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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Experimental and data-driven characterization of window-induced air leakage in residential buildings

Windows contributes up to 40% of envelope heat losses and around 9% of total building energy consumption due to air leakage. In the U.S., 48 million homes still use single-pane windows. Although the U.S. has an estimated 1.4 billion windows in its building stock and about 24 million windows are installed annually, only around 29 million individual window replacements (∼2%) occur each year. To address this gap, this study generates empirical evidence by (1) evaluating the contribution of windows to whole-building air leakage in 20 residential buildings using blower door tests before and after window replacement and (2) assessing whether building and window characteristics influence the measured change. Most simulation studies assume that replacing windows not only lowers the U-factor but also reduces air leakage by 10–20%. However, this assumption lacks empirical validation, highlighting the need for experimental analysis of air leakage specifically associated with windows. Using blower door tests in accordance with ASTM E779–19, the results indicated an average reduction in air infiltration of 6.1% within the range of 0.5–19.30% across all buildings and no significant correlations were found between air leakage improvements and any building/window characteristics. This research aims to help homeowners, and energy modelers to provide empirical data on importance of upgrading to more energy-efficient windows, supporting energy-efficient building standards.

Air leakage↗
DOE OSTI2019

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, contractors, and building owners analyse diverse options regarding the planning and management of energy consumption in buildings. Furthermore, recent advances in data processing and computing have led to the development of sophisticated machine learning algorithms that can learn from large datasets, e.g., sensor data from buildings, and use them to develop building-specific adaptive and automatic energy controllers. Control algorithms, such as deep reinforcement learning can tune themselves, are model-free, and economical to implement. In this paper, we introduce an integrated simulation environment that combines CitySim, a fast building energy simulator, and TensorFlow, a platform for efficient implementation of advanced machine learning algorithms. The integration is achieved via Keras - an API for TensorFlow - and a set of text and csv files for data transfer between the applications. This new environment will allow researchers to investigate novel learning control algorithms, and demonstrate their robustness and potential for diverse applications in the built environment. We present two case studies for energy savings and demand response, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Buildings.Occupants : A Modelica package for modelling occupant behaviour in buildings

Energy-related occupant behaviour is crucial to design and operation of energy and control systems in buildings. Occupant behaviours are often oversimplified as static schedules or settings in building performance simulation ignoring their stochastic nature. The continuous and dynamic interaction between occupants and building systems motivates their simultaneous simulation in an efficient manner. In the past, simultaneous simulation has relied on co-simulation approaches or customized source code changes to building simulation programmes. This paper presents Buildings. Occupants, an open-source package implemented in Modelica, for the simulation of occupant behaviours of lighting, windows, blinds, heating and air conditioning systems in office and residential buildings. Examples were presented to illustrate how the models in the Occupants package are capable to simulate stochastic occupant behaviours. Furthermore, the major contribution of this work is to introduce the equation-based modelling approach to simulate occupant behaviours in buildings and to develop an open-source Occupants package in the Modelica language.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Electrical Measurement and Verification of Energy in DC Buildings

Today's selection of DC buildings features a diverse set of electrical topologies and turnkey solutions, and each has specific design trade-offs and optimizations. Designers desperately need standardized metrics and procedures for measurement and verification (M&V) to analyze and compare the advantages of each DC solution to traditional AC building networks. This work develops the Measurement-Informed Modeling (MIM) method, which can be used to determine full-building efficiency and energy savings. The MIM M&V procedure develops a building model, and refines the model with metered data. This work demonstrates the MIM method by measuring the full-building efficiency of two DC buildings operated by the Institute of Building Research in Shenzhen, China. The MIM procedure can ultimately be used to compare and improve the efficiency of various DC topologies.

buildings↗
DOE OSTI2018

A Learning Framework for Control-Oriented Modeling of Buildings

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

buildings↗
DOE OSTI2020

Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain

The total energy consumed by buildings takes up to 40% of U.S. energy use, in which a large portion is contributed by commercial buildings. Building performance optimization is desirable but requires accurate building models with uncertainties taken into account. This paper proposes a novel probabilistic modeling method using Logistic Regression Markov Chain (LRMC). The LRMC model enhances the performance of traditional Markov Chain (MC) models by adopting time-variant transition matrices calibrated using logistic regression with exogenous inputs. Compared with existing building models, the proposed model produces accurate multi-step modeling results with full probability distribution. The proposed probabilistic building model is tested using actual commercial building measurements and modeling performance is evaluated with two probabilisitc metrics. The results show that the LRMC model has higher accuracy than traditional MC model and Logistic Regression (LR) model in that it yields lower error scores under both evaluation metrics.

Building modeling↗
DOE OSTI2013

Industry Research and Recommendations for Small Buildings and Small Portfolios

Small buildings have been left behind in the energy efficiency marketplace because financial and technical resources have flowed to larger commercial buildings. DOE's Building Technologies Office works with the commercial building industry to accelerate the uptake of energy efficiency technologies and techniques in existing and new commercial buildings (DOE 2013). BTO recognizes the SBSP sector'spotential for significant energy savings and the need for investments in resources that are tailored to this sector's unique needs. The industry research and recommendations described in this report identify potential approaches and strategic priorities that BTO could explore over the next 3-5 years that will support the implementation of high-potential energy efficiency opportunities for thisimportant sector. DOE is uniquely positioned to provide national leadership, objective information, and innovative tools, technologies, and services to support cost-effective energy savings in the fragmented and complex SBSP sector. Properly deployed, the DOE effort could enhance and complement current energy efficiency approaches. Small portfolios are loosely and qualitatively defined asportfolios of buildings that include only a small number of small buildings. This distinction is important because the report targets portfolio owners and managers who generally do not have staff and other resources to track energy use and pursue energy efficiency solutions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Diagramming Transactive Building Business Cases: Using Principles of e3 Value to Document Valuation Studies

Energy management in buildings is becoming more transactive. Pacific Northwest National Laboratory and the U.S. Department of Energy Building Technologies Office recently defined innovative use cases wherein market-like mechanisms are used to manage energy within buildings, between buildings, and between buildings and third-party entities, such as power utilities. A next step toward defining a set of transactive use cases in the buildings domain is to carefully diagram the corresponding business cases to capture details of transactions among all stakeholders and their economic value propositions. The principles of e3-value diagramming are applied in this report toward creating business value diagrams. These principles are extended to be consistent with Universal Modeling Language use-case diagrams. Example diagrams are presented for a subset of buildings-domain use cases that were introduced in an earlier Pacific Northwest National Laboratory report. The diagrams are intended to clearly represent an understanding of the transactions through which individual entities accumulate value in their respective use cases, and the diagrams should therefore support economic valuation studies. The report reviews some of the foundational principles of e3 value and includes authors’ insights concerning the formulation of these diagrams using Universal Modeling Language as a more systematic modeling approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Technology for Building Systems Integration and Optimization – Landscape Report

BTO's Commercial Building Integration (CBI) program helps advance a range of innovative building integration and optimization technologies and solutions, paving the way for high-performing buildings that could use 50-70% less energy than typical buildings. CBI’s work focuses on early stage technology innovation, with an emphasis on how components and systems work together and how whole buildings are integrated and optimized. This landscape study outlines the current body of knowledge, capabilities, and the broader array of solutions supporting integration and optimization in commercial buildings. CBI seeks to support solutions for both existing buildings and new construction, which often present very different challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Human-in-the-loop Sensing and Control for Commercial Building Energy Efficiency and Occupant Comfort

Most of the existing heating, ventilation and air conditioning (HVAC) systems in commercial buildings operate in a conservative manner by assuming maximum occupancy in each room during pre-specified periods of the week, leading to significant energy being wasted as rooms are over-conditioned compared to the actual requirements of the occupants. Though critical, our understanding of occupancy patterns and thermal comfort needs of the occupants in commercial buildings is lacking and it is well known that both of these quantities are stochastic and time-varying, thus requiring sensing solutions to estimate them. This project had the goal of designing, implementing and evaluating a hardware and software solution to ameliorate this challenge. In particular, a depth camera (one whose pixels reveal distance from the camera as opposed to color values) placed on doorways is used to detect entrance and exit events from thermal zones in the building, and thereby estimate their occupancy levels. This information is then fed to a novel control algorithm that can, through interactions with the HVAC system, learn how to provide control inputs that maximize comfort and minimize energy waste. The resulting system represents a significant improvement over existing controllers for commercial HVAC systems and allowed us to improve our understanding of the design of future human-in-the-loop control solutions. For this solution to be feasible, the project had target metrics for its performance and cost. In particular, entrance and exit events for occupants moving about the building would need to be detected with an accuracy higher than 97%; and the resulting control inputs derived from this information would need to lead to approximately 10% energy savings compared to a schedule-based controller. Furthermore, regarding the final hardware design, the project had a target bill of materials (BOM) cost for the sensing solution of less than US$200 per unit while using less than 25W of power on average. All of these target metrics were met or exceeded by our final proposed solution. We performed evaluations by deploying the system in over 20 rooms of different types across 6 commercial buildings in Pittsburgh, PA over the course of three years, and performing targeted controlled experiments to test its performance along the different metrics. The human-in-the-loop control solutions (both hardware and software) developed through this project are expected to lead to significant improvements in the comfort and energy efficiency of HVAC systems used in commercial buildings. The insights we developed through the project pave the way to HVAC systems that can condition interior spaces according to their real-time utilization and the thermal comfort needs of the occupants, thereby reducing energy use. They also open up a new learning-based way of configuring HVAC controllers without having to manually fine-tune them for each building. These innovations can significantly increase the adoption of novel control solutions by the industry and thereby save resources and reduce costs of operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Streamlined Approach for Environmental Restoration (SAFER) Plan for Corrective Action Unit 572: Test Cell C Ancillary Building and Structures - Nevada National Security Site, Nevada with ROTC 1

This Streamlined Approach for Environmental Restoration (SAFER) Plan addresses the actions needed to achieve closure for Corrective Action Unit (CAU) 572, Test Cell C Ancillary Building and Structures, identified in the Federal Facility Agreement and Consent Order (FFACO). CAU 572 comprises the following corrective action sites (CASs) located in Area 25 of the Nevada National Security Site: CAS 25-33-01, Building 3220, Equipment Building; CAS 25-33-02, Building 3230, Motor Drive Building; CAS 25-33-03, Building 3231, Pump House; CAS 25-33-04, Building 3232, Cryogenic Evaluation Lab; CAS 25-99-22, Ancillary Facilities. This plan provides the methodology for field activities needed to gather the necessary information for closing CAU 572. There is sufficient information and process knowledge from historical documentation and investigations of similar sites regarding the expected nature and extent of potential contaminants to recommend closure of CAU 572 using the SAFER process. Additional information will be obtained by conducting a field investigation before selecting the appropriate corrective action. It is anticipated that the results of the field investigation and implementation of corrective actions will support a defensible recommendation that no further corrective action is necessary. The purpose of the corrective action investigation (CAI) will be to document and verify the adequacy of existing information; to affirm the decision for either clean closure, closure in place, or no further action; and to provide sufficient data to implement the corrective action. The actual corrective action selected will be based on characterization activities implemented under this SAFER Plan. If it is determined that complete clean closure cannot be accomplished during the CAI, then a hold point will have been reached and the Nevada Division of Environmental Protection (NDEP) will be consulted to determine whether the remaining contamination will be closed under the alternative corrective action of closure in place. This will be presented in a closure report that will be prepared and submitted to NDEP for review and approval.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Building Stock Segmentation Cluster Development: Technical Reference Document

The building stock in the United States (U.S.) varies significantly as a function of several macro variables such as: climate, building type, vintage, and density. These variables change across the U.S. and can also significantly impact energy usage of the individual buildings and overall stock. For example, the square foot density and building type varies by several orders of magnitude from Manhattan to the eastern plains of Colorado. The diversity in energy use of the building stock of different areas of the U.S. is significant, and as a result, analyses that require localized results need to consider the relevant geography and the current makeup of the building stock. This document discusses the development and implementation of a stock clustering algorithm that produces a technically rigorous, consistent, and repeatable collection of geographies which are used as the basis for localized analysis. This framework considers the impact of built environment density, diversity, and climate in creating groupings of counties that create a far more nuanced analysis framework than national averages. Clustering of counties together represents a similarity of building characteristics and climate zone.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Residential Building Stock Characterization in Palm Beach County, Florida

This building stock characterizations is intended to help Palm Beach County (PBC) Office of Resilience (OOR) and municipalities composing the Municipal Resilience Partnership prioritize building energy efficiency investments to reduce energy costs and increase resilience for the county's most vulnerable residents. The analysis utilizes NREL’s ResStock model to characterize PBC’s residential building stock, including building type, renter/owner status, size (square footage), age of buildings (vintage), HVAC system types, and, for multi-family buildings, number of units. Building energy efficiency, weatherization, and electrification upgrade packages were assessed for approximate cost, customer bill-savings, and emissions reductions potential and findings will help guide OOR financial assistance program design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Cost-Effective Thermally Activated Building Systems to Support a Power Grid System With High Penetrations Of As-Available Renewable Energy Resources

With a goal to reduce the energy cost for building operation as well as to support renewable energy sources (RES) for the power grid reliability, quality, resilience, and dispatchability, this project developed and demonstrated a novel thermally activated building envelope system that integrates Phase Change Material (PCM)-based Thermal Energy Storage (TES) and the hydronic activation into the building envelope. The main objectives of this study are: 1) to design and laboraorty-test a novel thermally activated building envelope system that integrate PCM-based TES and the hydronic activation into building envelope, and 2) to exploit this new system to significantly reduce the energy cost of operating buildings and manage and support renewable energy sources (RES), e.g., solar and wind for the power grid reliability, quality, resilience, and dispatchability. To achieve the project objectives, a new low-cost, fire-retardant PCM packaging technology (CenoPCM) was developed specifically for high-volume building applications.

25 ENERGY STORAGE↗
DOE OSTI2025

2024 Buildings Technology Baseline: Dataset Documentation

The Buildings Technology Baseline is a curated and regularly updated dataset of current and projected performance, retail, and installed price data for all major building energy technologies needed to enable cost/benefit analyses. Building technology analyses require an up-to-date understanding of installation costs and cost-effectiveness of key building energy efficiency technologies. The dataset was assembled by Guidehouse during fiscal year 2024. Data was gathered from the 2024 National Residential Efficiency Measures Database (NREMDB), the 2023 Energy Information Administration Updated Buildings Sector Appliance and Equipment Costs and Efficiencies ("EIA Building Data Report"), DOE Lighting Market Model, the 2023 RSMeans database, and the 2020 Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics ("GEB Data Report"), Lawrence Berkeley National Laboratory, various literature, as well as new data from online retailers, stakeholder interviews, and contractor databases in 2023 and 2024. The dataset has been reviewed by subject matter experts at NREL and DOE. The 2024 dataset release is intended to be a starting point for interested users to provide feedback. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Building-to-Grid Modeling Framework with a Case Study of Battery Systems

Coordinating electricity load shifting operations of multiple buildings has the potential to achieve significant benefits for both building owners and grid operation. In support of this research, this paper develops a novel building-to-grid modeling framework, which uses EnergyPlus to develop physics-based building energy models and GridLAB-D to develop distribution-level grid models. The data exchanges between buildings and distribution-level grid are conducted using Python script and HELICS co-simulation platform. Then, by using this modeling framework, this paper conducts a case study of battery systems in a connected community, which consists of 10 multi-family buildings with appropriately sized batteries. The three scenarios, baseline operation, tolerant to price fluctuations, and opportune usage, are studied. The result displays that, by adopting onsite batteries, both electricity costs for building owners and aggregated peak power demands are reduced.

Building-to-grid, connected community, co-simulati↗
DOE OSTI2021

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Cost Control Best Practices for Net Zero Energy Building Projects: Preprint

For net zero energy (NZE) buildings to become the norm in commercial construction, it will be necessary to design and construct these buildings cost effectively. While industry leaders have developed workflows (for procurement, design, and construction) to achieve cost-effective NZE buildings for certain cases, the expertise embodied in those workflows has limited penetration within the commercial building sector. Documenting cost control best practices of industry leaders in NZE and packaging those strategies for adoption by the commercial building sector will help make the business case for NZE. Furthermore, it will promote market uptake of the innovative technologies and design approaches needed to achieve NZE. This paper summarizes successful cost control strategies for NZE procurement, design, and construction that key industry users (such as building owners, architects, and designers) can incorporate into their everyday workflows. It will also evaluate the current state of NZE economics and propose a path forward for greater market penetration of NZE buildings. By demonstrating how to combine NZE technologies and design approaches into an overall efficiency package that can be implemented at minimal (zero, in certain cases) incremental capital cost, the domain of NZE design and construction can be expanded from a niche market to the commercial construction mainstream.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗