EIGHT NEW MILKY WAY COMPANIONS DISCOVERED IN FIRST-YEAR DARK ENERGY SURVEY DATA
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Astrophysics,ASTRO↗
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City Energy: From Data to Decisions. Hutchinson, Minnesota: Evaluating Local Solar Energy Generation Potential
The City of Hutchinson, Minnesota, partnered with the Energy Department and the National Renewable Energy Laboratory (NREL) to demonstrate how data and analysis can inform more strategic energy decisions. NREL based its analysis in part on the City Energy Profiles on the State and Local Energy Data (SLED) website (eere.energy.gov/sled). The profiles contain data compiled by SLED and the Cities Leading through Energy Analysis and Planning (Cities-LEAP) program. Cities across the country can follow the same approach and use data-driven analysis in their own energy planning.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Data Center Energy Efficiency
Private and public sector efforts are underway to reduce energy use in data centers and the Office of Management and Budget’s Data Center Optimization Initiative outlines the energy efficiency requirements and strategies for federal data centers. The Federal Energy Management Program (FEMP) helps federal agencies meet these requirements. FEMP sponsors the Center of Expertise for Energy Efficiency in Data Centers (CoE), which helps federal agencies and other organizations implement data center energy efficiency projects by supplying technical support, tools, best practices, analyses, and the introduction of technologies. The CoE, located at the Lawrence Berkeley National Lab, partners with key public and private stakeholders to further efficiency efforts
Center of Expertise for Energy Efficiency in Data ↗
Data Center Energy Practitioner (DCEP) Program
The main objective for the DCEP program is to raise the standards of those involved in energy assessments of data centers to accelerate energy savings. The program is driven by the fact that significant knowledge, training, and skills are required to perform accurate energy assessments. The program will raise the confidence level in energy assessments in data centers. For those who pass the exam, the program will recognize them as Data Center Energy Practitioners (DCEPs) by issuing a certificate. Hardware req.: PC, MAC; Software Req.: Windows; Related/Auxiliary software--MS Office; Type of files: executable modules, user guide; Documentation: e-user manual; Documentation: http://www.1.eere.energy.gov/industry/datacenters/ 12/10/15-New Documentation URL: https://datacenters.lbl.gov/dcep
Traber, Kim↗
Data Center Optimization Strategies: NREL ESIF Data Center Energy and Water Savings
Presentation for the 2019 Energy Exchange describes data center optimization strategies for saving energy and water in the National Renewable Energy Laboratory's ESIF data center.
25 ENERGY STORAGE↗
Low-Income Energy Affordability Data - LEAD Tool - 2018 Update
The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities
affordability↗
DOE Voluntary Partnership Program with Utilities and Local Governments Supports the Design of New Data Access Solutions
The Better Buildings Energy Data Accelerator (BBEDA) is a unique effort that has supported 22 pairs of local governments and their utility companies to help building owners gain access to their whole-building energy data. Municipal and Utility BBEDA Partners committed to develop streamlined and easy-to-use solutions to provide whole-building energy data, especially for multitenant commercial buildings, by the end of 2015. As a result, building owners would be able to make data-driven decisions about their buildings by utilizing readily available energy consumption data for entire buildings. Traditionally, data access was difficult to implement due to technical barriers and the lack of clear value propositions for the utilities. During the past two years, BBEDA has taken a hands-on approach to overcome these barriers by offering a platform for the partners to discuss their challenges and solutions. Customized support was also provided to Partners building their local strategies. Based on the lessons learned from the partners, BBEDA developed a final toolkit with guiding documents that addressed key barriers and served as a resource for the other cities and utilities attempting to establish whole-building data access, including an exploration of opportunities to apply the whole-building data to various aspects of utility demand-side management (DSM) programs. BBEDA has been a catalyst for market transformation by addressing the upstream (to efficiency implementation) barrier of data access, demonstrated through the success of the BBEDA partners to address policy, engagement, and technical hurdles and arrive at replicable solutions to make data access a standard practice nationwide. As a result of best practices identified by the BBEDA, 18 utilities serving more than 2.6 million commercial customers nationwide will provide whole-building energy data access to building owners by 2017. This historic expansion of data accessibility will increase building energy benchmarking, the first step many building owners take to improve the energy efficiency of their buildings.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Low-Income Energy Affordability Data - LEAD Tool - 2022 Update
The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.
AMI↗
Application of Parametrized Post-Newtonian Methods to the Gravitational IS of Satellite Energy Exchange Data
Project Satellite Energy Exchange (SEE) is a free-flying, high altitude satellite that utilizes space to construct a passive, low-temperature, nano-g environment in order to accurately measure the poorly known gravitational constant G plus other gravitational parameters that are difficult to measure in an earth-based laboratory. Eventually data received from SEE must be analyzed using a model of the gravitational interaction including parameters that describe deviations from general relativity and experiment. One model that can be used to fit tile data is the Parametrized post- Newtonian (PPN) approximation of general relativity (GR) which introduces ten parameters which have specified values in (GR). It is the lowest-order, consistent approximation that contains non linear terms. General relativity predicts that the Robertson parameters, gamma (light deflection), and beta (advance of the perihelion), are both 1 in GR. Another eight parameters, alpha(sub k), k=1,2,3 and zeta(sub k), k=1,2,3,4 and Xi are all zero in GR. Non zero values for alpha(sub k) parameters predict preferred frame effects; for zeta(sub k) violations of globally conserved quantities such as mass, momentum and angular momentum; and for Xi a contribution from the Whitehead theory of gravitation, once thought to be equivalent to GR. In addition, there is the possibility that there may be a preferred frame for the universe. If such a frame exists, then all observers must measure the velocity omega of their motion with respect to this universal rest frame. Such a frame is somewhat reminiscent of the concept of the ether which was supposedly the frame in which the velocity of light took the value c predicted by special relativity. The SEE mission can also look for deviations from the r(exp -2) law of Newtonian gravity, adding parameters alpha and lamda for non Newtonian behavior that describe the magnitude and range of the r(exp -2) deviations respectively. The foundations of the GR supposedly agree with Newtonian gravity to first order so that the parameters alpha and lamda are zero in GR. More important, however, GR subsequently depends on this Newtonian approximation to build up the non linear higher-order terms which forms the basis of the PPN frame work.
Smalley, Larry L.↗
Data Center Energy Efficiency Standards in India: Preliminary Findings from Global Practices
Global data center energy consumption is growing rapidly. In India, information technology industry growth, fossil-fuel generation, and rising energy prices add significant operational costs and carbon emissions from energy-intensive data centers. Adoption of energy-efficient practices can improve the global competitiveness and sustainability of data centers in India. Previous studies have concluded that advancement of energy efficiency standards through policy and regulatory mechanisms is the fastest path to accelerate the adoption of energy-efficient practices in the Indian data centers. In this study, we reviewed data center energy efficiency practices in the United States, Europe, and Asia. Using evaluation metrics, we identified an initial set of energy efficiency standards applicable to the Indian context using the existing policy mechanisms. These preliminary findings support next steps to recommend energy efficiency standards and inform policy makers on strategies to adopt energy-efficient technologies and practices in Indian data centers.
Raje, Sanyukta↗
Low-Income Energy Affordability Data (LEAD) Tool
The Low-income Energy Affordability Data (LEAD) tool was created to provide data, such as energy burden, to stakeholders to make data driven decisions. The LEAD Tool provides a starting point for local governments, NGOs, companies, etc. to find areas with higher energy burden and cost to inform decisions about funding distribution and program qualifications. This presentation was given to energy efficiency and conservation block grant program applicants to explain how this tool can help them develop their energy efficiency and conservation strategy.
ENERGY PLANNING, POLICY, AND ECONOMY↗
Implementing the Data Center Energy Productivity Metric in a High Performance Computing Data Center
As data centers proliferate in size and number, the improvement of their energy efficiency and productivity has become an economic and environmental imperative. Making these improvements requires metrics that are robust, interpretable, and practical. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high-performance computing data center. We found that DCeP was successful in clearly distinguishing different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and between data centers.
Sego, Landon H.↗
Engineering-Scale Integrated Energy System Data Projection Demonstration via the Dynamic Energy Transport and Integration Laboratory
The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.
08 HYDROGEN↗
Characteristic recovery times of Forbush-type decreases in the cosmic radiation. I - Observations at earth at different energies
Data on 30 asymmetric Forbush decreases recorded by the IMP spacecraft at 1 AU and the Mt. Washington neutron monitor over the period 1972-84 are examined to characterize the recovery characteristics of cosmic rays after the events. The spacecraft data are concentrated at energies of 1.7 GV, while the terrestrial instruments recorded events at 5 GV. Attention is paid to the relative amplitudes of the recorded transient decreases, the characteristic recovery times, and the energy dependence of the amplitudes and recovery time. The recovery times were found to be equal at both energy levels, supporting a concept of energy independence for the recoveries. Also, no correlations were found between the recovery times and the occurrences of a solar magnetic field reversal or with phase in the solar modulation cycle. A time-dependent, two-dimensional model is defined, which expresses the cosmic ray particle distributions as a function of the decay of the disturbance, with a small dependence on the transport parameters of the cosmic rays.
Lockwood, J. A.↗
City-Level Energy Decision Making: Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities
The report analyzes and presents information learned from a sample of 20 cities across the United States, from New York City to Park City, Utah, including a diverse sample of population size, utility type, region, annual greenhouse gas reduction targets, vehicle use, and median household income. The report compares climate, sustainability, and energy plans to better understand where cities are taking energy-related actions and how they are measuring impacts. Some common energy-related goals focus on reducing city-wide carbon emissions, improving energy efficiency across sectors, increasing renewable energy, and increasing biking and walking.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Scalable tuning of building models to hourly data
Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
San Jose, California: Evaluating Local Solar Energy Generation Potential (City Energy: From Data to Decisions)
This fact sheet "San Jose, California: Evaluating Local Solar Energy Generation Potential" explains how the City of San Jose used data from the U.S. Department of Energy's Cities Leading through Energy Analysis and Planning (Cities-LEAP) and the State and Local Energy Data (SLED) programs to inform its city energy planning. It is one of ten fact sheets in the "City Energy: From Data to Decisions" series.
Cities leading through energy analysis and plannin↗
A case study on parallel HDF5 dataset concatenation for high energy physics data analysis
In High Energy Physics (HEP), experimentalists generate large volumes of data that, when analyzed, helps us better understand the fundamental particles and their interactions. This data is often captured in many files of small size, creating a data management challenge for scientists. In order to better facilitate data management, transfer, and analysis on large scale platforms, it is advantageous to aggregate data further into a smaller number of larger files. However, this translation process can consume significant time and resources, and if performed incorrectly the resulting aggregated files can be inefficient for highly parallel access during analysis on large scale platforms. In this paper, we present our case study on parallel I/O strategies and HDF5 features for reducing data aggregation time, making effective use of compression, and ensuring efficient access to the resulting data during analysis at scale. We focus on NOvA detector data in this case study, a large-scale HEP experiment generating many terabytes of data. Here, the lessons learned from our case study inform the handling of similar datasets, thus expanding community knowledge related to this common data management task.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗