole house airtightness of 1.5 air changes per hour at 50 Pascals (ACH50) with high R-value enclosures that use a hybrid insulation approach. The City of Wyandotte, Michigan, started a construction program in 2010 to build affordable, energy-efficient homes on lots in existing neighborhoods. A goal of the program was to engage local builders in energy-efficient construction and be able to deliver the new houses for less than $100/ft2. By the end of 2012, approximately 25 new houses were built by five local builders under this program. To help builders consistently achieve the airtightness target, a local architect worked with researchers from Building Science Corporation, a U.S. Department of Energy Building America team, to develop a technology specification with several key pieces. A high R-value wall and roof assembly made use of 2 ?6 advanced framing and a hybrid insulation approach that included insulating sheathing to control thermal bridging and closed cell spray polyurethane foam insulation (ccSPF) for its airtightness and vapor control benefits. This approach allows the air barrier to be completed and tested before any finishing work occurs, ensuring that problems are spotted and corrected early in the construction process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Occupancy Simulator Solver v1.0

Occupant schedules at room level are crucial information to building energy modeling which quantifies occupant-related energy efficiency technologies and controls for new building design as well as existing building retrofits to reduce energy use and GHG emissions in buildings. The Occupancy Simulator Solver is an executable software component that simulates the stochastic behavior of occupant presence and movement in a building using the Markov-chain model. Occupancy Simulator Solver is the calculation engine of the web App Occupancy Simulator, which takes high level input on occupants, spaces and events, then calls the Solver to simulate occupant movement and generate occupant schedules for each space. The generated schedules capture diversity and stochastic nature of occupant activities (e.g., in office, meetings, breaks, lunch). These schedules are stored in CSV files and can be downloaded from the App and used for building performance simulation.

Hong, Tianzhen↗
DOE OSTI2014

Energy Efficiency Pilot Projects in Jaipur: Testing the Energy Conservation Building Code

The Malaviya National Institute of Technology (MNIT) in Jaipur, India is constructing two new buildings on its campus that allow it to test implementation of the Energy Conservation Building Code (ECBC), which Rajasthan made mandatory in 2011. PNNL has been working with MNIT to document progress on ECBC implementation in these buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Buildings Interoperability Landscape – DRAFT

Buildings are an integral part of our nation’s energy economy. The advancement in information and communications technology (ICT) has revolutionized energy management in industrial facilities and large commercial buildings. As ICT costs decrease and capabilities increase, buildings automation and energy management features are transforming the small-medium commercial and residential buildings sectors. A vision of a connected world in which equipment and systems within buildings coordinate with each other to efficiently meet their owners’ and occupants’ needs, and where buildings regularly transact business with other buildings and service providers (such as gas and electric service providers) is emerging. However, while the technology to support this collaboration has been demonstrated at various degrees of maturity, the integration frameworks and ecosystems of products that support the ability to easily install, maintain, and evolve building systems and their equipment components are struggling to nurture the fledging business propositions of their proponents.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Regional Shelter Analysis: Inhalation Exposure Methodology

During normal operations, buildings can protect their occupants from outdoor hazards, including airborne pollutants. Purposeful sheltering increases this protection. A longterm international research effort has advanced our knowledge of building protection physics. However, an operationally efficient, regional-scale methodology to account for US building protection effects is not available. Such a method is necessary because (a) the overwhelming majority of the US population is indoors at any given time and (b) a regional-level building protection methodology could better estimate populations truly at risk in emergencies, support improved decision-making (shelter vs. evacuation decisions), help guide resources towards those most at risk, and improve population level dose-response relationships, which are often derived by estimating ambient (outdoor) exposures and then tuning dose-response relationship parameters to best match the distribution of illness reports.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The Efficiency-Resilience Nexus

This fact sheet provides information on the relationship between resilience and energy efficiency and how it applies to buildings. It also outlines tools and resources that building owners can use to make their buildings more resilient.

Resilience, Resiliency, Efficiency-Resilience Nexu↗
DOE OSTI2019

Integrating physics-based models with sensor data: An inverse modeling approach

Physics-based building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing buildings.

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 OSTI2012

International Review of Frameworks for Impact Evaluation of Appliance Standards, Labeling, and Incentives

In recent years, the number of energy efficiency policies implemented has grown very rapidly as energy security and climate change have become top policy issues for many governments around the world. Within the sphere of energy efficiency policy, governments (federal and local), electric utilities, and other types of businesses and institutions are implementing a wide variety of programs to spread energy efficiency practices in industry, buildings, transport, and electricity. As programs proliferate, there is an administrative and business imperative to evaluate the savings and processes of these programs to ensure that program funds spent are indeed leading to a more energy-efficient economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Global-Local Policy Search and its Application in Grid-Interactive Building Control

As the buildings sector represents over 70% of the total U.S. electricity consumption, it offers a great amount of untapped demand-side resources to tackle many critical grid-side problems and improve the overall energy system's efficiency. To help make buildings grid-interactive, this paper proposes a global-local policy search method to train a reinforcement learning (RL) based controller which optimizes building operation during both normal hours and demand response (DR) events. Experiments on a simulated five-zone commercial building demonstrate that by adding a local fine-tuning stage to the evolution strategy policy training process, the control costs can be further reduced by 7.55% in unseen testing scenarios. Baseline comparison also indicates that the learned RL controller outperforms a pragmatic linear model predictive controller (MPC), while not requiring intensive online computation.

demand response↗
DOE OSTI2019

BuildingIQ Technology Field Validation

This document describes a field validation and verification of the BuildingIQ Predictive Energy Optimization (PEO) technology based on a five-site study. BuildingIQ describes its PEO technology as a software-as-a-service (SaaS) platform that optimizes commercial building HVAC control for system efficiency, occupant comfort, and cost. It is targeted for use in large, complex buildings, and integrates with the building automation system (BAS) to conduct supervisory control. The PEO algorithm defines optimal space air temperature setpoints that are automatically implemented at the variable air volume (VAV) terminal units when possible, or through supply air temperature and duct static pressure setpoints at the air handling unit (AHU) level. The optimization is built upon a learned predictive model that provides a 24-hour ahead forecast of the building’s power profile, using weather forecasts and historical operational data; this model is updated every 4 to 6 hours. Demand-responsive load reductions may also be implemented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

BuildingIQ Technology Field Validation

This document describes a field validation and verification of the BuildingIQ Predictive Energy Optimization (PEO) technology based on a five-site study. BuildingIQ describes its PEO technology as a software-as-a-service (SaaS) platform that optimizes commercial building HVAC control for system efficiency, occupant comfort, and cost. It is targeted for use in large, complex buildings, and integrates with the building automation system (BAS) to conduct supervisory control. The PEO algorithm defines optimal space air temperature setpoints that are automatically implemented at the variable air volume (VAV) terminal units when possible, or through supply air temperature and duct static pressure setpoints at the air handling unit (AHU) level. The optimization is built upon a learned predictive model that provides a 24-hour ahead forecast of the building’s power profile, using weather forecasts and historical operational data; this model is updated every 4 to 6 hours. Demand-responsive load reductions may also be implemented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

ResStock Measure Documentation: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Envelope Improvements and Advanced Air Sealing

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson, et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (GHP) (4.0 COP, 20.5 EER) With Envelope Improvements. This measure combines a two-stage GHP with envelope improvements as a single package. As this package is a combination of two other measures, this document focused on documenting the results associated with this combination of technologies, with individual measure documents for two-stage GHPs and envelope improvements providing the information on the details of these measures. When the two technologies are combined, envelope improvements can modestly reduce energy consumption by a further 10%-15%, but also reduce the required size of the ground heat exchanger and heat pump by approximately 33% on average across all sites. The cost of installing envelope improvements in these homes is likely to be more than paid for by the reduction in equipment and drilling costs in these buildings for the majority of the stock.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

ResStock Measure Documentation: Residential Single-Stage Geothermal Heat Pump (3.8 COP, 18.6 EER)

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson et al. 2022). This documentation focuses on a single end-use savings shape measure: Residential Single-Stage Geothermal Heat Pump (GHP).?Single-stage GHPs are able to reduce energy consumption by 31% for the entire stock. Additional results provided below detail how savings changes for sections of the housing stock with different base heating fuel and in different climate zones, as well as the savings potential by state for both heating and cooling. Utility bills and electric panel impacts are also shown and discussed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Expert Meeting: Optimized Heating Systems Using Condensing Boilers and Baseboard Convectors

On August 11, 2011, in Denver, CO, a Building America Expert Meeting was held in conjunction with the Building America Residential Energy Efficiency Technical Update Meeting, to review and discuss results and future plans for research to improve the performance of hydronic heating systems using condensing boilers and baseboard convectors. A meeting objective was to provide an opportunity for other Building America teams and industry experts to provide feedback and specific suggestions for the planned research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Expert Meeting: Optimized Heating Systems Using Condensing Boilers and Baseboard Convectors

On August 11, 2011, in Denver, CO, a Building America Expert Meeting was held in conjunction with the Building America Residential Energy Efficiency Technical Update Meeting, to review and discuss results and future plans for research to improve the performance of hydronic heating systems using condensing boilers and baseboard convectors. A meeting objective was to provide an opportunity for other Building America teams and industry experts to provide feedback and specific suggestions for the planned research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗