SEARCH · Engineering Papers
Results for “Energy Data”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
From Theory to Practice: Lessons Learned from an Advanced M&V Commercial Pilot
Advanced measurement & verification (M&V), or M&V 2.0, is an energy data analysis method using advanced metering infrastructure (AMI) data in combination with analytics to quantify energy efficiency project savings. Advanced M&V is viewed as an enabler for underutilized behavioral, retrocommissioning, and holistic multi-measure efficiency efforts, as quantifying savings for these programs with existing methods has typically been very challenging. Despite its promise, there are still several unanswered questions about the use of advanced M&V such as: How accurately can these tools quantify energy savings? How can advanced M&V satisfy regulatory requirements for rigorous savings estimates? Where and how does advanced M&V intersect with traditional impact evaluation and the principles of ‘embedded’ evaluation, measurement & verification (EM&V)? Pilots are being pursued in various regions and program contexts to address these research questions. However, results from these pilots are rarely made public, meaning that lessons may not be widely shared and the scope of analyses varies widely. The paper presents encouraging results from a recent advanced M&V pilot and discusses key process-related aspects such as managing AMI data and refining analysis methodologies. An approach to reviewing and classifying projects based on savings characteristics and magnitude is demonstrated, as a potential risk management tool. The authors also demonstrate how advanced M&V can identify underperforming projects and non-routine events in a timely manner. The paper also outlines lessons learned from this and other recent pilot activity and barriers to be overcome in moving toward the goal of widespread adoption of holistic meter-based programs.
Assessing overall building energy performance of a large population of residential single-family homes using limited field data
The U.S. Department of Energy (DOE), through the Building Energy Codes Program (BECP), supports building energy code development and implementation processes to achieve the maximum practicable, cost-effective improvements in energy efficiency while providing safe, healthy buildings for occupants. Currently, there is a lack of statistically representative data that shows how energy codes are implemented in the field and the energy savings opportunities that exist. To address this gap, DOE initiated a research study targeted at new residential single-family homes in 8 states starting in the fall of 2014. The study comprises of a baseline phase to determine current levels of compliance with the key energy provisions of the residential building energy code, identify issues, and calculate potential energy savings from improved compliance; a second phase implementing education, training and outreach activities designed to increase targeted code compliance; and a post-study phase to measure the impact of the training and outreach activities compared to the baseline phase. Initial analyses determined the code requirements with the largest energy impact and the minimum number of observations required to detect statistically significant differences in energy use. Data was collected in the field during a single site visit to avoid bias from repeat visits. However, the single site visit principle prevented energy simulation of individual homes due to the lack of all required model inputs. To address this, energy simulation was conducted using Pacific Northwest National Laboratory’s prototype building models and employing bootstrap random sampling on the data collected from all homes visited. The modeled energy consumption using the observed field data was evaluated relative to that associated with the minimum code requirements and potential savings associated with improved compliance of select items were assessed. This paper discusses the methodology developed for this field study, and presents the results and findings from the baseline phase.
OEDI—Solar Grid Integration Data and Analytics Library
As a part of the Open Energy Data Initiative, this effort aims to develop and demonstrate novel distribution state estimation, control optimization, and transient analysis as well as provide access to data, data integration, and mapping information. More specifically, the focus of the effort will be on physics-based distribution system state estimation, hybrid (physics-based and machine learning) distribution optimal power flow, and event detection/analysis for solar integration and analytics. This work will enable reproducible, robust, replicable, and generalizable R&D in simulation and emulation of solar system integration. These test models and datasets will provide an integrated library for developing and testing power system operation technologies. To make the library user-friendly, this project will provide data curation tools such as data translators, mapping scripts and APIs, database schemas and metadata, interfaces and user dashboard, source code for the reference algorithms, description of the use-cases/scenarios, and comprehensive information on all the assumptions.
Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products
USAID Bangladesh and the U.S. Department of Energy’s National Renewable Energy Laboratory partnered with the Government of Bangladesh to develop a national wind resource assessment. The assessment used sophisticated resource modeling that was validated by a ground measurement campaign. Results from the project included a long-term, correlated wind data set; validated high-resolution wind resource maps; and publicly available data accessible through the RE Data Explorer (https://www.re-explorer.org/). The Renewable Energy (RE) Data Explorer allows users to access and download the Bangladesh wind resource assessment data and related geographic information system (GIS) data sets and perform customized technical potential analyses. Figure ES-1 is an example wind resource map for Bangladesh that can be created with the RE Data Explorer. This project improved upon existing global data sets by using best-in-class modeling techniques and analysis. Global models are good first steps in predicting wind resources in various parts of the world, but they do not generate the accuracy needed to reduce project risk and stimulate renewable investment. This project improved the quality of modeled wind resource data for Bangladesh. In addition to the modeling effort, the project team used a multi-year, local data-collection campaign to validate the model and further improve the accuracy of the data sets. The measurement campaign consisted of nine meteorological sites representing all geographical regions of the country. Seven meteorological towers and one remote-sensing, sonic detection and ranging unit (deployed at two sites) collected the data. Site-selection criteria focused on geographic diversity and proximity to existing transmission lines. Figure ES-1 shows measurement locations. The measurement campaign spanned June 2014 through December 2017. The results of the Bangladesh wind resource assessment will help Bangladesh overcome significant energy challenges. Its energy sector suffers from power shortages, increasing demand, decreasing domestic natural gas reserves, and inadequate transmission infrastructure. As part of a comprehensive plan to overcome these challenges, Bangladesh has committed that 10% of the total generation capacity will be renewable energy by 2021 (Power Division 2016). High-quality renewable energy resource data and other GIS data, such as those developed in this assessment, are important if Bangladesh wishes to reach its 10% renewable energy capacity target. These data support informed decision making, ranging from policy and investment decisions to reliable power sector planning. Specifically, the Bangladesh wind resource assessment will help reduce technical risk and encourage private-sector interest in the nascent wind market in Bangladesh. In addition, the Government of Bangladesh may use the results of the wind resource assessment to develop well-designed policies that could encourage investment in wind energy. This report provides a comprehensive description of the Bangladesh wind resource assessment, including details on the modeling approach and methods, instrumentation, data quality-control techniques, and resulting data sets.
Demonstration of high efficiency elastocaloric cooling with large Delta T using NiTi wires
Vapor compression (VC) is by far the most dominant technology for meeting all cooling and refrigeration needs around the world. It is a mature technology with the efficiency of modern compressors approaching the theoretical limit, but its environmental footprint remains a global problem. VC refrigerants such as hydrochloroflurocarbons (HCFCs) and hydrofluorocarbons (HFCs) are a significant source of green house gas emissions, and their global warming potential (GWP) is as high as 1000 times that of CO2 [Buildings Energy Data Book (Building Technologies Program, Department of Energy, 2009)]. There is an urgent need to develop an alternative high-efficiency cooling technology that is affordable and environmentally friendly [A. D. Little, Report For Office of Building Technology State and Community Programs, Department of Energy, 2001]. Here, we demonstrate that elastocaloric cooling (EC), a type of solid-state cooling mechanism based on the latent heat of reversible martensitic transformation, can have the coefficient of performance as high as 1 1, with a directly measured Delta T of 17 degrees C. The solid-state refrigerant of EC completely eliminates the use of any GWP refrigerants including HCFCs/HFCs. (C) 2012 American Institute. of Physics. [http://dx.doiorg/10.1063/1.4746257]
An Implementation for Transforming a Home Energy Management System to a Multi-agent System
In the United States, 41% of produced energy is consumed by the building sector, i.e. residential and commercial buildings (Building Energy Data Book, Buildings Sector, US Department of Energy, Office of Energy Efficiency and Renewable Energy, Building Technologies Office, 2012. n.d.). The anticipation is that Home Energy Management Systems (HEMS) will support energy efficiency gains through control of the devices in an optimal fashion. New opportunities are offering the ability to integrate grid type controls and the most suitable way to perform these controls is through a multi-agent system (MAS). In this paper, approaches on supporting a HEMS and MAS integration are discussed.
Autonomous Correction of Sensor Data Applied to Building Technologies Utilizing Statistical Processing Methods
Autonomous detection and correction of potentially missing or corrupt sensor data is a essential concern in building technologies since data availability and correctness is necessary to develop accurate software models for instrumented experiments. Therefore, this paper aims to address this problem by using statistical processing methods including: (1) least squares; (2) maximum likelihood estimation; (3) segmentation averaging; and (4) threshold based techniques. Application of these validation schemes are applied to a subset of data collected from Oak Ridge National Laboratory s (ORNL) ZEBRAlliance research project, which is comprised of four single-family homes in Oak Ridge, TN outfitted with a total of 1,218 sensors. The focus of this paper is on three different types of sensor data: (1) temperature; (2) humidity; and (3) energy consumption. Simulations illustrate the threshold based statistical processing method performed best in predicting temperature, humidity, and energy data.
Sector-Specific Methodologies for Subnational Energy Modeling
The National Renewable Energy Laboratory (NREL) and the Department of Energy's Office Strategic Priorities and Impact Analysis Office sought to address gaps in subnational energy data availability. This report describes the methodologies and analysis developed to estimate electricity and natural gas consumption and expenditures for the residential, commercial, and industrial sectors within each U.S. city.
Computed Tomography Scanning and Geophysical Measurements of the Clinton Formation in Ohio
Computed tomography and multi-sensor core logging of core material from twelve Ohio wells held by the Ohio Department of Natural Resources, Division of Geological Survey to make publicly available core information from the Early Silurian Clinton Formation in Eastern Ohio. Describes data available can be accessed from NETL’s Energy Data eXchange (EDX) online system, https://edx.netl.doe.gov/dataset/ct-and-geophysical-data-of-ohio-clinton-sands.
Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability
Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.
Adaptive Data Rate Techniques for Energy Constrained Ad Hoc LoRa Networks.
Abstract not provided.
Identification of Energy Efficiency Opportunities through Building Data Analysis and Achieving Energy Savings through Improved Controls
This chapter will highlight analysis techniques to identify energy efficiency opportunities to improve operations and controls. A free tool, Energy Charting and Metrics (ECAM), will be used to assist in the analysis of whole-building, sub-metered, and/or data from the building automation system (BAS). Appendix A describes the features of ECAM in more depth, and also provide instructions for downloading ECAM and all resources pertaining to using ECAM.
Data for Renewable Energy Planning, Policy, and Investment
Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerations to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.
Three-dimensional multimodal imaging and analysis of biphasic microstructure in a Ti–Ni–Sn thermoelectric material
The three-dimensional microstructure of levitation melted TiNi{sub 1.20}Sn has been characterized using the TriBeam system, a scanning electron microscope equipped with a femtosecond laser for rapid serial sectioning, to map the character of interfaces. By incorporating both chemical data (energy dispersive x-ray spectroscopy) and crystallographic data (electron backscatter diffraction), the grain structure and phase morphology were analyzed in a 155 μm × 178 μm × 210 μm volume and were seen to be decoupled. The predominant phases present in the material, half-Heusler TiNiSn, and full-Heusler TiNi{sub 2}Sn have a percolated structure. The distribution of coherent interfaces and high-angle interfaces has been measured quantitatively.
The calculation of proportional counter energy deposition spectra from experimental data. II - Very small energy losses and high energy delta rays
Explore the source record for details and available documents.
The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017
The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a unique registryID, based on the U.S. Environmental Protection Agency (EPA) Facility Registry Service, and includes its coordinates and other geographic identifiers. Energy-using units are characterized by design capacity, as well as their estimated energy use, greenhouse gas emissions, and physical throughput using 2017 data from the EPA's National Emissions Inventory and Greenhouse Gas Reporting Program. An overview of the derivation methods is provided in a separate technical report which will be linked after publication. The Python code used to compile the dataset is available in a GitHub repository. An updated 2020 version is under development.