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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Refining seasonal performance metrics for room air-conditioning in emerging markets: Integrating building simulations with real-world equipment performance data

Buildings significantly impact worldwide energy consumption, emphasizing the need to reduce the cooling energy demand, especially in warm climates. Minimum energy performance standards (MEPS) and seasonal performance metrics such as the Cooling Seasonal Performance Factor (CSPF) are crucial for improving room air conditioning (RAC) efficiency. However, challenges remain, particularly in emerging markets like Brazil, where seasonal performance metrics have recently been introduced. This study assesses the factors influencing country-level seasonal efficiency metrics and proposes a framework to refine these calculations by considering local climates and expected RAC usage in real-world households via building simulations. Key considerations include outdoor air temperature binning for different climates, RAC usage patterns (i.e., daytime and nighttime usages), envelope thermal performance of households, and urban heat island (UHI) effects. The results reveal that CSPF values can vary significantly based on climate conditions, with observed CSPF ranging from 4.10 to 11.59 Wh/Wh across 577 Brazilian climates. The inclusion of UHI effects led to a reduction in CSPF values by up to 29% during nighttime operations in hot urban areas. Additionally, building envelope efficiency showed contrasting impacts on RAC performance, with CSPFs reaching up to 15.35 Wh/Wh under specific optimized conditions. These findings highlight the need for transparent policymaking in RAC performance databases, facilitating the application of approaches like those proposed in this study and supporting diverse stakeholders in decision-making.

Bavaresco, Mateus↗
DOE OSTI2024

Advances in building data management for building performance standards using the SEED platform

Reducing energy consumption and greenhouse gas emissions in the built environment is a critical step in achieving emission goals to mitigate climate change impacts. Local, federal, and international jurisdictions are deploying several methods to reduce energy and emissions such as voluntary and mandatory benchmarking and building performance standards, requiring building owners to reach energy and emission targets. Jurisdictions leveraging benchmarking and building performance standards require knowledge of the buildings covered; which is a large task due to staffing constraints, limited information on building characteristics and tax parcel data, and the need for advanced data management techniques to align datasets. This paper describes an open-source platform's recent advances to create consistent taxonomies, identify erroneous data, enable auditability, and track building performance. The paper concludes with two use cases on how the platform has been used by jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. Furthermore, a case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Putting Data to Work (Final Technical Report)

The Putting Data to Work project was designed to pilot and summarize the use of building energy performance data to improve energy efficiency program design and delivery in DC and NYC. By doing this, the project aimed to expand the market for energy efficiency in multifamily and commercial buildings by producing a toolkit of resources for other jurisdictions to replicate the successes found in the project cities.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Building Performance Database API (BPD API) v2.1

The Building Performance Database (BPD) is the largest publicly-available source of measured energy performance data for buildings in the United States. It contains information about the building's energy use, location, and physical and operational characteristics. The BPD can be used by building owners, operators, architects and engineers to compare a building's energy efficiency against customized peer groups, identify energy efficiency opportunities, and set energy efficiency targets. It can also be used by energy efficiency program implementers and policymakers to analyze energy efficiency features and trends in the building stock. The BPD compiles data from various data sources, converts it into a standard format, cleanses and quality checks the data, and provides users with access to the data in a way that maintains anonymity for data providers. This software is the database and the Application Programming Interface (API). Users can utilize the BPD's data to develop their own applications using the API. Version 2.1 included a major update for multiple years of data and refactoring of code for faster queries.

Mathew, Paul↗
DOE OSTI2022

A Knowledge-based Framework for Building Energy Model Performance Verification

Building energy modeling (BEM) has been widely used by researchers, regulators, and engineers to quantify building energy performance. Quality assurance (QA) and quality control (QC) of the model's performance are essential parts of such analysis. Currently, QA/QC is done in a manual and ad-hoc manner, which is tedious, error-prone, and time-consuming when QA/QC a large number of models. To solve these challenges, we propose a a dAta-driveN buIlding perforMance verificATion framEwork (ANIMATE), which conducts automated output-based verification of building operations requirements (especially for time-series output-based verification of control requirements). While this framework was developed for verifying energy model performance, it can be extended for other applications such as BEM software testing and performance verification of real buildings in the field.

Chen, Yan↗
DOE OSTI2024

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Building Battery Energy Storage System Performance Data into an Economic Assessment

Economic assessments of battery energy storage systems (BESSs) rely on optimal dispatch methods to capture temporal interdependency of BESS operations and coupling among different grid applications. Many existing BESS economic assessment studies are based on a simplified first-order scalar model using BESS nameplate numbers. Such a method cannot accurately capture varying capabilities and nonlinear dynamics of a BESS, leading to inaccurate assessment results. This paper presents a practical implementation of performance data into the economic assessment of the BESSs recently developed within the Snohomish Public Utility District system in Washington State. Extensive testing conducted over multiple seasons is used to assess the technical performance and develop high-fidelity models of the BESSs. A dynamic programming algorithm is proposed to optimally dispatch the BESSs and assess the potential benefits from stacked value streams. The proposed method can be adapted for the economic assessment of other BESSs with different applications.

Wu, Di↗
DOE OSTI2023

Factors Influencing Building Demand Flexibility

The U.S. Department of Energy’s National Roadmap for Grid-interactive Efficient Buildings (GEB) acknowledged that building demand flexibility (DF) is both an important strategy to decarbonizing the buildings sector and an important resource for meeting the changing needs of the electrical grid such as improving grid reliability. However, understanding the complexity and uncertainties in real building field performance of DF strategies is a large gap hindering stakeholders on both grid and buildings side to make investments on deploying such strategies. The research work in this report intended to advance understanding of the variability and influential factors in building demand flexibility. Adding such knowledge based on lab testing results and measured performance data from real buildings is an important contribution. The report uses standardized metrics and methods to quantify DF performance from field-measured DF datasets of two significant building groups of big-box retail and medium office buildings to present the challenge of building DF variability in multiple dimensions. The report presents findings related to how several key factors influence building demand flexibility from implementing a common, cost-effective DF control strategy (i.e., adjusting zone temperatures). The findings are supported by full-scale lab testing, field data analysis and simulation research. The authors also provided application-oriented recommendations to stakeholders such as building aggregators, utility program design professionals, sophisticated building portfolio owners, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

BETTER Together

The Standard Energy Efficiency Data (SEED) and Building Efficiency Targeting Tool for Energy Retrofits (BETTER) platforms are both developed by the Department of Energy and work better together. SEED is a database to manage building characteristics and performance data from a variety of sources. BETTER provides simple energy efficiency measure analyses based on high level data about the building or portfolio of buildings. A demonstration of each platform and their integration will be provided. The inputs for BETTER are building type, floor area, location, utility data, and whether PV shall be included in the analysis. The BETTER analysis can be manually set up through the web application or data can be uploaded with a BuildingSync XML file either directly or through the API. SEED can be the source of this data and the data can be sent to BETTER through the SEED application after the BETTER API token has been entered. The benefit of utilizing SEED is that it has connections to many other sources of data such as ENERGY STAR Portfolio Manager, Audit Template, and Salesforce. Therefore, it is likely that a user of SEED will already have the required inputs for BETTER in SEED already and can create BETTER analyses across their whole portfolio in a couple mouse clicks. This is a major time savings and enables decision makers an easy path to identify buildings that should undergo more detailed audits or retrofit pathways.

ASHRAE↗
DOE OSTI2023

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m 2 ), 10.0 W/m 2 , 11.1 W/m 2 , 7.1 W/m 2 , and 4.7 W/m 2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m 2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. Here, the comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Measured Performance of Over the Range Microwave Range Hoods

The California residential building code requires kitchen exhaust ventilation to protect indoor air quality. The requirement can be met with a kitchen exhaust fan or a range hood that conforms to airflow and sound specifications based on certified, standard test results. Appliances that integrate an exhaust fan with an over the range microwave (OTRs) are popular for their space saving utility; but for years there were none with the certified performance data required for building code compliance. This project, initiated during that period, aimed to evaluate OTR models found in new California homes and compare their performance to range hoods that minimally met code requirements. The study aimed to expand on limited information available about OTR performance with a particular focus on measuring the fraction of cooktop-generated air pollutants that are captured and removed by the exhaust devices, a parameter called “capture efficiency”. Airflow and capture efficiency (CE) were measured in a simulated kitchen in Berkeley Lab’s FLEXLAB facility. Airflows were measured using several variations of a balanced-pressure flow method (Walker et al. 2001) including a protocol that had been used in the California Healthy Efficient New Gas Homes (HENGH) field study. Measurements were made for six OTRs observed in the field study, including three with certified test results (published after the start of this project) for airflow and sound. Measurements were also made on two standard range hoods with comparable airflows and costs to the OTRs, when accounting for the microwave functionality. CE was measured using the CO 2 emitted from burners while heating pots of water (POW) as a tracer and calculating the ratio of added CO 2 in the exhaust flow over the total CO 2 generated from burning fuel. Results show that OTRs generally met the California code requirements for airflow, which are the same as those of the residential ventilation standard of ASHRAE. It was determined that the field protocol used in HENGH study homes was biased low by ~14% on average. The CE performance of OTRs tested in this study were consistent with those tested under controlled conditions in prior studies, showing CE increasing with airflow and being higher for emissions occurring at the back cooktop burner(s) compared with front burner emissions. The measured CE covered a range of 40% to 85% for the front burners and 60% to 100% for the back burners. The relationship of CE to airflow for OTRs was within the range of those found for standard range hoods in this study and prior studies, with the key caveat that OTRs appear to have more consistent CE performance for emissions on the front burner.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Quality Control Methods for Advanced Metering Infrastructure Data

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗