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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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Moisture management of high-R walls is important to ensure optimal performance. This case study, developed by Building America team Building Science Corporation, focuses on how eight high-R walls handle the three main sources of moisture—construction moisture, air leakage condensation, and bulk water leaks.
Case study describes how the Byron G. Rogers Federal Building replaced existing T12 and T8 luminaires with LED dedicated troffers with advanced controls. Together these measures cut lighting energy use by nearly 60% in the 18-story, 791,000-square-foot facility. The new lighting control system provides automated dimming of lights down to 0% output when daylight provides adequate light levels. The project earned GSA an award for exemplary performance from the Interior Lighting Campaign in 2016.
Our paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. Lastly, our paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches.
A case study was conducted on 44 residential homes using both traditional house wrap and ZIP System systems to measure the overall airtightness and compare estimated energy usages. The labor, material, and overhead and profit (O&P) costs were analyzed and used to determine the optimal choice for long-term benefits in terms of cost and performance. The impact of insulation installation is considered a key factor in improving the strategy of reducing energy consumption. Improved installation practices can affect the airtightness of common wall assemblies to reduce the building energy performance gaps and provide insight on how to allocate resources better. A framework was developed to analyze operational costs and building energy performance to address how installation quality is a factor in the return of investment in building construction for heating and cooling systems within the thermal envelope. With this methodology, aggressive energy performance goals will be met while balancing the tradeoff between installation techniques and building systems efficiency based on the introduced probabilistic investment return (PIR) metric.
Highlights: • Impact assessment comparisons for various construction activities are presented. • Impacts of activities are compared from global, regional and local perspectives. • Excavation related activities have the highest POFP impact contributions at local level. • Material related activities have the highest GWP contribution. - Abstract: Different construction activities may indicate distinct environmental impacts due to their uniqueness. Ability to assess and compare the environmental impacts from different construction activities can aid the process of minimising emissions at different building construction processes. The study presents a comparative impact assessment methodology to evaluate environmental impacts at different activities during the building construction stage. Significant impact related construction activities for five major impact categories namely global warming potential (GWP 100), acidification potential (AP), Eutrophication potential (EP), Photochemical oxidation formation potential (POFP) and Human toxicity potential (HTP) are compared from the global, regional and local perspectives. A case study of a residential building in Australia is used to demonstrate the application of the functions of the developed method. The results of the case study indicated that the method can be effectively used to compare environmental impacts of different construction activities at different geographical perspectives considered. The method can be used by designers and contractors in comparing impacts of various construction activities to identify the most emission effective construction processes.
The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.
Battery Energy Storage Systems (BESS) are emerging as critical assets for enhancing grid reliability, integrating renewable energy, and enabling system flexibility. Yet, the regulatory and financial frameworks that determine how BESS projects are compensated vary widely across jurisdictions. This presentation explores international case studies - from Honduras, Costa Rica, Chile, South Africa, Mexico, and Brazil - to illustrate how tariff design and payment structures are evolving to support large-scale BESS deployment. The cases highlight a range of ownership and revenue models, including cost-of-service mechanisms, energy and capacity payments, and market-based arbitrage, as well as hybrid approaches under development. The discussion will examine key challenges such as defining remuneration for ancillary services, addressing double charging, and accounting for efficiency losses and degradation over time. By comparing experiences across markets, the presentation identifies emerging best practices for valuing BESS and designing tariffs that align technical performance with economic incentives, providing insights for regulators, utilities, and policymakers pursuing storage integration.
The ARIES Collaborative, a U.S. Department of Energy Building America research team, partnered with NeighborWorks America affiliate Homeowners' Rehab Inc. (HRI) of Cambridge, Massachusetts, to study improvements to the central hydronic heating system in one of the nonprofit's housing developments. The heating controls in the three-building, 42-unit Columbia Cambridge Alliance for Spanish Tenants housing development were upgraded. Fuel use in the development was excessive compared to similar properties. A poorly insulated thermal envelope contributed to high energy bills, but adding wall insulation was not cost-effective or practical. The more cost-effective option was improving heating system efficiency, which faced several obstacles, including inflexible boiler controls and failed thermostatic radiator valves. Boiler controls were replaced with systems that offer temperature setbacks and one that controls heat based on apartment temperature in addition to outdoor temperature. Utility bill analysis shows that post-retrofit weather-normalized heating energy use was reduced by 10%-31% (average of 19%). Indoor temperature cutoff reduced boiler runtime (and therefore heating fuel consumption) by 28% in the one building in which it was implemented. Nearly all savings were obtained during night which had a lower indoor temperature cut off (68°F) than day (73° F). This implies that the outdoor reset curve was appropriately adjusted for this building for daytime operation. Nighttime setback of heating system supply water temperature had no discernable impact on boiler runtime or gas bills.
The report offers several use cases and case studies of electrification in buildings and industry: air source heat pumps for space heating, zero net energy buildings, electric water heaters and demand response, electric arc furnaces, and electric boilers. Finally, the report suggests several areas for further research to better understand and advance beneficial electrification.
Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.
In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. Furthermore, the presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.
The Benchmark Datasets Development and Applications project is a three-year collaboration between the National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Lawrence Berkeley National Laboratory. The project seeks to collect and curate high-resolution, well-calibrated time series of building operational and indoor/outdoor environmental data, which are crucial to understanding and optimizing building energy efficiency performance and demand flexibility capabilities as well as benchmarking energy algorithms. Project outcomes include approximately twelve high-fidelity building datasets, enhanced data representation tools, and four case studies to illustrate example applications. The goal of these case studies is to define and execute analyses that demonstrate how one or more datasets collected through this project can address a data gap or challenge historically faced by building stakeholders. This technical paper summarizes the findings of one of these case studies, in which we studied the operational efficiencies of the central cooling system at NREL. We looked at three years of data from the three chillers in the Field Test Laboratory Building (FTLB), from 2019 to 2021, to compare equipment operation and demand throughout the time period. Our analysis indicates that all three chillers are operating at or below the optimal loading conditions for most of the operation time, and thus there was no efficiency drop due to loading of the chillers at full capacity. Our recommendation is that no chiller capacity increase is needed; instead, the central plant could benefit from adopting advanced control logics for optimal sequencing of chillers during part load operations. Analysis of adding chilled water thermal storage to the central plant indicated 34% savings in demand cost and 24.5% savings in total cost (energy consumption and demand charge cost). The payback period is estimated to be 11-22 years with an assumed TES cost of $\$$100-$200 per ton. This case study shows how a selected dataset is used to solve a practical building problem - learning the operational status of its components, analyzing the effectiveness of a proposed new technique, and aiding decision-making for the building operations and maintenance team.
Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.
Office buildings represent a significant portion of the commercial sector and are ideal candidates to lead the market towards achieving zero energy or zero energy ready buildings. The variety of sizes and uses for office buildings make them challenging to determine set design guidance that can be deployed across the market. A project committee for ASHRAE’s Zero Energy - Advanced Energy Design Guide (ZE-AEDG) for small and medium office buildings , comprised of members from five major national associations with market-pull in commercial buildings, created design guidance for achieving zero energy office buildings for new construction and major retrofit. This design guidance focuses on the process, providing a pathway for both office owners and design teams to achieve zero energy status. Energy use intensity targets range from 16 kBtu/ft²·yr to 36 kBtu/ft²·yr for the different climate zones were established such that office buildings can be “zero-ready,” that is, having low energy consumption that can easily be offset with renewables, preferably on-site. The design guidance was rooted in extensive energy modeling, as well as case studies of actual office buildings that have achieved high levels of energy performance and met zero energy or zero energy ready status. A unique aspect of the zero-energy guide is that it is not compared against a baseline but focuses on achieving an absolute target. The paper presents process of creating the targets, the selection of strategies to achieve the targets, and sample case studies that show how the cost-sensitive solutions are achievable for the design community. It also presents outreach strategies to deploy market achievable zero energy measures.
This software package contains a simple calculator that can be used to estimate savings from implementing a retrofit of HVAC controls to ASHRAE Guideline 36 in a multi-zone office building. Implemented in Excel, the calculator has a user-interface front-end to configure parameters of the case study, such as location, building size, and existing control strategies, and a back-end containing results from a large number of pre-run annual energy simulations. The software package also contains the simulation models and associated pre- and post-processing scripts to generate these results.
Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Furthermore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.
Highlights: • The safe management of asbestos is a matter of considerable importance. • There is a lack of literature concerning the development of a program for ACBMs. •A management program for the optimal disposal of ACBMs has been developed. • This program is also available in the event of a post-disaster. - Abstract: The safe management and disposal of asbestos is a matter of considerable importance. A large number of studies have been undertaken to quantify the issue of waste management following a disaster. Nevertheless, there have been few (if any) studies concerning asbestos waste, covering the amount generated, the cost of disposal, and the degree of hazard incurred. Thus, the current study focuses on developing a program for the management of Asbestos Containing Building Materials (ACBMs), which form the source of asbestos waste in the event of a disaster. The study will also discuss a case study undertaken in a specific region in Korea in terms of: (1) the location of ACBM-containing buildings; (2) types and quantities of ACBMs; (3) the cost of ACBM disposal; (4) the amount of asbestos fiber present during normal times and during post-disaster periods; (5) the required order in which ACBM-containing buildings should be dismantled; and (6) additional greenhouse gases generated during ACBM removal. The case study will focus on a specific building, with an area of 35.34 m{sup 2}, and will analyze information concerning the abovementioned points. In addition, the case study will focus on a selected area (108 buildings) and the administrative district (21,063 buildings). The significance of the program can be established by the fact that it visibly transmits information concerning ACBM management. It is a highly promising program, with a widespread application for the safe management and optimal disposal of asbestos in terms of technology, policy, and methodology.