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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 OSTI2020

Proving the Business Case for Building Analytics

As building monitoring becomes more common, facilities teams are faced with an overwhelming amount of data. These data do not typically lead to insights or corrective actions unless they are stored, organized, analyzed, and prioritized in automated ways. Buildings are full of energy savings potential that can be uncovered with the right analysis. With analytic software applied to everyday building operations, owners are using data to their advantage and realizing cost savings through improved energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices

Buildings are becoming more data-rich. Building analytics tools, including energy information systems (EIS) and fault detection and diagnostic (FDD) tools, have emerged to enable building operators to translate large amounts of time-series data into actionable findings to achieve energy and non-energy benefits. To expedite data analytics adoption and facilitate technology innovation, building owners, technology developers, and researchers need reliable cost–benefit data and evidence-based guidance on deployment practices. This paper fulfills these needs with the energy use and survey data from a wide-ranging research and industry partnership program that covers thousands of buildings installed with analytics tools. The paper indicates that after two years of implementation, organizations using FDD tools and EIS tools achieved 9% and 3% median annual energy savings, respectively. The median base cost and annual recurring cost for FDD are USD 0.65 per square meter (m2) (USD 0.06 per square foot [ft2]) and USD 0.22 per m2 (USD 0.02 per ft2), and are USD 0.11 per m2 (USD 0.01 per ft2) and USD 0.11 per m2 (USD 0.01 per ft2) for EIS. The common metrics and analyses that are used in the tools to support the discovery of energy efficiency measures are summarized in detail. Two best practice examples identified to maximize the benefits of tool implementation are also presented. Opportunities to advance the state of technology include simplified data integration and management, and more efficient processes for acting on analytics outputs. Compared with previous efforts in the literature, the findings presented in this paper demonstrate the effectiveness of building analytics tools with the largest known dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Building Adapter: Automatic Mapping of Commercial Buildings for Scalable Building Analytics

This project creates new solutions for the manual metadata mapping problem: the costly process of creating a match between a building’s sensor data streams and the inputs of a building analytics engine. This goal is achieved by creating and improving techniques for metadata inference: automatically constructing new contextual information for sensing and control points based on the sensor point names and the raw time series values. The objective is to enable vendors to apply building analytics to 90% of buildings with no manual mapping, and to 10% of buildings with a 90% reduction in manual mapping. These targets are set for all types of metadata required by current analytics engines, including type, location, equipment type, and other relationships. The outcome of this project is a suite of solutions to the manual mapping problem collectively called the Building Adapter that allows vendors to apply analytics engines to new buildings at a significantly reduced cost.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Mortar: An Open Testbed for Portable Building Analytics

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1977

The design of a solar energy collection system to augment heating and cooling for a commercial office building

Analytical studies supported by experimental testing indicate that solar energy can be utilized to heat and cool commercial buildings. In a 50,000 square foot one-story office building at the Langley Research Center, 15,000 square feet of solar collectors are designed to provide the energy required to supply 79 percent of the building heating needs and 52 percent of its cooling needs. The experience gained from the space program is providing the technology base for this project. Included are some of the analytical studies made to make the building design changes necessary to utilize solar energy, the basic solar collector design, collector efficiencies, and the integrated system design.

Basford, R. C.↗
DOE OSTI2020

Can We Fix It Automatically? Development of Fault Auto-Correction Algorithms for HVAC and Lighting Systems

A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.

Lin, Guanjing↗
DOE OSTI2020

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

field testing↗
NASA NTRS1993

The use of analytical models in human-computer interface design

Recently, a large number of human-computer interface (HCI) researchers have investigated building analytical models of the user, which are often implemented as computer models. These models simulate the cognitive processes and task knowledge of the user in ways that allow a researcher or designer to estimate various aspects of an interface's usability, such as when user errors are likely to occur. This information can lead to design improvements. Analytical models can supplement design guidelines by providing designers rigorous ways of analyzing the information-processing requirements of specific tasks (i.e., task analysis). These models offer the potential of improving early designs and replacing some of the early phases of usability testing, thus reducing the cost of interface design. This paper describes some of the many analytical models that are currently being developed and evaluates the usefulness of analytical models for human-computer interface design. This paper will focus on computational, analytical models, such as the GOMS model, rather than less formal, verbal models, because the more exact predictions and task descriptions of computational models may be useful to designers. The paper also discusses some of the practical requirements for using analytical models in complex design organizations such as NASA.

Gugerty, Leo↗
NASA NTRSDate not supplied

Celestial Mapping System for Lunar Surface Mapping and Analytics

Celestial Mapping System (CMS) is a software platform to generate virtual 3D globe for celestial bodies within our solar system. Various layers are built on top of the virtual globe to provide visualization of high resolution imagery, enable precise measurements, build analytical capabilities and broad range of functionalities to assist planetary scientists and mission planners. CMS is built using OpenJDK 11 and will run on a wide variety of platforms such as Linux, Windows, OSX, etc. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The present focus of CMS is on developing lunar mapping tool kits to provide features such as - 3D first person view with zoom and navigational capabilities, realistic terrain visualization based on LRO data, measurement tools, Apollo landing site annotations, stereoscopic view, elevation profiles, line of sight analysis and many more. The application is developed to provide situational and domain awareness on Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on Lunar surface. CMS utilizes NASA WorldWind Java library and OpenGL to achieve high-performance rendering of data and measurements, and also adheres to OGC standards. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. Nomenclature is pulled from USGS Moon IAU2000 database, and lunar parameters are based of the standardized IAU2000 Moon ellipsoid. GDAL (Geospatial Data Abstraction Library) was used to modify and test the accuracy of datasets before integrating into the application. Our high-resolution global elevation model was compared with the LRO LOLA DEM elevation values and tested to ensure accuracy. Celestial Mapping System has several potential use cases for NASA including subsurface lava tubes visualization and analysis, soil analysis, resource visualization and representation on 3D globe.

GIS system↗
NASA NTRS2018

Towards Design Principles for Visual Analytics in Operations Contexts

Operations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics.

Lombeyda, Santiago↗
NASA NTRSDate not supplied

Celestial Mapping System

Celestial Mapping System (CMS) is a software platform developed by Dr. Agrawal’s team to generate virtual 3D globes for celestial bodies. Various layers are built on top of the virtual globe to provide visualization for high-resolution imagery, enable precise measurements, build analytical capabilities and broad range of functionalities to assist planetary scientists and mission planners. In her June 14th presentation, Dr. Agrawal will showcase the user interactive software platform to virtually travel the lunar terrain, place rovers and instruments on the surface of the moon and perform measurements of various geological features including permanently shadowed regions. Her team’s future goals include building interactive 3D globes for other planetary bodies such as Venus, Mars, and Titan in the solar system.

GIS system↗
NASA NTRS2008

Expressions Module for the Satellite Orbit Analysis Program

The Expressions Module is a software module that has been incorporated into the Satellite Orbit Analysis Program (SOAP). The module includes an expressions- parser submodule built on top of an analytical system, enabling the user to define logical and numerical variables and constants. The variables can capture output from SOAP orbital-prediction and geometric-engine computations. The module can combine variables and constants with built-in logical operators (such as Boolean AND, OR, and NOT), relational operators (such as >, <, or =), and mathematical operators (such as addition, subtraction, multiplication, division, modulus, exponentiation, differentiation, and integration). Parentheses can be used to specify precedence of operations. The module contains a library of mathematical functions and operations, including logarithms, trigonometric functions, Bessel functions, minimum/ maximum operations, and floating- point-to-integer conversions. The module supports combinations of time, distance, and angular units and has a dimensional- analysis component that checks for correct usage of units. A parser based on the Flex language and the Bison program looks for and indicates errors in syntax. SOAP expressions can be built using other expressions as arguments, thus enabling the user to build analytical trees. A graphical user interface facilitates use.

Edmonds, Karina↗
DOE OSTI2020

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

SeeQ: A Programming Model for Portable Data-Driven Building Applications

This paper introduces SeeQ, a programming model and an abstraction framework that facilitates the development of portable data- driven building applications. Data-driven approaches can provide insights into building operations and guide decision-making to achieve operational objectives. Yet the configuration of such applications per building requires extensive effort and tacit knowledge. In SeeQ, we propose a portable programming model and build a software system that enables self-configuration and execution across diverse buildings. The configuration of each building is captured in a unified data model - in this paper, we work with the Brick ontology without loss of generality. SeeQ focuses on the distinction between the application logic and the configuration of an application against building-specific data inputs and systems. We test the proposed approach by configuring and deploying a diverse range of applications across five heterogeneous real-world buildings. The analysis shows the potential of SeeQ to significantly reduce the efforts associated with the delivery of building analytics.

analytics↗
DOE OSTI2022

Near Infrared Emitting Semiconductor Polymer Dots for Bioimaging and Sensing

Semiconducting polymer dots (Pdots) are rapidly becoming one of the most studied nanoparticles in fluorescence bioimaging and sensing. Their small size, high brightness, and resistance to photobleaching make them one of the most attractive fluorophores for fluorescence imaging and sensing applications. This paper highlights our recent advances in fluorescence bioimaging and sensing with nanoscale luminescent Pdots, specifically the use of organic dyes as dopant molecules to modify the optical properties of Pdots to enable deep red and near infrared fluorescence bioimaging applications and to impart sensitivity of dye doped Pdots towards selected analytes. Building on our earlier work, we report the formation of secondary antibody-conjugated Pdots and provide Cryo-TEM evidence for their formation. We demonstrate the selective targeting of the antibody-conjugated Pdots to FLAG-tagged FLS2 membrane receptors in genetically engineered plant leaf cells. We also report the formation of a new class of luminescent Pdots with emission wavelengths of around 1000 nm. Finally, we demonstrate the formation and utility of oxygen sensing Pdots in aqueous media.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Modelica-json: Transforming energy models to digitize the control delivery process

Building simulation models are typically not used to generate the documentation required for bidding and project delivery of commercial building systems, or for their semantic modeling and commissioning. This paper presents a software tool that aids in digitizing the control delivery process, spanning simulation during design to implementation and formal verification during commissioning. The tool can generate from Modelica models digital documentation of control sequences. This digital documentation, along with other project drawings and specifications can be used for project bidding. It can also be used for implementation of control sequences through machineto-machine translation to commercial legacy control products, for which we are currently developing the proposed ASHRAE Standard 231P based on the presented work. Moreover, as-installed control sequences can be formally verified against the design specification, and a semantic model in Brick can be exported to aid in configuration of building analytics and fault detection. The paper presents what we believe is the first translation of a Modelica-implemented control sequence to a native implementation on a commercial control platform, using the webCTRL product line from Automated Logic. The paper also shows how a webCTRL implementation can be formally verified against its Modelica specification. These use cases have all been demonstrated with a prototype implementation that is now being further developed.

Wetter, Michael↗