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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Citizen-Led Community Innovation for Food Energy Water Nexus Resilience

Food-energy-water (FEW) resources are necessary for the function of multiple socio-natural systems. Understanding the synergies and trade-offs in the FEW nexus, and how these interconnections impact earth’s systems, is critical to ensure adequate access to these resources in the future; an essential component for achieving the Sustainable Development Goals (Scanlon et al., 2017). Although, over the last decade, the identification of FEW nexus complexities has increased at a global (IPCC, 2018; D’Orodico et al., 2018), national (Lant et al., 2019), and city scale (Rushforth and Ruddell, 2018), these findings are yet to be adequately translated into ‘on the ground’ action due a lack of technical and political capacity (Weitz et al., 2017). Specifically, local FEW systems have been overlooked in these analyses (Scanlon et al., 2017; Lant et al., 2019), thus leaving small and medium towns vulnerable due to a lack of data and inadequate FEW system management. Building on three years of field-tested FEW nexus research in the Ruddell Lab, we argue that participatory citizen science projects, such as our FEWSION for Community Resilience initiative, can bridge the data-policy gaps that exist within local FEW system management by: 1) providing last mile data on the FEW system, and 2) translating local data into evidence-based solutions at a grassroots level. Thus, we present a broadly applicable framework and call to action for local scale participatory citizen science to solve complex FEW nexus issues at a local, regional, and national scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Unique Building Identifier (UBID): Public Sector Implementation Guide

Buildings generate data throughout their lifecycle – about ownership & taxation, usage, zoning, code compliance, energy use, and retrofits. State and local governments collect this data after it flows through growing networks of people and systems. But collecting data is only half the battle; what’s really needed is information – the actionable insights that lead to successful policy outcomes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2023

Interpreting T-History Data on Supercooling and Phase Change Enthalpy of a Salt-Hydrogel Complex for Building Thermal Management

Phase change materials (PCMs) are promising materials for storing and discharging latent heat in buildings to regulate the thermal environment. Measurement of salt hydrate PCM thermal performance is often challenging because of supercooling and phase segregation. Though differential scanning calorimetry (DSC) has been widely used to measure thermal properties of various phase change materials, DSC uses milligrams of sample and is unrealistic for predicting large-scale materials performance. The T-history method [1], in contrast, handles higher volumes but the analysis typically requires a lumped capacitance assumption. Here, we report and analyze T-History data on a novel salt-hydrogel complex with high viscosity, that renders low Biot number sample preparation extremely challenging. We re-examine the standard T-history method and compared against DSC data in this complex material. We report details of an experimental setup, built with necessary additions to a controlled environmental chamber. We carefully examine the validity of the lumped capacitance assumption and suggest a modified analysis using a computational approach for reliable data. Experimental parameters such as ramp rate and temperature range of the environmental chamber are considered via computational modeling to understand the effect of ramp rate on supercooling [2]. Finally, we report thermal cycling experiments on the salt-hydrogel complex. Our modified approach to measuring supercooling and enthalpy of fusion at large scales is important in better understanding the performance of phase-change materials at scale for building thermal storage. References: [1] Marin, Jose M, Belen Zalba, Luisa F Cabeza, and Harald Mehling. “Determination of Enthalpy Temperature Curves of Phase Change Materials with the Temperature-History Method: Improvement to Temperature Dependent Properties.” Measurement Science and Technology 14, no. 2 (February 1, 2003): 184–89. https://doi.org/10.1088/0957-0233/14/2/305. [2] Safari, A., R. Saidur, F.A. Sulaiman, Yan Xu, and Joe Dong. “A Review on Supercooling of Phase Change Materials in Thermal Energy Storage Systems.” Renewable and Sustainable Energy Reviews 70 (April 2017): 905–19. https://doi.org/10.1016/j.rser.2016.11.272

thermodynamics↗
DOE OSTI2021

CO 2 Storage Site Screening Platform Development and CO 2 Storage Resource Analysis in SECARB Offshore Reservoirs Using SAS Viya

A major goal of the SECARB Offshore Partnership (DE-FE0031557) is to screen deep saline aquifers and hydrocarbon reservoirs in the central Gulf of Mexico for CO 2 sequestration and CO 2 -enhanced oil and gas recovery (EOR/EGR) and estimate the corresponding CO 2 storage resources for select reservoirs. CO 2 storage potential associated with offshore CO 2 -EOR is considerable and likely represents “low hanging fruit” for near-term CO 2 storage given the in-place infrastructure in the region. It is for these reasons that this assessment focuses on oil and gas fields. To this end, three major objectives have been completed and include (1) managing geological data derived from different sources, (2) building a reservoir screening platform for CO 2 storage, and (3) ranking the reservoirs based on the estimated CO 2 storage resources. The SAS ® Viya platform was used for data management and analytics. The Viya platform is a cloud service platform that provides data integration, data management, quick analytics, data visualization, machine learning functions, and application programming interfaces (APIs) for multi-programming languages. Different sources of data containing geologic information, reservoir properties, and EOR/EGR information were collected, cleaned, formatted, and loaded into the SAS ® Viya platform for evaluation. The major geological characteristics of both shelf and deep-water areas of the central Gulf were examined and compared to define the appropriate reservoir screening criteria. Next, a CO 2 storage site screening system was built in the SAS ® Viya platform with the pre-defined criteria. Finally, the CO 2 storage resources of the screened reservoirs were calculated and reported at the BOEM field level to identify fields with the highest estimated CO 2 storage resource. The fields with the largest total estimated CO 2 storage resource are located in the Mississippi Canyon protraction area. Due to proximity to the Mississippi Delta (indicative of less infrastructure) and large estimated CO 2 storage resources, future development activities may wish to focus efforts in the Mississippi Canyon protraction area.

02 PETROLEUM↗
DOE OSTI2023

Learning to Predict and Improve Build Successes in Package Ecosystems

Software has become increasingly complex, with a typical application depending on tens or hundreds of packages. Finding compatible versions and build configurations of these packages is challenging. This paper presents a method to learn the likelihood of software build success, and techniques for leveraging this information to guide dependency solvers to better software configurations. We leverage the heavily parameterized package recipes from the Spack package manager to produce a training data set of builds, and we use Graph Neural Networks to learn whether a given package configuration will build successfully or not. We apply our tool to the U.S. Exascale Computing Project’s software stack. We demonstrate its effectiveness in predicting whether a given package will build successfully. We show that our technique can be used to improve the solutions generated by dependency solvers, reducing the need for developers to find working builds by trial and error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data Centers in the Commercial Sector

This fact sheet summarizes some of the key needs, barriers, and opportunities related to data center management and operations in the Commercial Sector.

data center, data center management, commercial bu↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Equitable Energy Metrics for Integration into Building Performance Standard Tracking Platforms: Preprint

Building Performance Standards (BPS) are being adopted globally and in the United States of America, where 14 different states and jurisdictions have a policy in place and many others are under development (Department of Energy (DOE) 2023). Accurate and equitable data sources are essential to make informed decisions about focusing investment on upgrading buildings to meet jurisdictional goals. There have been multiple new tools developed related to Energy Equity and Environmental Justice (EEEJ) and the resulting datasets need to be integrated into large building port-folios for quick access and better scalability. Integrating EEEJ data in a user-friendly format can help decision makers more quickly assess impacts and analyze the multitude of potentially significant metrics for which there is not yet consensus. In the U.S. and Canada, many BPS ordinances rely primarily on ENERGY STAR Portfolio Manager (ESPM) to capture building characteristics and energy and water consumption data. These datasets can then be imported into city-specific building tracking tools like the Standard Energy Efficiency Data Platform (SEED). Crucially, BPS decision makers require an efficient means of identifying buildings in priority communities to effectively allocate resources and funding. This process must integrate seamlessly with existing jurisdictional toolsets for optimal utility. This paper will demonstrate, for the case of Washington D.C.'s (the District) data, a workflow that provides actionable data for building upgrade investment prioritization in disadvantaged communities.

BPS↗
DOE OSTI2023

Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO Practice

As awareness around building energy consumption increases, practitioners are encouraged to consider performance aspects regarding the built environment more closely and find ways to improve its efficiency. Improvements in building information modeling (BIM) and building performance simulation (BPS) tools present opportunities to facilitate information communication with a wider range of stakeholders. The building sector can benefit from the integration of performance informatics; however, there has been limited success in utilizing available technologies that promote data integration and management in favor of enriching our knowledge and understanding of buildings as artifacts of information. This phenomenon was investigated by conducting a survey, together with a review of relevant literature, to depict the relevant challenges and opportunities for the architecture, engineering, construction, and owner-operated (AECO) industry, as it undergoes digital transformation, as well as the working practices that have formed around them. It is argued that the current tools available to practitioners do not support effective data serialization between design and analytics processes, affecting the collaboration between team members. Lastly, a series of functional goals are proposed to support a higher level of reliability in the ways information is mobilized, by rethinking the technologies and methods for organizing information systems.

42 ENGINEERING↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Spanish Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Spanish translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (French Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the French translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Arabic Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Arabic translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Mandarin Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Mandarin translation of NREL/FS-5500-90691.

benchmarking↗