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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q2

The vision of the National Microbiome Data Collaborative (NMDC) centers on the concept of connecting data, people, and ideas to advance microbiome innovation and discovery. Building data infrastructure, while key to NMDC’s ability to execute on our vision, can only go so far in creating scientific impact. By fostering strong community partnerships and developing a set of robust community outreach and training programs, we are able to turn our products – the Submission Portal, NMDC EDGE, and the Data Portal – into tools that empower the scientific community. Our multi-pronged community building approach spans individual researchers, research teams, consortia and scientific societies, and institutions and federal agencies. To foster a collaborative and inclusive community-centered environment, we have identified three strategic objectives to promote an inclusive and connected community: (1) recognize and support the diverse research needs and perspectives of the microbiome research community; (2) promote best practices across the microbiome community, from researchers to funders, through community-driven practices (FAIR, CARE, and TRUST); and (3) build a microbiome ecosystem that enables scientific discovery and innovation across stakeholders. These strategic objectives allow our team to focus on impact across a diverse range of activities, from launching the American Society for Microbiology (ASM) Microbiome Data Prize to supporting the Ambassador and Champions programs fostering learning and building a collaborative network. We broadly communicate our work through social media (X/Twitter, LinkedIn, and Instagram), The Microbiome Standard (our quarterly newsletter), and Annual Reports. All our work is underpinned by a strong commitment to diversity, equity, and inclusion as articulated in our Action Plan that tracks progress towards key metrics. A core component of our engagement strategy is user research. User research ensures the Submission Portal, NMDC EDGE, Data Portal, and the new Field Notes mobile app are designed with and for the scientific community. Our user research efforts consist of asking researchers exploratory questions to collect information on researcher priorities, methodologies, and perceptions to ensure that we are aware of the current state of microbiome research. Our usability testing provides researchers with prototypes or test environments of the NMDC products, and we capture valuable information on how users interact with the products to make improvements. Given the diverse nature of microbiome work, we acknowledge that we are not aware of all pressing data challenges and thus rely on the research community to help us identify the most important issues to prioritize. To date, we have conducted 24 interviews and one beta-testing call with 10 participants across all NMDC products, which have generated 321 insights and 120 action items. Herein, we describe the ways we engage with the microbiome research community to advance the NMDC mission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Efficiency and Demand Flexibility in Large Office Buildings: The Potential for Cost Savings and CO 2 Reductions from Lighting and Cooling Measures

This report presents the estimated impact of lighting and cooling efficiency and demand flexibility measures in large office buildings in each state in the contiguous United States. It provides modeled results for three different metrics: bill savings, regional grid operational costs savings, and carbon dioxide (CO 2 ) emissions reductions. Lighting efficiency and demand flexibility are estimated to reduce load by up to 80 MWh/yr in a single large office building. These load reductions result in customer bill savings of up to $8,800/yr per building, with the highest savings in southern and midwestern states. Grid operating cost savings are estimated at up to $3,240/yr/building, with greatest benefit in southern and northeastern states. CO 2 emissions reduction potential is highest in the Dakotas, Nebraska, across the Midwest, in West Virginia, and in Mississippi (<48,200 kg/yr/building). Comparatively, cooling measures are found to have less load reduction potential (<28.5 MWh/yr/building), with the greatest potential in southern states including Texas, which ranks top of the list across several of the metrics studied. In numerous states, shifting cooling load to off-peak hours is found to increase costs and CO 2 emissions because precooling results in increased load during high-cost or high-CO 2 emissions periods. In general, focusing on cooling efficiency and load shedding has the potential for more savings. In all cases, the specific rate structure is a significant determinant in actual bill savings, which are up to $4,000/yr/building. To realize the full potential for bill savings through an energy measure, building operators must identify how the measure will change the building load pattern and the interaction of this load change with the applicable rate tariff. To realize CO 2 emissions reductions, industry and state coordination is needed to verify which fuel source is on the margin and then to create incentives for end users to reduce load during high-CO 2 emissions hours. Regular updates to data sets and analyses are critical. Regulators and policymakers are well positioned to facilitate the necessary coordination between the electric industry and building energy managers to develop appropriate price signals and incentives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Data-Driven Approach to Nation-Scale Building Energy Modeling

In 2019, 125 million U.S. residential and commercial buildings consumed $412 billion in energy bills. These buildings currently consume 40% of the nation's primary energy, 73% of electricity, 80% of energy during peak electric grid use, and responsible for 39% of greenhouse gas emissions [14]. Urban-scale building energy modeling has grown significantly in the past decade, allowing individual campuses or communities of buildings to be modeled, simulated, and cost-effective solutions for intelligent management to be identified and implemented. While traditionally limited to individual counties and usually less than 2,000 buildings, the Automatic Building Energy Modeling (AutoBEM) soft-ware suite has been developed to process unconventional, nation-scale data sources to generate unique OpenStudio and EnergyPlus models of each building. Through the use of High Performance Computing (HPC) resources, every U.S. building has been simulated. This paper showcases the data layout, node partitioning, algorithmic approaches, and analytic results that were used to create, share, and analyze 124.4 million U.S. building models.

Berres, Andy↗
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

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Fully Automatic Method for Rapidly Mapping Impacted Area by Natural Disaster

Deep learning based change detection methods have achieved the state-of-the-art performance in several recent studies. However, such methods usually are supervised, and therefore a large number of training samples is often a requisite. Manually preparing those training samples is not only expensive but also time-consuming, which does not fit the need of rapidly mapping the impacted area caused by nature disaster for further rescue mission and damage assessment. In this study, a fully automatic method was proposed to address the issue by automating training sample generation for mapping the impacted area caused by nature disaster. We used the 2011 tornado event in Joplin, Missouri, US, as an example of its application. The generated impacted area map was both visually and quantitatively evaluated against the ground truth data collected by US Federal Emergency Management Agency (FEMA). The results show that the map matches well with the FEMA ground truth data with 86% of major-damaged and destroyed buildings identified by FEMA on the ground also detected by this fully automatic framework using very high resolution (VHR) satellite images.

Liu, Tao↗
DOE OSTI2020

Understanding Impacts of Data Integrity Attacks on Transactive Control Systems

With the rapid growth of internet-connected smart devices capable of exchanging energy price information and adaptively controlling energy consumption of connected loads, the role of transactive control is expected to become more prominent in the modern grid. Transactive control systems integrate the wholesale and retail energy markets, and enable active participation of end users, thereby playing a key role in managing the rising number of distributed assets in the smart grid. The use of internet for the communication of data between the controllers at the building, distribution, and transmission levels makes the system susceptible to cyber-attacks. A skilled adversary can potentially manipulate the exchanged data with the intention of inflicting damage to the system. In this paper, four categories of metrics are put forth for evaluating the performance of the system when under data integrity attacks. A model for scaling-based data integrity attack has been described, and co-simulations have been performed on a 240 bus WECC transmission system model with detailed modeling of distribution systems at specific buses. Our results show that scaling-based data integrity attacks can have non-trivial impacts on the operational, financial, and comfort aspects of the transactive control system.

Smart grid, transactive control system, Buildings-↗
DOE OSTI2023

Building 438 Drainage Channel Soil Sampling and Analysis Plan (February 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Building 438 Drainage Channel Project (project) located near Building 438 (B438) in the southern portion, Building 433 (B433) in the middle portion, and Building 543 (B543) in the northern portion (project area as shown on Figure 1). The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Building Monitoring and Energy Efficiency Training for Small and Mid-Sized Commercial Buildings in Non-Urbanized Areas of Alaska

The goal of this project was to create an effective training that increases understanding, competency and capacity in monitoring critical building data. Mentorship, verification and follow-up ensured participants now have necessary skills to install and maintain building monitoring systems. This training sought to empower building managers and maintenance staff to properly and efficiently operate their buildings for energy savings and increased lifespan of equipment. Through the project, participants’ knowledge of the building was increased through hands-on training using a familiar building environment and eliminating common barriers to effective training through peer-to peer delivery. In successful cases the host organization will save money and resources due to more efficient operation of building equipment already in place.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Watchr 3.0.0

SAND2021-0624 O Watchr is a Jenkins plugin for tracking the performance of subroutines over many individual builds. It reads build-generated performance reports and displays the data contained in these performance reports as a series of graphs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Ridgway, ElliottM↗
DOE OSTI2021

Sustainable Campus with PEV and Microgrid

Market penetration of electric vehicles (EVs) is gaining momentum, as is the move towards increasingly distributed, clean and renewable electricity sources. EV charging shifts a significant portion of transportation energy use onto building electricity meters. Hence, integration strategies for energy-efficiency in buildings and transport sectors are of increasing importance. This paper focuses on a portion of that integration: the analysis of an optimal interaction of EVs with a building-serving transformer, and coupling it to a microgrid that includes PV, a fuel cell and a natural gas micro-turbine. The test-case is the Nanyang Technological University (NTU), Singapore campus. The system under study is the Laboratory of Clean Energy Research (LaCER) Lab that houses the award winning Microgrid Energy Management System (MG-EMS) project. The paper analyses three different case scenarios to estimate the number of EVs that can be supported by the building transformer serving LaCER. An approximation of the actual load data collected for the building into different time intervals is performed for a transformer loss of life (LOL) calculation. The additional EV loads that can be supported by the transformer with and without the microgrid are analyzed. The numbers of possible EVs that can be charged at any given time under the three scenarios are also determined. The possibility of using EV fleet at NTU campus to achieve demand response capability and intermittent PV output leveling through vehicle to grid (V2G) technology and building energy management systems is also explored.

Singh, Reshma↗
DOE OSTI2020

The Demonstration of Power over Ethernet (PoE) Technologies in Commercial and Institutional Buildings

PoE technologies are currently ubiquitous in offices in the form of phones, access points, and IP security cameras and managed by on-site IT staff. As low voltage DC-power capabilities have increased with network switches, PoE devices are now being integrated across additional building systems, including lighting, computers, and HVAC controls. Since PoE devices are connected to network switches, energy monitoring and management can be performed through the IT network management software. Data and results from technology validations are still very limited for PoE equipment. This lack of information hinders higher adoption as its benefits and challenges have been insufficiently quantified in real-world scenarios. The use of IT network switches to power and control lighting, plug loads, and HVAC were demonstrated at a variety of sites including offices, schools, and a hotel. This paper presents the findings of a three-year DOE-/State of Minnesota-funded project that demonstrated and assessed the energy and non-energy benefits of Power over Ethernet (PoE) technologies at four commercial/institutional sites. The goals of the demonstrations were to: (1) assess the receptiveness of commercial and institutional markets to these new technologies, (2) demonstrate the feasibility of PoE technologies within standard design/construction practices and commercial codes, (3) assess the energy and cost savings opportunities of these technologies, (4) provide energy management opportunities where they typically are not available, and (5) formulate approaches to spur adoption of these technologies in the marketplace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗
DOE OSTI2023

Multi-Unit Dwelling Plug-in EV Charging Innovation Pilots (Final Report)

Nearly one-third of residences in the U.S. are multi-unit dwellings (MUDs), e.g., apartments and condominiums, and MUDs with five or more units account for approximately 45% of rental households. While 80% of EV charging takes place at home, less than 5% of home charging takes place at MUDs. With public electric vehicle (EV) charging still underdeveloped, lack of access to reliable home charging is a major barrier to EV adoption for MUD residents. Challenges to siting electric vehicle supply equipment (EVSE) at MUDs include the high upfront cost of EVSE installation, physical and/or electrical infrastructure constraints, a lack of clear incentives for property managers to invest in installing EV charging for tenants, and a limited number of EV charging service providers that offer solutions adapted to the unique needs of MUDs. Through award DE-EE0008473 from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE), Center for Sustainable Energy (CSE), Energetics, and Forth, along with a diverse team of partners, led a three-year project to address barriers to EV charging at MUDs by developing an online toolkit geared toward residents, homeowner associations (HOAs), and property managers. The project, referred to as Vehicle Charging Innovations – Multi-Unit Dwellings (VCI-MUD project), engaged stakeholders across the country to identify real and perceived barriers to EV charging at MUDs and explored innovative technologies that attempt to alleviate the identified barriers. Over the course of three and a half years, the project conducted 60 interviews with industry stakeholders, compiled findings in an easy-to-use toolkit, and disseminated the toolkit across national, regional, state, and local channels. Key findings and outcomes of the VCI-MUD project include: Identifying six primary barriers to the installation of EV charging at MUDs; Developing five fact sheets and eight case studies highlighting innovative charging solutions to address barriers, including real-world operational and financial data; Developing a user-friendly, online empowerment toolkit with five important points for “making your pitch” to support EV charging to assist residents, property managers, building managers, and HOAs explore options for MUD charging. The VCI-MUD online toolkit was designed with replicability in mind. It includes general tools and guides to evaluate EV charging demand, gauge readiness for EV charging installation, and develop actionable plans. The fact sheets and case studies highlight the diversity of emerging MUD EV charging solutions, featuring different geographic and structural installation scenarios and providing interested parties with a menu of options, rather than prescribing a one-size-fits-all solution. The following resources are included in the toolkit to provide MUD stakeholders with all the information needed to navigate EV charging installation at their MUD location: Empowerment Toolkit – Easy-to-read FAQ overview, stakeholder roles and responsibilities, and additional resources for MUD EV charging installations; Charging Basics – Glossary of terms and descriptions of charging features, installation and operating expenses; EV Charging Survey Templates – Resident-to-Resident, Property Manager-to-Resident and HOA-to-Resident template letters with pre-populated template questions; Technology Selection Tool – Charging barrier fact sheets and case study examples; Installation Checklist – Submittal document requirements for EVSE installations; MUD Building Self-Evaluation Survey – Self-guided evaluation of potential barriers to EV charging installation at MUD locations; Curbside Resources – Case studies and fact sheets for curbside charging options; Find a Certified Electrician – Approved list of certified EV charging installers; “Right-to-Charge” State Legal References – Legal requirements for charging equipment at MUD locations. The online toolkit was promoted extensively in the final six months of the project and will continue to be disseminated by the Clean Cities Coalitions and other partners after the end of the VCI-MUD project to encourage, support, and demonstrate viable solutions for vehicle charging infrastructure in MUDs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluation of the Radioactive Material Release in the Harborview Research and Training Building and Implications for Emergency Response

On May 2, 2019, during the 137 Cs source recovery operation, a source capsule in a research irradiator containing approximately 77.1 TBq was breached. Based on a geometric reconstruction analysis of the damage to the capsule, approximately 46.3 GBq (0.04%) was impacted by the chop saw (grinder) inside a Mobile Hot Cell (MHC) on the loading dock at the University of Washington Harborview Research and Training (HRT) Building. A very small fraction of the material impacted, less than 1%) was released from the Mobile Hot Cell and then to the rest of the HRT Building. The objectives of this project were to assess the accidental release of 137 CsCl and its implications related to emergency response methods and the ramifications of 137 CsCl transport. The phenomenology of this event was also compared with past alkali halide dispersal events. The vast number of measurements and samples collected by the remediation contractors, the Department of Energy's Nuclear Emergency Support Team, and the small number of retrospective samples collected by the authors informed the analysis. The techniques included (1) autoradiography and electron microscopy of samples collected from the HRT Building and the irradiator, (2) 3D visualization of deposition on surfaces and within the ventilation system, and (3) a study of the damage to the source capsule to evaluate the Cs particle size and particle composition due to the grinding accident. Subsequently, the cesium contaminant transport through the numerous pathways in the building was reconstructed to assess the deposition on surfaces as a function of particle size. The implications for emergency response are relevant to data quality and management. A Data Quality Objective (DQO) guides data collection methods so that they have appropriate accuracy and precision for the intended application. Recommendations were made with respect to the sample collection protocols and sample archival.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Better bang for your buck? Comparing savings realization from ESPCs and direct-funded projects

Energy savings performance contracts (ESPCs) offer an opportunity to tremendously scale decarbonization projects, given their paid-from-savings premise. However, prospective customers still question whether ESPC is worth the effort and expense. This study evaluates the savings realization rates of ESPCs compared to direct-funded projects using ENERGY STAR Portfolio Manager (ESPM) benchmarking data. It compares normalized EUIs from ESPM before and after energy conservation projects implemented via ESPC or direct funding in roughly 450 federal buildings. This documented change, ideally a savings, can then be compared to the estimated savings from the energy conservation initiative in order to generate a rough realization rate. Preliminary results indicate a notably greater savings realization rate for the ESPC buildings (median = 105% of estimated savings) than those that underwent direct-funded projects (median = 46%). Because of a shortage of good quality data and the wide range in results, the difference is only significant at the p < 0.20 level. However, the higher savings realization of ESPCs corroborates the authors’ 2014 findings using a different comparison method. With continued soft government funding and regulatory impediments for climate change mitigation in the U.S., financed, paid-from-savings project models (e.g., ESPC, PACE, EaaS and others) certainly merit more attention. This study’s results are particularly compelling (and encouraging) given the country’s likely reliance on these vehicles to address existing building retrofits.

Earni, Shankar↗
DOE OSTI2026

Buildings Sector Scenarios: Demand-side data to support energy system planning in the United States

The US energy system is in a period of high uncertainty about load growth, its implications for the energy generation mix, and downstream impacts on customer energy costs. In this context, there is a need for comprehensive, credible, and readily-customized projections of energy demand to ensure that planning decisions account for end-use management opportunities to improve system reliability and affordability. Here we introduce the Buildings Sector Scenarios (BSS) dataset, which includes a benchmark suite of such projections for the buildings sector — a key source of energy consumption, peak electricity demand, and consumer energy expenditures. The dataset contains projections through 2050 covering the contiguous United States (CONUS) resolved down to the county, hourly level by sector and end use for electricity demand and to the state, annual level by sector and end use for non-electric fuels. We summarize the BSS analysis workflow and the tools and datasets that support it, document key BSS scenario inputs and modeling assumptions, and outline BSS scenario outputs. We assess the technical quality of the dataset against historical surveys and projected estimates of buildings sector demand. Finally, we provide guidance on how stakeholders can access, use, and reproduce the dataset, and/or create new scenarios to explore their own analysis questions.

Langevin, Jared↗
DOE OSTI2022

Initial Findings from US Department of Energy’s Better Plants Virtual in-Plant Training on 50001 Ready

Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies’ energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management’s understanding of the time and resources needed to build an EnMS as well as the benefits; (2) creating linear regression models for more accurate energy performance tracking; (3) understanding energy use, collecting and analyzing energy performance data; (4) optimizing equipment operational controls, and creating action plans.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗