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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comprehensive Analysis of Model Parameter Uncertainty Influence on Evaluation of HVAC Operation to Mitigate Indoor Virus: A Case Study for an Office Building in a Cold and Dry Climate

Simulation-based studies of HVAC operation to mitigate indoor virus have been conducted to understand tradeoffs between indoor air quality (IAQ) and energy consumption. However, the influence of model parameter uncertainty in these studies has not been systematically quantified, which is critical when providing guidance to building operators. To address this gap, we identify 20 model parameters for a typical medium office building system in a cold and dry climate that can influence IAQ and energy consumption for indoor virus scenarios. Next, the distributions of the parameter values are estimated from literature and a set of simulation samples for three representative days is generated that simultaneously sample all of the parameters from their distributions. Two HVAC virus mitigation strategies are studied: increased filtration using MERV 13 filters and increased ventilation, via supply of 100% outdoor air into buildings. The model parameter uncertainty leads to significant variability in the results, particularly for the IAQ because of the highly uncertain virus generation rate. Use of 100% outdoor air can be beneficial for some uncertain scenarios on the hot day, but shows less IAQ improvement and/or significant energy increases on the other days. The virus removal efficiency and pressure drop of the HVAC filter, fan efficiency, and internal heat gain are the most important parameters to determining the tradeoffs of the two strategies. Our results demonstrate how this model parameter uncertainty analysis methodology can provide practical guidance to building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Implementing Solar - Leased Retail Building

This case study describes a multi-site solar photovoltaic (PV) installation project undertaken by BJ’s Wholesale Club. It describes how BJ’s worked with their landlords to overcome the split incentive of the installation, ongoing operational requirements, roof maintenance, and vacancy/turnover at several locations in Massachusetts, Maryland, and New Jersey.

Commercial, retail, leased, solar, solar photovolt↗
DOE OSTI2017

Appraisal of Green and High-Performance Buildings

This case study describes Principal Real Estate’s participation in DOE’s appraisal working group, established to help understand and address energy-related challenges in the commercial real estate appraisal process.

appraisal, commercial real estate, valuation proce↗
DOE OSTI2015

Energy management study: A proposed case of government building

Align with the current needs of the sustainable and green technology in Malaysian construction industry, this research is conducted to seek and identify opportunities to better manage energy use including the process of understand when, where, and how energy is used in a building. The purpose of this research is to provide a best practice guideline as a practical tool to assist construction industry in Malaysia to improve the energy efficiency of the office building during the post-production by reviewing the current practice of the building operation and maintenance in order to optimum the usage and reduce the amount of energy input into the building. Therefore, this paper will review the concept of maintenance management, current issue in energy management, and on how the research process will be conducted. There are several process involves and focuses on technical and management techniques such as energy metering, tracing, harvesting, and auditing based on the case study that will be accomplish soon. Accordingly, a case study is appropriate to be selected as a strategic research approach in which involves an empirical investigation of a particular contemporary phenomenon within its real life context using multiple sources of evidence for the data collection process. A Government office building will be selected as an appropriate case study for this research. In the end of this research, it will recommend a strategic approach or model in a specific guideline for enabling energy-efficient operation and maintenance in the office building.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Options For Large-Scale Installation of Individual Heat Substations Based on International Best Practices

The purpose of this report is to provide recommendations for actions Ukraine could take to implement large-scale installation of IHSs as part of comprehensive district heating reform. The report discusses the importance of IHS in the district heating sector, then presents several case studies of approaches that European countries have taken to facilitate large-scale IHS installation in multi-apartment buildings. These case studies focus on the ownership and financing of IHS, as there are different models for how this can be done (the DH company can make the investments and recover the costs through the tariff, or building residents can make the investments themselves). They also include details on the approval process and technical specifications, as these can add additional barriers and time to the installation process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

How Do Electricity Pricing Programs Impact the Selection of Energy Efficiency Measures? - A Case Study with U.S. Medium Office Buildings

Building owners usually select energy efficiency measures (EEMs) by referring to return on investment (ROI). Current studies tend to apply static energy price to estimate ROI. However, more and more buildings are adopting dynamic electricity pricing programs. To understand how electricity pricing programs impact the selection of EEMs, this paper presents an analysis of the ROIs of EEMs under different pricing programs using U.S. medium office buildings as an example. Eight EEMs in four typical cities are selected as case studies. Considering five electricity pricing programs scenarios (one static program and four dynamic programs), EEMs are selected based on their ROIs. The main findings are: (1) The ROIs of EEMs change under different pricing programs. (2) In Honolulu, Buffalo, and Denver, replacing interior fixtures with higher-efficiency fixtures has a significantly higher ROI than the rest EEMs under all five pricing programs. However, the ROI of this EEM in Honolulu ranges from 28% to 47% for different pricing programs. (3) Similarly, in Fairbanks, replace heating coil with higher-efficiency coil produce higher ROI than the rest under all five pricing programs. (4) For other EEMs, their ROI rankings vary according to electricity pricing programs.

demand response↗
DOE OSTI2019

Preparing for a City-Scale Building Energy Upgrade Analysis: A Case Study for New York City

The purpose of this document is to offer an overview of the value of detailed building stock energy-efficiency analysis and to provide a road map of actionable steps New York City (NYC) can take to prepare for such a study. There are a variety of approaches to building stock energy-efficiency analysis. This document focuses on the approach taken by the open-source ResStock (TM) and ComStock (TM) tools, because NYC is interested in learning how it can leverage DOE investment in this area. Although this document was prepared specifically for use by NYC, and the examples highlight application in the NYC context, the steps are written to be generalizable so that other cities interested in building stock analysis can use this as a framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
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 OSTI2026

Battery Energy Storage System (BESS) Licensing and Permitting [Slides]

This presentation provides an overview of the licensing, permitting, and regulatory frameworks governing Battery Energy Storage Systems (BESS) across multiple international contexts. It outlines foundational permitting considerations - including safety standards, siting requirements, regulatory and environmental obligations, and interconnection processes - alongside typical pathways for installation, approval, and inspection. The presentation summarizes key technical standards such as NFPA 855 and IEEE guidelines, and emphasizes best practices for safe and effective deployment. A series of detailed case studies from South Africa, Chile, Mexico, Brazil, Costa Rica, Honduras, and the Dominican Republic illustrates how different countries structure approval authority, evaluate environmental impacts, and integrate BESS into national grid systems. The deck concludes with insights on emerging challenges, including evolving regulatory frameworks, financial viability, and the need for pricing mechanisms that accurately value storage services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Sustainability and affordability of building electrification: A state-by-state holistic approach for multifamily buildings

This paper explores the evolving narrative of building electrification, considering its potential to solve climate change. Previous research has predominantly focused on hybrid renewable energy portfolios and increasing home pricing premiums, overlooking the perspectives of renters burdened by housing costs. Moreover, existing studies have primarily examined the effects of electrification on single-family homes and case studies, neglecting multifamily buildings and their relationship with the grid. This comprehensive study leverages calibrated building energy models to address these gaps and evaluates sustainability through carbon dioxide emissions and affordability through economic performance. The findings demonstrate significant progress in electrification since 2017, with nearly all states showing decreased energy usage in the electrified models. Environmentally, twenty-two states perform better or comparably with the electrified models in the most recent study. Economically, challenges persist, but a sensitivity analysis demonstrates how results could improve soon. Furthermore, the study discusses the outlook of electrified multifamily buildings, considering ongoing decarbonization plans for the electric grid. In three case studies, electrification demonstrates improvements in over half of the states by 2026. Overall, the research highlights the importance of expanding analysis to include multifamily buildings and emphasizes the positive impact of electrification as a viable energy, environmental, and economic solution.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Modeled Performance of the Hathaway Solar Patriot house - Washington D.C

This Dataset contains field research raw data, analysis spreadsheet, photos, and final report from the Hathaway Solar Patriot House Building America Case Study project. This dataset details the monitored and modeled performance of a solar home outside of Washington, D.C. We modeled the home energy performance using DOE 2.2, performed numerous short-terms tests on the home, and monitored its occupied performance for 29 months. The home used modular construction, solar water heating, a ground-coupled heat pump, efficient appliances, and compact fluorescent lighting to reduce its energy consumption by 35% compared to the Building America research benchmark home. The addition of 6kW of photovoltaics (PV) increased the savings to 67% compared to the Building America research benchmark. A more efficient shell to reduce space conditioning loads would have brought the home closer to its zero energy goals. However, even with efficient lighting and appliances, the lights, appliance and plug loads were a significant energy consumer. About 4 kW of PV was required to meet the needs of these loads alone. To achieve the zero energy goal with no further efficiency increases, the Hathaway house would need about 2.6 kW of PV in addition to the 6.0 kW it now has. Applying advanced efficiency measures available or being developed, such as heat-recovery ventilation, superinsulation, and electrochromic windows, could reduce the heating, cooling, and domestic hot water (DHW) energy use to less than 1700 kWh per year, an 88% reduction in these loads from the Building America Benchmark. At this efficiency level, the appliance and plug loads come to dominate energy consumption and account for nearly 70% of the total energy use. This analysis points out that even with highly effective energy-savings technologies (pushed beyond levels currently practical), whole-house energy use reduction by efficiency measures is only about 60% without also reducing the energy use of appliances and plug loads largely considered outside the designers jurisdiction.

Array↗
DOE OSTI2019

Performance analysis of absorption thermal energy storage for distributed energy systems

In recent years, distributed energy systems (DES) have attracted worldwide attention. Distributed generation unit (DG) in DES usually works under part load during night, which results in low efficiency and the waste heat of DG cannot be fully utilized. This study proposes a novel absorption thermal energy storage system together with electric energy storage for distributed energy systems. The proposed absorption thermal energy storage system which is a combination of absorption chiller and liquid storage tanks, has a higher energy storage density. During off-peak hours, the extra electricity of DG can be used by electric chillers (EC) and the extra waste heat of DG is stored by the proposed absorption thermal energy storage system. The stored thermal energy is released in peak hours to meet the cooling loads of buildings. A case study of DES in a campus under cooling-dominated climate is conducted to evaluate the performance of the proposed system. The results in a typical summer day indicate that the DG utilization rate increases from 80% to 92.9%, meanwhile the required capacities of electric chillers can be obviously reduced. The operating cost of DES also reduces by 12.9% compared with the DES without energy storage. Through appropriate operation strategy, off-the-grid operation for DES can be achieved without energy waste by applying the proposed energy storage method.

Wang, Lingshi↗
DOE OSTI2023

A Bayesian model for multivariate discrete data using spatial and expert information with application to inferring building attributes

When modeling sparsely observed multivariate data, strong prior information elicited from experts can be used to bolster predictive accuracy and counteract sampling bias. Similarly, modeling autocorrelation in space can help make use of co-occurrence patterns present in many types of spatial data. To make use of both expert prior information and spatial structure, we propose a novel graphical model for a spatial Bayesian network developed specifically to address challenges in inferring the attributes of buildings from geographically sparse observational data. This model is implemented as the sum of a spatial multivariate Gaussian random field and a tabular conditional probability function in real-valued space prior to projection onto the probability simplex. This modeling form is especially suitable for the usage of prior information in the form of sets of atomic rules obtained from experts. To perform inference with missing data, we implement a Markov chain Monte Carlo scheme composed of alternating steps of Gibbs sampling of missing entries and Hamiltonian Monte Carlo for model parameters. A case study in building attribution is presented to highlight the advantages and limitations of this approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quantifying and Monetizing Renewable Energy Resiliency

Energy resiliency has been thrust to the forefront by recent severe weather events and natural disasters. Billions of dollars are lost each year due to power outages. This article highlights the unique value renewable energy hybrid systems (REHS), comprised of solar, energy storage, and generators, provide in increasing resiliency. We present a methodology to quantify the amount and value of resiliency provided by REHS, and ways to monetize this resiliency value through insurance premium discounts. A case study of buildings in New York City demonstrates how implementing REHS in place of traditional backup diesel generators can double the amount of outage survivability, with an added value of $781,200. For a Superstorm Sandy type event, results indicate that insurance premium reductions could support up to 4% of the capital cost of REHS, and the potential exists to prevent up to $2.5 billion in business interruption losses with increased REHS deployment.

14 SOLAR ENERGY↗