National Library of Energy BETA

Sample records for xls csv graph

  1. CSV's | NISAC

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    State Energy Data System CSV File Documentation Consumption Estimates The State Energy Data System (SEDS) comma-separated value (CSV) files contain consumption estimates shown in the tables located on the SEDS website. There are four files that contain estimates for all states and years. Consumption in Physical Units contains the consumption estimates in physical units for all states; Consumption in Btu contains the consumption estimates in billion British thermal units (Btu) for all states.

  2. CSV to ISO XML metadata transformation tool

    Energy Science and Technology Software Center (OSTI)

    2009-08-01

    Django app for converting CSV records to XML metadata documents. This transformation from the metadata content model to parsed ISO XML documents allows for metadata integration into NGDS.

  3. Recursive Feature Extraction in Graphs

    Energy Science and Technology Software Center (OSTI)

    2014-08-14

    ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

  4. OMBDOEFAIR2005.xls | Department of Energy

    Energy Savers [EERE]

    OMBDOEFAIR2005.xls&0; OMBDOEFAIR2005.xls&0; More Documents & Publications 2003 DOE IGCA Inventory Data for web.xls&0; 3REV2004DOEFAIR.xls&0; N:My Documentsporfin.pdf...

  5. hud_doe_supplemental_list_of_eligible_properties_list_1.xls ...

    Broader source: Energy.gov (indexed) [DOE]

    xls More Documents & Publications huddoesupplementallistofeligiblepropertieslist1.xls rdmfhlowandverylow...

  6. hud_doe_supplemental_list_of_eligible_properties_list_2.xls ...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2.xls huddoesupplementallistofeligiblepropertieslist2.xls huddoesupplementallistofeligiblepropertieslist2.xls More Documents & Publications huddoesupplementallis...

  7. Grantsdown.xls | Department of Energy

    Office of Environmental Management (EM)

    Grantsdown.xls Grantsdown.xls Grantsdown.xls More Documents & Publications Class Patent Waiver W(C)2012-001 Amendment No. 1 (August 5, 2010) FOA 148 Amendment...

  8. State Energy Data System CSV File Documentation Price and Expenditure Estimates

    Gasoline and Diesel Fuel Update (EIA)

    State Energy Data System CSV File Documentation Price and Expenditure Estimates The State Energy Data System (SEDS) comma-separated value (CSV) fles contain the price and expenditure esti- mates shown in the tables located on the SEDS website. There are three fles that contain estimates for all states and years. Prices contains the price estimates for all states and Expenditures contains the expenditure estimates for all states. The third fle, Adjusted Consumption for Ex- penditure Calculations

  9. rd_mfh_low_and_very_low.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    mfhlowandverylow.xls More Documents & Publications list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls hudlist-107-01-11.xls hudlist-107-01-11.xls...

  10. hud_list-1_07-01-11.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    list-107-01-11.xls More Documents & Publications hudlist-107-01-11.xls list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls rdmfhlowandverylow...

  11. 2003 DOE IGCA Inventory Data for web.xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 DOE IGCA Inventory Data for web.xls&0; 2003 DOE IGCA Inventory Data for web.xls&0; 2003 DOE IGCA Inventory Data for web.xls&0; (570.91 KB) More Documents & Publications ...

  12. Role Discovery in Graphs

    Energy Science and Technology Software Center (OSTI)

    2014-08-14

    RolX takes the features from Re-FeX or any other feature matrix as input and outputs role assignments (clusters). The output of RolX is a csv file containing the node-role memberships and a csv file containing the role-feature definitions.

  13. Cell Total Activity Final Estimate.xls

    Office of Legacy Management (LM)

    WSSRAP Cell Total Activity Final Estimate (calculated September 2002, Fleming) (Waste streams & occupied cell volumes from spreadsheet titled "cell waste volumes-8.23.02 with macros.xls") Waste Stream a Volume (cy) Mass (g) 2 Radiological Profile 3 Nuclide Activity (Ci) 4 Total % of Total U-238 U-234 U-235 Th-228 Th-230 Th-232 Ra-226 Ra-228 Rn-222 5 Activity if > 1% Raffinate Pits Work Zone (Ci) Raffinate processed through CSS Plant 1 159990 1.49E+11 Raffinate 6.12E+01 6.12E+01

  14. Graph Theory

    SciTech Connect (OSTI)

    Sanfilippo, Antonio P.

    2005-12-27

    Graph theory is a branch of discrete combinatorial mathematics that studies the properties of graphs. The theory was pioneered by the Swiss mathematician Leonhard Euler in the 18th century, commenced its formal development during the second half of the 19th century, and has witnessed substantial growth during the last seventy years, with applications in areas as diverse as engineering, computer science, physics, sociology, chemistry and biology. Graph theory has also had a strong impact in computational linguistics by providing the foundations for the theory of features structures that has emerged as one of the most widely used frameworks for the representation of grammar formalisms.

  15. 3REV2004DOEFAIR.xls | Department of Energy

    Office of Environmental Management (EM)

    More Documents & Publications N:My Documentsporfin.pdf&0; 2003 DOE IGCA Inventory Data for web.xls&0; 2002 DOE Final Inherently Governmental and Commercial Activities Inventory

  16. Utilization Graphs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    that use data from the PDSF batch scheduler (SGE) to show the utilization of the cluster over the past 24 hours. The graphs were generated with RRDTool and are updated...

  17. list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls |...

    Energy Savers [EERE]

    list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls Office spreadsheet icon list2eligiblemultifamilybuildings1...

  18. FY 2007 Operating Plan for DOE--March 16, 2007.xls | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    FY 2007 Operating Plan for DOE--March 16, 2007.xls U.S Department of Energy 2007 operating plan by appropriation. PDF icon FY 2007 Operating Plan for DOE--March 16, 2007.xls More ...

  19. FINAL Combined SGIG Selections - By State for Press -5.xls | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy FINAL Combined SGIG Selections - By State for Press -5.xls FINAL Combined SGIG Selections - By State for Press -5.xls FINAL Combined SGIG Selections - By State for Press -5.xls (161.6 KB) More Documents & Publications Recovery Act Selections for Smart Grid Invesment Grant Awards- By Category Updated July 2010 FINAL Combined SGIG Selections - By Category for Press -AOv10.xls Recovery Act Selections for Smart Grid Investment Grant Awards - By State - Updated November 2011

  20. Final FY 2009 NEUP RD Awards (2).xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Final FY 2009 NEUP RD Awards (2).xls Final FY 2009 NEUP RD Awards (2).xls Final FY 2009 NEUP RD Awards (2).xls (32.32 KB) More Documents & Publications NEET Awards for FY2012 Meeting Materials: June 9, 2009 EA-1775: Final Environmental Assessment

  1. csv.cfm

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    State Energy Data System (SEDS): 2015 (updates by energy source) Schedule of new releases of energy consumption, price, and expenditure estimates by energy source. See the 2014 version of this page. + EXPAND ALL Petroleum and fuel ethanol 2015 Release date Jet fuel Planned 10/21/2016 Lubricants Planned 10/21/2016 Asphalt and road oil Planned 10/21/2016 Motor gasoline Planned 12/16/2016 Fuel ethanol Planned 12/16/2016 Aviation gasoline Planned 12/16/2016 Distillate fuel oil consumption Planned

  2. CSV File Documentation: Consumption

    Gasoline and Diesel Fuel Update (EIA)

    Product: Total Finished Motor Gasoline Reformulated Gasoline Reformulated Blended w/ Fuel Ethanol Reformulated Other Conventional Gasoline Conventional Blended w/ Fuel Ethanol Conventional Blended w/ Fuel Ethanol, Ed55 and Lower Conventional Blended w/ Fuel Ethanol, Greater than Ed55 Conventional Other Finished Aviation Gasoline Kerosene-Type Jet Fuel Kerosene Distillate Fuel Oil Distillate F.O., 15 ppm Sulfur and under Distillate F.O., Greater than 15 ppm to 500 ppm Sulfur Distillate F.O.,

  3. Attachment 5 Volume II Pricing Matrix.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    Attachment 5 Volume II Pricing Matrix.xls&0; More Documents & Publications Microsoft Word - FY07AnnualReport.doc CX-005455: Categorical Exclusion Determination Microsoft Word -...

  4. Copy of FINAL SG Demo Project List 11 13 09-External.xls | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    of FINAL SG Demo Project List 11 13 09-External.xls More Documents & Publications Smart Grid Regional and Energy Storage Demonstration Projects: Awards Energy Storage Activities...

  5. supplemental_lists_1d-2d-3c_06-24-2011.xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    lists1d-2d-3c06-24-2011.xls supplementallists1d-2d-3c06-24-2011.xls Office spreadsheet icon supplementallists1d-2d-3c06-24-2011.xls More Documents & Publications...

  6. Methods of visualizing graphs

    DOE Patents [OSTI]

    Wong, Pak C.; Mackey, Patrick S.; Perrine, Kenneth A.; Foote, Harlan P.; Thomas, James J.

    2008-12-23

    Methods for visualizing a graph by automatically drawing elements of the graph as labels are disclosed. In one embodiment, the method comprises receiving node information and edge information from an input device and/or communication interface, constructing a graph layout based at least in part on that information, wherein the edges are automatically drawn as labels, and displaying the graph on a display device according to the graph layout. In some embodiments, the nodes are automatically drawn as labels instead of, or in addition to, the label-edges.

  7. 2011 Cost Symposium Agenda 4-28-11 web draft.xls | Department...

    Broader source: Energy.gov (indexed) [DOE]

    Cost Symposium Agenda 4-28-11 web draft.xls (17.46 KB) More Documents & Publications 2011 Cost Symposium Agenda for web (2)-OPAM 2011 Workshop AgendaVer9

  8. mpiGraph

    Energy Science and Technology Software Center (OSTI)

    2007-05-22

    MpiGraph consists of an MPI application called mpiGraph written in C to measure message bandwidth and an associated crunch_mpiGraph script written in Perl to process the application output into an HTMO report. The mpiGraph application is designed to inspect the health and scalability of a high-performance interconnect while under heavy load. This is useful to detect hardware and software problems in a system, such as slow nodes, links, switches, or contention in switch routing. Itmore » is also useful to characterize how interconnect performance changes with different settings or how one interconnect type compares to another.« less

  9. Graph Generator Survey

    SciTech Connect (OSTI)

    Lothian, Josh; Powers, Sarah S; Sullivan, Blair D; Baker, Matthew B; Schrock, Jonathan; Poole, Stephen W

    2013-12-01

    The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allowed the emulation of dierent application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report examines existing synthetic graph generator implementations in preparation for further study on the properties of their generated synthetic graphs.

  10. Graphs, matrices, and the GraphBLAS: Seven good reasons

    SciTech Connect (OSTI)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

  11. Graphs, matrices, and the GraphBLAS: Seven good reasons

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

  12. Subdominant pseudoultrametric on graphs

    SciTech Connect (OSTI)

    Dovgoshei, A A; Petrov, E A

    2013-08-31

    Let (G,w) be a weighted graph. We find necessary and sufficient conditions under which the weight w:E(G)?R{sup +} can be extended to a pseudoultrametric on V(G), and establish a criterion for the uniqueness of such an extension. We demonstrate that (G,w) is a complete k-partite graph, for k?2, if and only if for any weight that can be extended to a pseudoultrametric, among all such extensions one can find the least pseudoultrametric consistent with w. We give a structural characterization of graphs for which the subdominant pseudoultrametric is an ultrametric for any strictly positive weight that can be extended to a pseudoultrametric. Bibliography: 14 titles.

  13. TOTAL ARRA Homes Weatherized thru Q2 2010 8.19.10.xls | Department of

    Broader source: Energy.gov (indexed) [DOE]

    Energy TOTAL ARRA Homes Weatherized thru Q2 2010 8.19.10.xls (14.26 KB) More Documents & Publications U.S. Department of Energy Weatherization Assistance Program Homes Weatherized By State through 06/30/2010 (Calendar Year) Homes Weatherized by State March 2010 ARRA Homes Weatherized by Grantee

  14. GraphLib

    Energy Science and Technology Software Center (OSTI)

    2013-02-19

    This library is used in several LLNL projects, including STAT (the Stack Trace Analysis Tool for scalable debugging) and some modules in P^nMPI (a tool MPI tool infrastructure). It can also be used standalone for creating and manipulationg graphs, but its API is primarily tuned to support these other projects

  15. Temporal Representation in Semantic Graphs

    SciTech Connect (OSTI)

    Levandoski, J J; Abdulla, G M

    2007-08-07

    A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

  16. Simple and Flexible Scene Graph

    Energy Science and Technology Software Center (OSTI)

    2007-10-01

    The system implements a flexible and extensible scene graph for the visualization and analysis of scientific information.

  17. A Clustering Graph Generator

    SciTech Connect (OSTI)

    Winlaw, Manda; De Sterck, Hans; Sanders, Geoffrey

    2015-10-26

    In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps to understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.

  18. Graph Coarsening for Path Finding in Cybersecurity Graphs

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh

    2013-01-01

    n the pass-the-hash attack, hackers repeatedly steal password hashes and move through a computer network with the goal of reaching a computer with high level administrative privileges. In this paper we apply graph coarsening in network graphs for the purpose of detecting hackers using this attack or assessing the risk level of the network's current state. We repeatedly take graph minors, which preserve the existence of paths in the graph, and take powers of the adjacency matrix to count the paths. This allows us to detect the existence of paths as well as find paths that have high risk of being used by adversaries.

  19. Quantum Graph Analysis

    SciTech Connect (OSTI)

    Maunz, Peter Lukas Wilhelm; Sterk, Jonathan David; Lobser, Daniel; Parekh, Ojas D.; Ryan-Anderson, Ciaran

    2016-01-01

    In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

  20. A Collection of Features for Semantic Graphs

    SciTech Connect (OSTI)

    Eliassi-Rad, T; Fodor, I K; Gallagher, B

    2007-05-02

    Semantic graphs are commonly used to represent data from one or more data sources. Such graphs extend traditional graphs by imposing types on both nodes and links. This type information defines permissible links among specified nodes and can be represented as a graph commonly referred to as an ontology or schema graph. Figure 1 depicts an ontology graph for data from National Association of Securities Dealers. Each node type and link type may also have a list of attributes. To capture the increased complexity of semantic graphs, concepts derived for standard graphs have to be extended. This document explains briefly features commonly used to characterize graphs, and their extensions to semantic graphs. This document is divided into two sections. Section 2 contains the feature descriptions for static graphs. Section 3 extends the features for semantic graphs that vary over time.

  1. Graph Partitioning and Sequencing Software

    Energy Science and Technology Software Center (OSTI)

    1995-09-19

    Graph partitioning is a fundemental problem in many scientific contexts. CHACO2.0 is a software package designed to partition and sequence graphs. CHACO2.0 allows for recursive application of several methods for finding small edge separators in weighted graphs. These methods include inertial, spectral, Kernighan Lin and multilevel methods in addition to several simpler strategies. Each of these approaches can be used to partition the graph into two, four, or eight pieces at each level of recursion.more » In addition, the Kernighan Lin method can be used to improve partitions generated by any of the other algorithms. CHACO2.0 can also be used to address various graph sequencing problems, with applications to scientific computing, database design, gene sequencing and other problems.« less

  2. Petroleum Supply Annual 2005, Volume 1

    Gasoline and Diesel Fuel Update (EIA)

    Districts PDF CSV XLS Refinery Capacity Tables Refinery Capacity Report HTML Appendices A District Descriptions and Maps PDF B Detailed Statistics Explanatory Notes PDF C Northeast...

  3. Khovanov homology of graph-links

    SciTech Connect (OSTI)

    Nikonov, Igor M

    2012-08-31

    Graph-links arise as the intersection graphs of turning chord diagrams of links. Speaking informally, graph-links provide a combinatorial description of links up to mutations. Many link invariants can be reformulated in the language of graph-links. Khovanov homology, a well-known and useful knot invariant, is defined for graph-links in this paper (in the case of the ground field of characteristic two). Bibliography: 14 titles.

  4. Graph Analytics for Signature Discovery

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh; Lo, Chaomei

    2013-06-01

    Within large amounts of seemingly unstructured data it can be diffcult to find signatures of events. In our work we transform unstructured data into a graph representation. By doing this we expose underlying structure in the data and can take advantage of existing graph analytics capabilities, as well as develop new capabilities. Currently we focus on applications in cybersecurity and communication domains. Within cybersecurity we aim to find signatures for perpetrators using the pass-the-hash attack, and in communications we look for emails or phone calls going up or down a chain of command. In both of these areas, and in many others, the signature we look for is a path with certain temporal properties. In this paper we discuss our methodology for finding these temporal paths within large graphs.

  5. Graph modeling systems and methods

    SciTech Connect (OSTI)

    Neergaard, Mike

    2015-10-13

    An apparatus and a method for vulnerability and reliability modeling are provided. The method generally includes constructing a graph model of a physical network using a computer, the graph model including a plurality of terminating vertices to represent nodes in the physical network, a plurality of edges to represent transmission paths in the physical network, and a non-terminating vertex to represent a non-nodal vulnerability along a transmission path in the physical network. The method additionally includes evaluating the vulnerability and reliability of the physical network using the constructed graph model, wherein the vulnerability and reliability evaluation includes a determination of whether each terminating and non-terminating vertex represents a critical point of failure. The method can be utilized to evaluate wide variety of networks, including power grid infrastructures, communication network topologies, and fluid distribution systems.

  6. Dr.L: Distributed Recursive (Graph) Layout

    Energy Science and Technology Software Center (OSTI)

    2007-11-19

    Dr. L provides two-dimensional visualizations of very large abstract graph structures. it can be used for data mining applications including biology, scientific literature, and social network analysis. Dr. L is a graph layout program that uses a multilevel force-directed algorithm. A graph is input and drawn using a force-directed algorithm based on simulated annealing. The resulting layout is clustered using a single link algorithm. This clustering is used to produce a coarsened graph (fewer nodes)more » which is then re-drawn. this process is repeated until a sufficiently small graph is produced. The smallest graph is drawn and then used as a basis for drawing the original graph by refining the series of coarsened graphs that were produced. The layout engine can be run in serial or in parallel.« less

  7. PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

    Energy Science and Technology Software Center (OSTI)

    2012-01-04

    PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables acrossmore » different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.« less

  8. PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

    SciTech Connect (OSTI)

    2012-01-04

    PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables across different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.

  9. Archived Weekly Files, Revised, 1984 Forward EIA revises its...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    XLS XLS 1990 XLS XLS 1989 XLS XLS 1988 XLS XLS 1987 XLS XLS 1986 XLS XLS 1985 XLS XLS 1984 XLS XLS Original estimates* year weekly monthly 2015 XLS XLS 2014 XLS XLS 2013 XLS XLS...

  10. API Requirements for Dynamic Graph Prediction

    SciTech Connect (OSTI)

    Gallagher, B; Eliassi-Rad, T

    2006-10-13

    Given a large-scale time-evolving multi-modal and multi-relational complex network (a.k.a., a large-scale dynamic semantic graph), we want to implement algorithms that discover patterns of activities on the graph and learn predictive models of those discovered patterns. This document outlines the application programming interface (API) requirements for fast prototyping of feature extraction, learning, and prediction algorithms on large dynamic semantic graphs. Since our algorithms must operate on large-scale dynamic semantic graphs, we have chosen to use the graph API developed in the CASC Complex Networks Project. This API is supported on the back end by a semantic graph database (developed by Scott Kohn and his team). The advantages of using this API are (i) we have full-control of its development and (ii) the current API meets almost all of the requirements outlined in this document.

  11. Fast generation of sparse random kernel graphs

    SciTech Connect (OSTI)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  12. Fast generation of sparse random kernel graphs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  13. Graph algorithms in the titan toolkit.

    SciTech Connect (OSTI)

    McLendon, William Clarence, III; Wylie, Brian Neil

    2009-10-01

    Graph algorithms are a key component in a wide variety of intelligence analysis activities. The Graph-Based Informatics for Non-Proliferation and Counter-Terrorism project addresses the critical need of making these graph algorithms accessible to Sandia analysts in a manner that is both intuitive and effective. Specifically we describe the design and implementation of an open source toolkit for doing graph analysis, informatics, and visualization that provides Sandia with novel analysis capability for non-proliferation and counter-terrorism.

  14. Enabling Graph Appliance for Genome Assembly

    SciTech Connect (OSTI)

    Singh, Rina; Graves, Jeffrey A; Lee, Sangkeun; Sukumar, Sreenivas R; Shankar, Mallikarjun

    2015-01-01

    In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers developed have used de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multi-threaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray s Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing a de Bruin graphs as RDF graphs and propose an iterative querying approach for finding Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

  15. Useful Graphs and Charts - Ion Beams - Radiation Effects Facility...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Times 15 MeVu LET vs Range Graph 25 MeVu LET vs Range Graph 40 Mevu LET vs Range Graph Radiation Effects Facility Cyclotron Institute Texas A&M University MS 3366 ...

  16. Graph Mining Meets the Semantic Web

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

    2015-01-01

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.

  17. GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

    SciTech Connect (OSTI)

    Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil; Schwan, Karsten

    2015-11-15

    Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and device.

  18. GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

    SciTech Connect (OSTI)

    Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen; Schwan, Karsten

    2015-09-30

    Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.

  19. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

  20. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existingmore » graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.« less

  1. The MultiThreaded Graph Library (MTGL)

    Energy Science and Technology Software Center (OSTI)

    2008-07-17

    The MultiThreaded Graph Library (MTGL) is a set of header files that implement graph algorithm in such a way that they can run on massively multithreaded architectures. It is based upon the Boost Graph Library, but doesn’t use Boost since the latter doesn’t run well on these architectures.

  2. Bipartite graph partitioning and data clustering

    SciTech Connect (OSTI)

    Zha, Hongyuan; He, Xiaofeng; Ding, Chris; Gu, Ming; Simon, Horst D.

    2001-05-07

    Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, the authors propose a new data clustering method based on partitioning the underlying biopartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. They show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. They point out the connection of their clustering algorithm to correspondence analysis used in multivariate analysis. They also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, they apply their clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.

  3. Visualization Graph | OpenEI Community

    Open Energy Info (EERE)

    8 August, 2012 - 12:37 New Gapminder Visualizations Added EIA Energy data Gapminder OECD OpenEI SEDS Visualization Graph OpenEI now features some cool new Gapminder...

  4. Fault-tolerant dynamic task graph scheduling

    SciTech Connect (OSTI)

    Kurt, Mehmet C.; Krishnamoorthy, Sriram; Agrawal, Kunal; Agrawal, Gagan

    2014-11-16

    In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. We elicit from the user the basic task graph structure in terms of successor and predecessor relationships. The work stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and meta-data associated with a task get corrupted. We use this redundancy, and the knowledge of the task graph structure, to selectively recover from faults with low space and time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.

  5. Bayati Kim Saberi random graph sampler

    Energy Science and Technology Software Center (OSTI)

    2012-06-05

    This software package implements the algorithm from a paper by Bayati, Kim, and Saberi (first reference below) to generate a uniformly random sample of a graph with a prescribed degree distribution.

  6. Accelerating semantic graph databases on commodity clusters

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Haglin, David J.; Feo, John T.; Weaver, Jesse R.; Tumeo, Antonino; Villa, Oreste

    2013-10-06

    We are developing a full software system for accelerating semantic graph databases on commodity cluster that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to C++ compiler, a library of parallel graph methods and a custom multithreaded runtime layer, which provides a Partitioned Global Address Space (PGAS) programming model with fork/join parallelism and automatic load balancing over a commodity clusters. We present preliminary results for the compiler and for the runtime.

  7. Graph representation of protein free energy landscape

    SciTech Connect (OSTI)

    Li, Minghai; Duan, Mojie; Fan, Jue; Huo, Shuanghong; Han, Li

    2013-11-14

    The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation of free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.

  8. Continuous-time quantum walks on star graphs

    SciTech Connect (OSTI)

    Salimi, S.

    2009-06-15

    In this paper, we investigate continuous-time quantum walk on star graphs. It is shown that quantum central limit theorem for a continuous-time quantum walk on star graphs for N-fold star power graph, which are invariant under the quantum component of adjacency matrix, converges to continuous-time quantum walk on K{sub 2} graphs (complete graph with two vertices) and the probability of observing walk tends to the uniform distribution.

  9. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2015-01-27

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  10. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2016-04-12

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  11. Communication Graph Generator for Parallel Programs

    Energy Science and Technology Software Center (OSTI)

    2014-04-08

    Graphator is a collection of relatively simple sequential programs that generate communication graphs/matrices for commonly occurring patterns in parallel programs. Currently, there is support for five communication patterns: two-dimensional 4-point stencil, four-dimensional 8-point stencil, all-to-alls over sub-communicators, random near-neighbor communication, and near-neighbor communication.

  12. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    SciTech Connect (OSTI)

    Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D; Reed, Joel W; Goodall, John R

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  13. StreamWorks - A system for Dynamic Graph Search

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Ray, Abhik; Beus, Sherman J.; Feo, John T.

    2013-06-11

    Acting on time-critical events by processing ever growing social media, news or cyber data streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Mining and searching for subgraph patterns in a continuous setting requires an efficient approach to incremental graph search. The goal of our work is to enable real-time search capabilities for graph databases. This demonstration will present a dynamic graph query system that leverages the structural and semantic characteristics of the underlying multi-relational graph.

  14. Modular Environment for Graph Research and Analysis with a Persistent

    Energy Science and Technology Software Center (OSTI)

    2009-11-18

    The MEGRAPHS software package provides a front-end to graphs and vectors residing on special-purpose computing resources. It allows these data objects to be instantiated, destroyed, and manipulated. A variety of primitives needed for typical graph analyses are provided. An example program illustrating how MEGRAPHS can be used to implement a PageRank computation is included in the distribution.The MEGRAPHS software package is targeted towards developers of graph algorithms. Programmers using MEGRAPHS would write graph analysis programsmore » in terms of high-level graph and vector operations. These computations are transparently executed on the Cray XMT compute nodes.« less

  15. Knowledge Representation Issues in Semantic Graphs for Relationship Detection

    SciTech Connect (OSTI)

    Barthelemy, M; Chow, E; Eliassi-Rad, T

    2005-02-02

    An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' may be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.

  16. Frequent Subgraph Discovery in Large Attributed Streaming Graphs

    SciTech Connect (OSTI)

    Ray, Abhik; Holder, Larry; Choudhury, Sutanay

    2014-08-13

    The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

  17. Graph processing platforms at scale: practices and experiences

    SciTech Connect (OSTI)

    Lim, Seung-Hwan; Lee, Sangkeun; Brown, Tyler C; Sukumar, Sreenivas R; Ganesh, Gautam

    2015-01-01

    Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution, connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.

  18. Jargon and Graph Modularity on Twitter

    SciTech Connect (OSTI)

    Dowling, Chase P.; Corley, Courtney D.; Farber, Robert M.; Reynolds, William

    2013-09-01

    The language of conversation is just as dependent upon word choice as it is on who is taking part. Twitter provides an excellent test-bed in which to conduct experiments not only on language usage but on who is using what language with whom. To this end, we combine large scale graph analytical techniques with known socio-linguistic methods. In this article we leverage both expert curated vocabularies and naive mathematical graph analyses to determine if network behavior on Twitter corroborates with the current understanding of language usage. The results reported indicate that, based on networks constructed from user to user communication and communities identified using the Clauset- Newman greedy modularity algorithm we find that more prolific users of these curated vocabularies are concentrated in distinct network communities.

  19. Mining Graphs for Understanding Time-Varying Volumetric Data...

    Office of Scientific and Technical Information (OSTI)

    SciTech Connect Search Results Journal Article: Mining Graphs for Understanding ... DOE Contract Number: AC02-06CH11357 Resource Type: Journal Article Resource Relation: ...

  20. A Graph Search Heuristic for Shortest Distance Paths

    SciTech Connect (OSTI)

    Chow, E

    2005-03-24

    This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.

  1. Sequoia supercomputer tops Graph 500 | National Nuclear Security...

    National Nuclear Security Administration (NNSA)

    Lawrence Livermore National Laboratory scientists' search for new ways to solve large complex national security problems led to the top ranking on Graph 500 and new techniques for ...

  2. Two linear time, low overhead algorithms for graph layout

    Energy Science and Technology Software Center (OSTI)

    2008-01-10

    The software comprises two algorithms designed to perform a 2D layout of a graph structure in time linear with respect to the vertices and edges in the graph, whereas most other layout algorithms have a running time that is quadratic with respect to the number of vertices or greater. Although these layout algorithms run in a fraction of the time as their competitors, they provide competitive results when applied to most real-world graphs. These algorithmsmore » also have a low constant running time and small memory footprint, making them useful for small to large graphs.« less

  3. International energy indicators. [Statistical tables and graphs

    SciTech Connect (OSTI)

    Bauer, E.K.

    1980-05-01

    International statistical tables and graphs are given for the following: (1) Iran - Crude Oil Capacity, Production and Shut-in, June 1974-April 1980; (2) Saudi Arabia - Crude Oil Capacity, Production, and Shut-in, March 1974-Apr 1980; (3) OPEC (Ex-Iran and Saudi Arabia) - Capacity, Production and Shut-in, June 1974-March 1980; (4) Non-OPEC Free World and US Production of Crude Oil, January 1973-February 1980; (5) Oil Stocks - Free World, US, Japan, and Europe (Landed, 1973-1st Quarter, 1980); (6) Petroleum Consumption by Industrial Countries, January 1973-December 1979; (7) USSR Crude Oil Production and Exports, January 1974-April 1980; and (8) Free World and US Nuclear Generation Capacity, January 1973-March 1980. Similar statistical tables and graphs included for the United States include: (1) Imports of Crude Oil and Products, January 1973-April 1980; (2) Landed Cost of Saudi Oil in Current and 1974 Dollars, April 1974-January 1980; (3) US Trade in Coal, January 1973-March 1980; (4) Summary of US Merchandise Trade, 1976-March 1980; and (5) US Energy/GNP Ratio, 1947 to 1979.

  4. Scaling Semantic Graph Databases in Size and Performance

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Villa, Oreste; Tumeo, Antonino; Weaver, Jesse R.; Haglin, David J.; Choudhury, Sutanay; Feo, John T.

    2014-08-06

    In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grained data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.

  5. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    SciTech Connect (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'

  6. Query optimization for graph analytics on linked data using SPARQL

    SciTech Connect (OSTI)

    Hong, Seokyong; Lee, Sangkeun; Lim, Seung -Hwan; Sukumar, Sreenivas R.; Vatsavai, Ranga Raju

    2015-07-01

    Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

  7. An Experiment on Graph Analysis Methodologies for Scenarios

    SciTech Connect (OSTI)

    Brothers, Alan J.; Whitney, Paul D.; Wolf, Katherine E.; Kuchar, Olga A.; Chin, George

    2005-09-30

    Visual graph representations are increasingly used to represent, display, and explore scenarios and the structure of organizations. The graph representations of scenarios are readily understood, and commercial software is available to create and manage these representations. The purpose of the research presented in this paper is to explore whether these graph representations support quantitative assessments of the underlying scenarios. The underlying structure of the scenarios is the information that is being targeted in the experiment and the extent to which the scenarios are similar in content. An experiment was designed that incorporated both the contents of the scenarios and analysts’ graph representations of the scenarios. The scenarios’ content was represented graphically by analysts, and both the structure and the semantics of the graph representation were attempted to be used to understand the content. The structure information was not found to be discriminating for the content of the scenarios in this experiment; but, the semantic information was discriminating.

  8. Graph facilitates tracking water and gas influx

    SciTech Connect (OSTI)

    Gruy, H.J. )

    1990-03-26

    Graphing the vertical distribution of reservoir volume is an easy method for estimating the acre-ft remaining to be exploited in reservoirs with water or gas encroachment. To evaluate reservoir performance and estimate oil and gas reserves in water-drive reservoirs or oil reservoirs with a gas cap, it is necessary to determine the magnitude of the movement of oil-water and gas-oil contact surfaces. In reviewing reserve estimates and reservoir studies done by others, the authors have found that very few reservoir engineers or geologists have an easy method for tracking the movement of these surfaces and estimating the volumes of oil displaced water encroachment, gas cap expansion, or the volumes of oil lost by wetting the gas cap. The following method evolved from the author's studies of the East Texas field starting in 1942, and it took this form in the early 1950s.

  9. Composing Data Parallel Code for a SPARQL Graph Engine

    SciTech Connect (OSTI)

    Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste; Haglin, David J.; Feo, John

    2013-09-08

    Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basic graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.

  10. Parallel Algorithms for Graph Optimization using Tree Decompositions

    SciTech Connect (OSTI)

    Sullivan, Blair D; Weerapurage, Dinesh P; Groer, Christopher S

    2012-06-01

    Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

  11. Data Sources For Emerging Technologies Program MYPP Target Graphs

    Broader source: Energy.gov [DOE]

    The BTO Emerging Technologies Accomplishments and Outcomes – 2015 page contains graphs on Multi-Year Program Plan R&D targets for certain technologies. This page contains information on data...

  12. Must all charting and graphing code be written in javascript...

    Open Energy Info (EERE)

    Must all charting and graphing code be written in javascript? Home > Groups > Databus In the documentation chapter entitled Developing charts using 3rd party api, we are told that...

  13. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    Energy Science and Technology Software Center (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graphmore » mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

  14. TIFF Image Writer patch for OpenSceneGraph

    Energy Science and Technology Software Center (OSTI)

    2012-01-05

    This software consists of code modifications to the open-source OpenSceneGraph software package to enable the creation of TlFF images containing 16 bit unsigned data. They also allow the user to disable compression and set the DPI tags in the resulting TIFF Images. Some image analysis programs require uncompressed, 16 bit unsigned input data. These code modifications allow programs based on OpenSceneGraph to write out such images, improving connectivity between applications.

  15. Highly Asynchronous VisitOr Queue Graph Toolkit

    Energy Science and Technology Software Center (OSTI)

    2012-10-01

    HAVOQGT is a C++ framework that can be used to create highly parallel graph traversal algorithms. The framework stores the graph and algorithmic data structures on external memory that is typically mapped to high performance locally attached NAND FLASH arrays. The framework supports a vertex-centered visitor programming model. The frameworkd has been used to implement breadth first search, connected components, and single source shortest path.

  16. The peculiar phase structure of random graph bisection

    SciTech Connect (OSTI)

    Percus, Allon G; Istrate, Gabriel; Goncalves, Bruno T; Sumi, Robert Z

    2008-01-01

    The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of 'cut' edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.

  17. b22.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Mall buildings add an estimated 213 thousand buildings comprising 6.9 billion square feet. a "Other" includes wood, coal, solar, and all other energy sources. QData withheld ...

  18. table11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... 14.1 NA 17.9 18.3 19.6 20.1 Table 11. Fuel Economy, Selected Survey Years (Miles Per Gallon) Survey Years Page A-1 of A-5 1983 1985 1988...

  19. c21.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    per Building (gallons) per Square Foot (gallons) per Worker (gallons) per Building (thousand dollars) per Square Foot (dollars) per Gallon (dollars) All Buildings...

  20. c15.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    25th Percentile Median 75th Percentile per Building (thousand dollars) per Square Foot (dollars) per Thousand Cubic Feet (dollars) All Buildings ......

  1. c25.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    per Building (million Btu) per Square Foot (thousand Btu) per Worker (million Btu) per Building (thousand dollars) per Square Foot (dollars) per Thousand Pounds (dollars) All...

  2. c16.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    per Building (thousand dollars) per Square Foot (dollars) per Thousand Cubic Feet (dollars) All Buildings ... 736 43.2 34.9 15.7 34.1 75.4...

  3. c2.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 567 19,482 34,904 24,710 6,466 724 3,003 Separate Computer Area ... 553 26,873 44,552 33,308 6,230 732 4,282 HVAC...

  4. c34.xls

    Gasoline and Diesel Fuel Update (EIA)

    Large Amounts of Hot Water ... 8,391 0.09 71.2 8.9 0.09 1.06 Separate Computer Area ... 8,742 0.07 43.7 9.2 0.07 1.05 HVAC Conservation Features...

  5. c9.xls

    Gasoline and Diesel Fuel Update (EIA)

    Hot Water ... 222 182 239 1,776 1,384 2,048 124.9 131.3 116.8 Separate Computer Area ... 290 196 262 3,132 1,607 3,462 92.5 121.9 75.6 HVAC...

  6. c24.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 2,235 56.3 50.2 27.2 62.9 141.0 16.0 0.40 7.16 Separate Computer Area ... 2,276 41.2 29.9 14.4 30.9 58.2 16.7 0.30 7.34 HVAC...

  7. c13.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 603 17.6 15.7 7.2 13.2 26.0 43.6 1.27 0.072 Separate Computer Area ... 821 16.9 12.0 6.6 11.5 19.2 60.2 1.24 0.073 HVAC...

  8. c17.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 15 40 56 995 2,927 3,546 15.2 13.5 15.7 Separate Computer Area ... 17 75 73 1,045 4,880 4,759 16.6 15.4 15.3 HVAC...

  9. c19.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 36 26 35 1,776 1,384 2,048 20.5 19.0 17.1 Separate Computer Area ... 58 30 49 3,132 1,607 3,462 18.4 18.6 14.3 HVAC...

  10. c32.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 249 437 217 4,152 7,176 4,694 59.9 60.9 46.2 Separate Computer Area ... 238 418 192 5,023 10,078 5,514 47.4 41.5 34.9 HVAC...

  11. c18.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 24 94 16 1,678 4,178 949 14.3 22.4 17.2 Separate Computer Area ... 26 106 20 1,723 5,236 1,028 15.1 20.3 19.1 HVAC...

  12. c36.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Hot Water ... 595 42 Q Q 1.04 1.07 1.15 1.30 0.23 0.03 0.02 Q Separate Computer Area ... 576 45 66 Q 1.03 1.08 1.11 1.30 0.16 0.02 0.02 Q HVAC...

  13. c4.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 567 19,482 34.4 34,904 61.6 1.79 14.16 Separate Computer Area ... 553 26,873 48.6 44,552 80.6 1.66 15.39 HVAC Conservation...

  14. c8.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 193 518 115 1,678 4,178 949 115.2 124.1 121.6 Separate Computer Area ... 173 532 121 1,723 5,236 1,028 100.5 101.6 117.9 HVAC...

  15. c7.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 139 367 490 995 2,927 3,546 139.5 125.4 138.1 Separate Computer Area ... 158 605 558 1,045 4,880 4,759 151.0 124.0 117.3 HVAC...

  16. c11.xls

    Gasoline and Diesel Fuel Update (EIA)

    Water ... 303 757 1,405 1,477 7,554 10,451 204.9 100.3 134.5 Separate Computer Area ... 87 959 1,849 969 10,433 15,471 89.8 92.0 119.5 HVAC...

  17. c35.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Water ... 574 40 47 Q 2,577 1,652 2,380 1,081 0.22 0.02 0.02 Q Separate Computer Area ... 560 41 59 35 3,623 1,957 2,916 1,756 0.15 0.02 0.02 Q HVAC...

  18. c33.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Large Amounts of Hot Water ... 8,391 0.09 71.2 8.9 0.09 1.06 Separate Computer Area ... 8,742 0.07 43.7 9.2 0.07 1.05 HVAC Conservation Features...

  19. c29.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 77 80 91 1,575 1,126 1,678 48.7 71.1 54.3 Separate Computer Area ... 65 77 59 2,253 1,296 2,543 29.0 59.5 23.2 HVAC...

  20. c31.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 157 290 455 1,022 5,671 9,329 153.5 51.2 48.8 Separate Computer Area ... 28 307 513 578 7,533 12,505 49.3 40.8 41.0 Energy...

  1. c28.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 86 130 49 1,391 2,806 833 62.1 46.5 58.9 Separate Computer Area ... 63 89 37 1,345 3,137 900 46.7 28.3 41.1 HVAC Conservation...

  2. c3.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 567 19,482 34.4 2,465 4,349 126.6 113.3 Separate Computer Area ... 553 26,873 48.6 2,895 5,236 107.7 76.5 HVAC Conservation...

  3. c27.xls

    Gasoline and Diesel Fuel Update (EIA)

    of Hot Water ... 38 130 221 652 2,652 3,310 58.7 48.9 66.9 Separate Computer Area ... 48 190 220 685 4,197 4,260 69.7 45.3 51.8 HVAC...

  4. c38.xls

    Gasoline and Diesel Fuel Update (EIA)

    Amounts of Hot Water ... 14,656 120.84 86.8 161.3 1.33 11.00 Separate Computer Area ... 19,658 114.53 68.8 224.9 1.31 11.44 HVAC Conservation...

  5. c12.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Hot Water ... 533 1,271 661 4,912 9,140 5,430 108.6 139.1 121.8 Separate Computer Area ... 630 1,561 703 6,222 13,495 7,156 101.3 115.7 98.3 HVAC...

  6. c23.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    of Hot Water ... 2,235 56.3 50.2 27.2 62.9 141.0 16.0 0.40 7.16 Separate Computer Area ... 2,276 41.2 29.9 14.4 30.9 58.2 16.7 0.30 7.34 HVAC...

  7. 2010 APS.xls

    Energy Savers [EERE]

    Department of Energy (DOE) Savannah River Site (SRS) Jan-10 Estimated Schedule (**NEPA ... plutonium in the Waste Isolation Pilot Plant (WIPP) is a reasonable alternative. ...

  8. EIA-912.xls

    Gasoline and Diesel Fuel Update (EIA)

    over the web using secure, encrypted processes. (It is the same method that commercial companies communicate with customers when transacting business on the web.) To use this ...

  9. b28.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    188 94 68 Q N Food Service ...... 297 282 94 149 Q Q Health Care ...... 129 124 49 65 Q 1 Inpatient ...

  10. b29.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    437 568 Q N Food Service ...... 1,654 1,608 436 957 Q Q Health Care ...... 3,163 3,100 592 1,972 Q 388 Inpatient ...

  11. a3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Q Q Q Food Service ...... 297 Q 27 54 34 61 24 42 Q 34 Health Care ...... 129 Q 17 20 11 27 11 10 13 18 Inpatient ...

  12. a7.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    203 Q N N Q N Food Service ...... 297 270 26 Q N N N Health Care ...... 129 91 34 Q Q Q N Inpatient ...

  13. b40.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    297 283 69 30 33 Q Q 176 Q Q Health Care ............ 195 171 50 35 39 Q Q 78 Q Q Health Care Complex ...... 39 ...

  14. a5.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Q Q Q Q N N Food Service ...... 297 202 65 23 Q Q N Q N Health Care ...... 129 56 38 19 5 5 3 2 1 Inpatient ...

  15. b26.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    79 Q N Q Q Food Service ...... 297 282 125 171 Q Q 31 Q Health Care ...... 129 124 62 68 Q 2 Q Q Inpatient ...

  16. a6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Q Q N N Food Service ...... 1,654 544 442 345 Q Q N Q N Health Care ...... 3,163 165 280 313 157 364 395 514 973 ...

  17. c1.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Q District Heat ... 27 3,088 8,155 4,241 218 Q 3,690 Propane ... 128 1,422 1,871 1,734 Q Q Q Cooking...

  18. AWGagenda_033009.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Monday, March 30, 1:00-3:00 PM Time Speaker Title 1:00 Jefferson The AOS system at SGP, BRW, and AMF China 1:15 Hallar The AMF2 deployment at Storm Peak Laboratory for StormVEx ...

  19. oil2001.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Below Poverty Line 100 Percent 1.4 1.1 2.2 59 29 46.8 18 538 0.27 429 163 125 Percent 1.9 ... were conducted. (4) Below 150 percent of poverty line or 60 percent of median State ...

  20. oil1990.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Below Poverty Line 100 Percent 2.1 1.5 3.0 72 37 52.8 16 573 0.30 417 128 150 Percent 3.0 ... for 1990. (3) Below 150 percent of poverty line or 60 percent of median State ...

  1. oil1987.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Average Fuel OilKerosene Consumption Expenditures Below Poverty Line 100 Percent 2.0 1.4 ... for 1987. (3) Below 150 percent of poverty line or 60 percent of median State ...

  2. oil1997.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Below Poverty Line 100 Percent 2.0 1.2 2.2 102 56 63.0 23 658 0.36 405 146 125 Percent 2.6 ... were conducted. (6) Below 150 percent of poverty line or 60 percent of median State ...

  3. oil1982.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Below Poverty Line 100 Percent 2.1 1.3 2.7 128 59 78.3 26 1,082 0.50 662 216 125 Percent 3.0 2.0 4.3 118 55 77.2 26 1,002 0.47 653 217 per Total per Square per per per Total Total ...

  4. oil1993.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Below Poverty Line 100 Percent 1.9 1.3 2.8 80 38 55.4 20 501 0.24 347 126 125 Percent 2.7 ... Energy Consumption Survey. (4) Below 150 percent of poverty line or 60 percent of median ...

  5. oil1984.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Below Poverty Line 100 Percent 2.6 1.8 3.3 98 53 69.1 24 745 0.40 525 186 125 Percent 3.7 ... for 1984. (3) Below 150 percent of poverty line or 60 percent of median State income. ...

  6. oil1981.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Below Poverty Line 100 Percent 1.5 1.1 2.1 101 53 71.8 25 902 0.47 639 220 125 Percent 2.4 1.7 3.3 108 55 76.3 28 958 0.48 677 250 per Total per Square per per per Total Total ...

  7. b34.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table. Both can be accessed from the CBECS web site - http:www.eia.doe.govemeucbecs. ...

  8. b37.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Principal Building Activity Education ......Energy Information Administration 2003 Commercial Buildings ... 50,732 43,125 Energy Management and Control System ...

  9. b36.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Principal Building Activity Education ......Energy Information Administration 2003 Commercial Buildings ... 14,735 25,429 Energy Management and Control System ...

  10. table13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Survey Years (Nominal Dollars) Survey Years Household Composition Households With Children... NA NA 599 708 722 886 Age of Oldest Child Under...

  11. table12.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Years (Billion Nominal Dollars) Survey Years Household Composition Households With Children... NA NA 35.9 46.1 46.7 70.7 Age of Oldest Child...

  12. table2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Vehicles, Selected Survey Years Survey Years Household Composition Households With Children... NA NA 91 92 91 93 Age of Oldest Child Under 7...

  13. table1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Selected Survey Years (Millions) Survey Years Household Composition Households With Children... NA NA 29.9 33.0 32.1 37.1 Age of Oldest Child...

  14. table8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Survey Years (Billion Gallons) Survey Years Household Composition Households With Children... NA NA 36.4 38.9 40.4 53.1 Age of Oldest Child...

  15. table4.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Household, Selected Survey Years Survey Years Household Composition Households With Children... NA NA 2.0 2.0 2.0 2.2 Age of Oldest Child Under...

  16. table10.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    1,520 1,450 1,449 1,265 1,411 1,665 Household Composition Households With Children... NA NA 1,216 1,176 1,257 1,429 Age of Oldest Child Under 7...

  17. table14.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Survey Years (Nominal Dollars) Survey Years Household Composition Households With Children... NA NA 1,198 1,395 1,453 1,903 Age of Oldest...

  18. table3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Selected Survey Years (Millions) Survey Years Household Composition Households With Children... NA NA 59.8 65.1 64.6 79.8 Age of Oldest Child...

  19. table5.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Selected Survey Years (Billions) Survey Years Household Composition Households With Children... NA NA 674 753 796 1,078 Age of Oldest Child...

  20. table7.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Selected Survey Years (Thousands) Survey Years Household Composition Households With Children... NA NA 22.5 22.8 24.8 29.2 Age of Oldest Child...

  1. eia-910.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Indicate unit of measure by placing an "X" in the appropriate box. Commercial Residential ... Address 1: OOG.SURVEYS@eia.gov Contact Name: Fax: (202) 586-1076 Ext: Fax No.: enter an "X...

  2. nstec_home.xls

    National Nuclear Security Administration (NNSA)

    1 11767 1 11772 1 11778 1 11787 1 12144 1 12170 1 12189 1 12569 1 14625 1 NY Total 20 OK 73044 1 OK Total 1 PA 17302 1 PA Total 1 SC 29715 1 29909 1 SC Total 2 TN 37604 1 37722...

  3. c30.xls

    Gasoline and Diesel Fuel Update (EIA)

    27.3 Building Floorspace (Square Feet) 1,001 to 5,000 ... 56 81 35 55 16 660 979 421 789 234 85.0 82.9 82.5 69.8 66.6 5,001 to 10,000...

  4. c26.xls

    Gasoline and Diesel Fuel Update (EIA)

    3,553 4,844 3,866 2,261 8.56 7.09 8.40 7.28 0.39 0.37 0.29 0.29 Building Floorspace (Square Feet) 1,001 to 5,000 ... 456 782 599 317 9.84 8.57 9.21...

  5. c21.xls

    Gasoline and Diesel Fuel Update (EIA)

    Q 14.5 18.7 Buildings without Cooling ... 11 8 Q 2,142 2,757 Q 5.2 2.8 7.7 Water-Heating Energy Sources Electricity ... 88 163...

  6. c15.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 7 Q 3 6 1,855 2,232 1,214 1,080 3.6 6.4 2.6 5.8 Water-Heating Energy Sources Electricity ... 57 86...

  7. c14.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    0.069 Buildings without Cooling ... 39 4.8 11.8 1.1 2.4 5.1 3.2 0.39 0.082 Water-Heating Energy Sources Electricity ... 211...

  8. c20.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 7 Q 1 5 Q 1,843 2,567 430 1,195 Q 4.0 6.3 3.0 4.1 Q Water-Heating Energy Sources Electricity ... 43 88 77...

  9. c22.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    19.1 Buildings without Cooling ... Q 8 4 3,308 1,832 1,241 5.7 4.4 2.9 Water-Heating Energy Sources Electricity ... 51 216...

  10. c16.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 741 Q 279 708 0.11 0.05 0.09 0.11 0.40 0.33 0.23 0.66 Water-Heating Energy Sources Electricity ... 5,313...

  11. OMBDOEFAIR2005.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    NV Las Vegas US 1 Y515 C B 2005 7049 019 05 NV NNSA NV Las Vegas US 1 Y550 C B 1999 7050 019 05 NV NNSA NV NTS Area 6 US 1 Y999 C B 1999 7051 019 20 OE DC Washington US 1 R110...

  12. natgas1980.xls

    Gasoline and Diesel Fuel Update (EIA)

    Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 51.6 39.7 88.5 125 56 96.2 34 497 0.22 383 137 Census Region and Division Northeast 10.9 6.5 18.8 144 50 86.6 31 771 0.27 463 168 New England 1.9 0.9 3.1 162 47 78.9 28 971 0.28 472 169 Middle Atlantic 9.0 5.6 15.7 141 51 88.1 32 739 0.27 461 168 Midwest 15.5 12.4 29.4 164 70 131.6 46 586 0.25 470 165

  13. oil1980.xls

    Gasoline and Diesel Fuel Update (EIA)

    5.4 11.6 29.7 131 51 99.0 36 1,053 0.41 795 287 Census Region and Division Northeast 9.2 6.0 18.2 176 59 116.2 42 1,419 0.47 934 335 New England 2.7 2.0 6.0 161 53 118.3 42 1,297 0.43 954 336 Middle Atlantic 6.5 4.1 12.2 184 61 115.3 42 1,478 0.49 926 335 Midwest 2.0 1.9 4.4 92 39 84.5 28 728 0.31 669 220 East North Central 1.5 1.4 3.3 92 39 84.4 28 731 0.31 673 220 West North Central 0.5 0.5 1.1 93 40 85.0 29 720 0.31 657 220 South 3.6 3.2 6.0 79 42 68.8 26 637 0.34 558 214 South Atlantic 3.5

  14. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    1 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone: 30-Year Average Under 2,000 CDD and -- More than 7,000 HDD

  15. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 15 Released: Dec 2006 Next CBECS will be conducted in 2007 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone:

  16. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    All Buildings RSEs for Total Floorspace RSEs for Total Workers in All Buildings RSEs for Mean Square Feet per Building RSEs for Mean Square Feet per Worker RSEs for Mean Hours per Week All Buildings*................................... 3.9 3.1 5.6 4.1 5.4 2.0 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 5.7 5.6 6.0 1.3 4.5 3.3 5,001 to 10,000 ................................. 5.8 5.6 8.8 0.9 8.0 4.1 10,001 to 25,000 ............................... 5.0 5.0

  17. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Released: Dec 2006 Next CBECS will be conducted in 2007 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone: 30-Year Average

  18. b10.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    One Floor Two Floors Three Floors Four to Nine Floors Ten or More Floors All Build- ings* One Floor Two Floors Three Floors Four to Nine Floors Ten or More Floors All Buildings* .................................. 4,645 3,136 1,031 339 128 12 64,783 25,981 16,270 7,501 10,085 4,947 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,014 411 115 Q N 6,789 5,192 1,217 343 Q N 5,001 to 10,000 ................................. 889 564 239 70 Q N 6,585 4,150

  19. b11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Table B11. Selected Principal Building Activity: Part 1, Number of Buildings for Non- Mall Buildings, 2003 Principal Building Activity Number of Buildings (thousand) Health Care All Buildings* Education Food Sales Food Service Lodging Retail (Other Than Mall) Energy Information Administration 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables Revised June 2006 81 Released: June 2006 Next CBECS will be conducted in 2007 Inpatient Outpatient All Buildings*

  20. b11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Lodging Retail (Other Than Mall) Table B11. Selected Principal Building Activity: Part 1, Number of Buildings for Non- Mall Buildings, 2003 Principal Building Activity Number of Buildings (thousand) Health Care All Buildings* Education Food Sales Food Service Energy Information Administration 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables Released: June 2006 Next CBECS will be conducted in 2007 Inpatient Outpatient All Buildings*

  1. b12.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 9,874 1,255 1,654 1,905 1,258 5,096 4,317 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 409 409 544 N 165 99 638 5,001 to 10,000 ................................. 6,585 399 356 442 N 280 160 725 10,001 to 25,000 ............................... 11,535 931 Q 345 Q 312 631 1,284 25,001 to 50,000 ............................... 8,668 1,756 Q Q Q Q 803 578 50,001 to 100,000 ............................. 9,057 2,690 Q Q Q 206 841 Q 100,001 to 200,000

  2. b13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 824 277 71 370 622 597 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 503 119 37 152 434 294 5,001 to 10,000 ................................. 889 127 67 Q 104 100 110 10,001 to 25,000 ............................... 738 116 69 Q 83 66 130 25,001 to 50,000 ............................... 241 43 9 Q 27 17 27 50,001 to 100,000 ............................. 129 17 7 Q Q Q 21 100,001 to 200,000 ........................... 65 11 6 Q Q Q 8 200,001 to

  3. b14.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 12,208 3,939 1,090 3,754 4,050 10,078 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 1,382 336 122 416 1,034 895 5,001 to 10,000 ................................. 6,585 938 518 Q 744 722 868 10,001 to 25,000 ............................... 11,535 1,887 1,077 Q 1,235 1,021 2,064 25,001 to 50,000 ............................... 8,668 1,506 301 Q 930 560 1,043 50,001 to 100,000 ............................. 9,057 1,209 474 Q Q Q 1,494 100,001 to

  4. b15.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 105 Released: Dec 2006 Next CBECS will be conducted in 2007 Fewer than 5 Workers 5 to 9 Workers 10 to 19 Workers 20 to 49 Workers 50 to 99 Workers 100 to 249 Workers 250 or More Workers All Buildings* .................................. 4,645 2,653 778 563 398 147 77 30 Table B15. Employment Size Category, Number of Buildings for Non-Mall Buildings, 2003 All Buildings* Number of Workers Number of Buildings (thousand) Number of Floors One

  5. b15.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Fewer than 5 Workers 5 to 9 Workers 10 to 19 Workers 20 to 49 Workers 50 to 99 Workers 100 to 249 Workers 250 or More Workers All Buildings* .................................. 4,645 2,653 778 563 398 147 77 30 Table B15. Employment Size Category, Number of Buildings for Non-Mall Buildings, 2003 All Buildings* Number of Workers Number of Buildings (thousand) Number of Floors One ................................................... 3,136 2,005 515 333 198 69 13 3 Two

  6. b16.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 15,492 6,166 7,803 10,989 7,934 6,871 9,528 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 4,659 1,264 689 155 Q Q N 5,001 to 10,000 ................................. 6,585 3,323 1,373 1,109 689 Q Q N 10,001 to 25,000 ............................... 11,535 4,006 2,075 2,456 2,113 692 Q N 25,001 to 50,000 ............................... 8,668 1,222 836 1,327 2,920 1,648 667 Q 50,001 to 100,000 ............................. 9,057 704 291 1,157

  7. b17.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 4,011 1,841 2,029 141 635 46 164 425 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,272 980 1,205 87 280 Q 77 183 5,001 to 10,000 ................................. 889 783 384 375 Q 106 Q Q 87 10,001 to 25,000 ............................... 738 625 320 293 Q 113 Q 40 64 25,001 to 50,000 ............................... 241 185 91 86 Q 56 Q 16 36 50,001 to 100,000 ............................. 129 82 35 40 Q 47 Q 9 37 100,001 to 200,000

  8. b18.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 49,421 23,591 23,914 1,916 15,363 1,956 3,808 9,599 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 6,043 2,682 3,162 199 746 Q 206 498 5,001 to 10,000 ................................. 6,585 5,827 2,858 2,791 Q 758 Q Q 620 10,001 to 25,000 ............................... 11,535 9,738 5,028 4,530 Q 1,797 Q 604 1,044 25,001 to 50,000 ............................... 8,668 6,659 3,197 3,141 Q 2,009 Q 531 1,327 50,001 to 100,000

  9. b19.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 3,754 643 55 23 14 157 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,131 311 Q Q N 100 5,001 to 10,000 ................................. 889 720 136 Q N Q Q 10,001 to 25,000 ............................... 738 590 104 22 Q Q Q 25,001 to 50,000 ............................... 241 163 50 11 Q Q Q 50,001 to 100,000 ............................. 129 87 25 4 5 Q Q 100,001 to 200,000 ........................... 65 43 11 4 Q Q Q 200,001 to 500,000

  10. b2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total Workers in All Buildings (thousand) Median Square Feet per Building (thousand) Median Square Feet per Worker Median Hours per Week Median Age of Buildings (years) All Buildings* .................................. 4,645 64,783 72,807 4.6 1,000 50 30.5 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 6,789 9,936 2.4 750 48 30.5 5,001 to 10,000 ................................. 889 6,585 7,512 7.2 1,300 50 30.5 10,001 to 25,000

  11. b20.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 45,144 10,960 1,958 1,951 2,609 2,161 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,613 916 Q Q N 223 5,001 to 10,000 ................................. 6,585 5,304 1,031 Q N Q Q 10,001 to 25,000 ............................... 11,535 9,098 1,732 383 Q Q Q 25,001 to 50,000 ............................... 8,668 5,807 1,837 355 Q Q Q 50,001 to 100,000 ............................. 9,057 6,218 1,739 273 337 Q Q 100,001 to 200,000

  12. b21.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Buildings With Central Physical Plant All Buildings With Central Physical Plant All Buildings* .................................. 4,645 1,477 116 64,783 24,735 6,604 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 771 Q 6,789 2,009 Q 5,001 to 10,000 ................................. 889 259 Q 6,585 1,912 Q 10,001 to 25,000 ............................... 738 263 33 11,535 4,158 520 25,001 to 50,000 ............................... 241 92 18 8,668 3,277

  13. b22.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 144 Released: Dec 2006 Next CBECS will be conducted in 2007 Elec- tricity Natural Gas Fuel Oil District Heat District Chilled Water Propane Other a All Buildings* .................................. 4,645 4,414 4,404 2,391 451 67 33 502 132 Table B22. Energy Sources, Number of Buildings for Non-Mall Buildings, 2003 Number of Buildings (thousand) Energy Sources Used (more than one may apply) All Buildings* Buildings Using Any Energy Source Number of Workers (main shift) Fewer

  14. b23.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 63,343 63,307 43,468 15,157 5,443 2,853 7,076 1,401 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 6,362 6,346 3,084 600 Q Q 806 199 5,001 to 10,000 ................................. 6,585 6,212 6,197 3,692 716 Q Q 725 Q 10,001 to 25,000 ............................... 11,535 11,370 11,370 7,053 966 289 Q 1,014 Q 25,001 to 50,000 ............................... 8,668 8,385 8,385 6,025 825 369 240 638 Q 50,001 to 100,000

  15. b24.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Water Heating Cooking Manu- facturing All Buildings* .................................. 4,645 3,982 3,625 3,472 801 119 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,100 1,841 1,715 354 Q 5,001 to 10,000 ................................. 889 782 732 725 155 29 10,001 to 25,000 ............................... 738 659 629 607 127 28 25,001 to 50,000 ............................... 241 225 216 217 69 Q 50,001 to 100,000 .............................

  16. b25.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Space Heating Cooling Water Heating Cooking Manu- facturing All Buildings* .................................. 64,783 60,028 56,940 56,478 22,237 3,138 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,668 5,007 4,759 997 Q 5,001 to 10,000 ................................. 6,585 5,786 5,408 5,348 1,136 214 10,001 to 25,000 ............................... 11,535 10,387 9,922 9,562 1,954 472 25,001 to 50,000 ............................... 8,668 8,060

  17. b27.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 60,028 28,600 36,959 5,988 5,198 3,204 842 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,668 2,367 2,829 557 Q 665 183 5,001 to 10,000 ................................. 6,585 5,786 2,560 3,358 626 Q 529 Q 10,001 to 25,000 ............................... 11,535 10,387 4,872 6,407 730 289 597 Q 25,001 to 50,000 ............................... 8,668 8,060 4,040 5,394 436 325 392 Q 50,001 to 100,000 ............................. 9,057 8,718 4,243

  18. b3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 31 Released: Dec 2006 Next CBECS will be conducted in 2007 All Buildings* North- east Mid- west South West All Buildings* North- east Mid- west South West All Buildings* .................................. 4,645 726 1,266 1,775 878 64,783 12,905 17,080 23,489 11,310 Table B3. Census Region, Number of Buildings and Floorspace for Non-Mall Buildings, 2003 Number of Buildings (thousand) Total Floorspace (million square feet) Elevators and Escalators (more than one may apply) Any

  19. b3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Released: Dec 2006 Next CBECS will be conducted in 2007 All Buildings* North- east Mid- west South West All Buildings* North- east Mid- west South West All Buildings* .................................. 4,645 726 1,266 1,775 878 64,783 12,905 17,080 23,489 11,310 Table B3. Census Region, Number of Buildings and Floorspace for Non-Mall Buildings, 2003 Number of Buildings (thousand) Total Floorspace (million square feet) Elevators and Escalators (more than one may apply) Any Elevators

  20. b30.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    District Chilled Water Elec- tricity Natural Gas District Chilled Water All Buildings* .................................. 4,645 3,625 3,589 17 33 64,783 56,940 54,321 1,018 2,853 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 1,841 1,838 Q Q 6,789 5,007 4,994 Q Q 5,001 to 10,000 ................................. 889 732 727 Q Q 6,585 5,408 5,367 Q Q 10,001 to 25,000 ............................... 738 629 618 Q Q 11,535 9,922 9,743 Q Q 25,001 to

  1. b31.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 3,472 1,910 1,445 94 27 128 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 1,715 1,020 617 41 N 66 5,001 to 10,000 ................................. 889 725 386 307 Q Q 27 10,001 to 25,000 ............................... 738 607 301 285 16 Q 27 25,001 to 50,000 ............................... 241 217 110 114 Q Q Q 50,001 to 100,000 ............................. 129 119 53 70 Q 5 Q 100,001 to 200,000 ........................... 65 60 27 35 Q 5 Q

  2. b34.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 178 Released: Dec 2006 Next CBECS will be conducted in 2007 All Build- ings* Not Heated 1 to 50 Percent Heated 51 to 99 Percent Heated 100 Percent Heated All Build- ings* Not Heated 1 to 50 Percent Heated 51 to 99 Percent Heated 100 Percent Heated All Buildings* .................................. 4,645 663 523 498 2,962 64,783 4,756 6,850 8,107 45,071 Table B34. Percent of Floorspace Heated, Number of Buildings and Floorspace for Non- Mall Buildings, 2003 Number of Buildings

  3. b35.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Cooled 1 to 50 Percent Cooled 51 to 99 Percent Cooled 100 Percent Cooled All Build- ings* Not Cooled 1 to 50 Percent Cooled 51 to 99 Percent Cooled 100 Percent Cooled All Buildings* .................................. 4,645 1,020 985 629 2,011 64,783 7,843 16,598 13,211 27,132 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 710 407 279 1,155 6,789 1,782 1,206 781 3,021 5,001 to 10,000 ................................. 889 157 226 133 374 6,585 1,177

  4. b38.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 194 Released: Dec 2006 Next CBECS will be conducted in 2007 Heat Pumps Furnaces Individual Space Heaters District Heat Boilers Packaged Heating Units Other All Buildings* .................................. 4,645 3,982 476 1,864 819 65 579 953 205 Table B38. Heating Equipment, Number of Buildings for Non-Mall Buildings, 2003 Heating Equipment (more than one may apply) Number of Buildings (thousand) All Buildings* Heated Buildings Number of Floors One

  5. b38.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Released: October 2006 Next CBECS will be conducted in 2007 Heat Pumps Furnaces Individual Space Heaters District Heat Boilers Packaged Heating Units Other All Buildings* .................................. 4,645 3,982 476 1,864 819 65 579 953 205 Table B38. Heating Equipment, Number of Buildings for Non-Mall Buildings, 2003 Heating Equipment (more than one may apply) Number of Buildings (thousand) All Buildings* Heated Buildings Number of Floors One

  6. b39.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 60,028 8,814 19,615 12,545 5,166 20,423 18,021 3,262 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,668 685 2,902 1,047 Q 461 1,159 330 5,001 to 10,000 ................................. 6,585 5,786 462 2,891 1,282 Q 773 1,599 Q 10,001 to 25,000 ............................... 11,535 10,387 1,400 4,653 2,129 289 2,164 2,765 456 25,001 to 50,000 ............................... 8,668 8,060 1,150 2,761 1,748 325 2,829 2,449 419 50,001 to 100,000

  7. b4.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    East South Central West South Central Mountain Pacific All Buildings* .................................. 4,645 233 493 696 571 874 348 553 299 580 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 127 237 369 356 457 215 294 165 333 5,001 to 10,000 ................................. 889 48 101 117 97 189 56 116 56 110 10,001 to 25,000 ............................... 738 37 90 122 75 139 51 88 54 81 25,001 to 50,000 ............................... 241 10 26

  8. b41.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 56,940 11,035 9,041 12,558 2,853 11,636 29,969 1,561 1,232 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,007 1,568 675 972 Q Q 1,957 179 Q 5,001 to 10,000 ................................. 6,585 5,408 1,523 563 1,012 Q Q 2,741 207 Q 10,001 to 25,000 ............................... 11,535 9,922 2,173 1,441 1,740 Q 456 5,260 378 Q 25,001 to 50,000 ............................... 8,668 7,776 1,683 1,155 2,301 240 729 4,264 Q Q 50,001 to 100,000

  9. b42.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ized System Distrib- uted System Combin- ation Central- ized and Distrib- uted Systems Central- ized System Distrib- uted System Combin- ation Central- ized and Distrib- uted Systems All Buildings* .................................. 4,645 3,472 2,513 785 175 64,783 56,478 34,671 11,540 10,267 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 1,715 1,267 418 Q 6,789 4,759 3,452 1,206 Q 5,001 to 10,000 ................................. 889 725 557 150 Q

  10. b43.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 4,248 2,184 3,943 941 455 565 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,261 1,070 2,068 382 101 205 5,001 to 10,000 ................................. 889 821 416 772 148 88 107 10,001 to 25,000 ............................... 738 716 412 665 189 105 123 25,001 to 50,000 ............................... 241 231 145 223 102 60 55 50,001 to 100,000 ............................. 129 126 75 123 60 51 37 100,001 to 200,000

  11. b44.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 62,060 38,528 59,688 27,571 20,643 17,703 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 6,038 2,918 5,579 1,123 312 604 5,001 to 10,000 ................................. 6,585 6,090 3,061 5,726 1,109 686 781 10,001 to 25,000 ............................... 11,535 11,229 6,424 10,458 2,944 1,721 1,973 25,001 to 50,000 ............................... 8,668 8,297 5,176 8,001 3,662 2,191 2,013 50,001 to 100,000 ............................. 9,057

  12. b45.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 3,176 1,007 666 308 696 2,370 996 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 1,591 486 332 142 353 1,159 268 5,001 to 10,000 ................................. 889 642 188 124 65 117 494 181 10,001 to 25,000 ............................... 738 548 138 75 40 103 427 250 25,001 to 50,000 ............................... 241 196 78 44 19 53 148 134 50,001 to 100,000 ............................. 129 114 60 44 19 34 81 89 100,001 to 200,000

  13. b46.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 52,974 26,768 20,254 10,425 17,218 38,884 35,335 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 4,333 1,310 916 366 935 3,174 830 5,001 to 10,000 ................................. 6,585 4,738 1,406 909 497 894 3,609 1,407 10,001 to 25,000 ............................... 11,535 8,646 2,230 1,188 614 1,665 6,725 4,072 25,001 to 50,000 ............................... 8,668 7,068 2,829 1,626 676 1,933 5,289 4,910 50,001 to 100,000

  14. b5.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    West South Central Mountain Pacific All Buildings* .................................. 64,783 2,964 9,941 11,595 5,485 12,258 3,393 7,837 3,675 7,635 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 360 666 974 922 1,207 538 788 464 871 5,001 to 10,000 ................................. 6,585 359 764 843 722 1,387 393 879 418 820 10,001 to 25,000 ............................... 11,535 553 1,419 1,934 1,164 2,240 810 1,329 831 1,256 25,001 to 50,000

  15. b6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 49 Released: June 2006 Next CBECS will be conducted in 2007 1,001 to 5,000 Square Feet 5,001 to 10,000 Square Feet 10,000 to 25,000 Square Feet 25,001 to 50,000 Square Feet 50,001 to 100,000 Square Feet 100,001 to 200,000 Square Feet 200,001 to 500,000 Square Feet Over 500,000 Square Feet All Buildings* .................................. 4,645 2,552 889 738 241 129 65 25 7 Table B6. Building Size, Number of Buildings for Non-Mall Buildings, 2003 Number of Buildings (thousand)

  16. b6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Released: June 2006 Next CBECS will be conducted in 2007 1,001 to 5,000 Square Feet 5,001 to 10,000 Square Feet 10,000 to 25,000 Square Feet 25,001 to 50,000 Square Feet 50,001 to 100,000 Square Feet 100,001 to 200,000 Square Feet 200,001 to 500,000 Square Feet Over 500,000 Square Feet All Buildings* .................................. 4,645 2,552 889 738 241 129 65 25 7 Table B6. Building Size, Number of Buildings for Non-Mall Buildings, 2003 Number of Buildings (thousand) All Buildings*

  17. b7.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Square Feet 50,001 to 100,000 Square Feet 100,001 to 200,000 Square Feet 200,001 to 500,000 Square Feet Over 500,000 Square Feet All Buildings* .................................. 64,783 6,789 6,585 11,535 8,668 9,057 9,064 7,176 5,908 Principal Building Activity Education .......................................... 9,874 409 399 931 1,756 2,690 2,167 1,420 Q Food Sales ....................................... 1,255 409 356 Q Q Q Q N N Food Service ..................................... 1,654 544

  18. b8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 330 527 562 579 731 707 876 334 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 174 315 331 298 350 438 481 165 5,001 to 10,000 ................................. 889 71 107 90 120 180 98 158 66 10,001 to 25,000 ............................... 738 55 64 90 95 122 103 151 58 25,001 to 50,000 ............................... 241 19 23 26 33 48 32 39 21 50,001 to 100,000 ............................. 129 7 9 14 22 16 20 28 13 100,001 to 200,000

  19. b9.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 3,769 6,871 7,045 8,101 10,772 10,332 12,360 5,533 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 490 796 860 690 966 1,149 1,324 515 5,001 to 10,000 ................................. 6,585 502 827 643 865 1,332 721 1,209 486 10,001 to 25,000 ............................... 11,535 804 988 1,421 1,460 1,869 1,647 2,388 958 25,001 to 50,000 ............................... 8,668 677 838 935 1,234 1,720 1,174 1,352 739 50,001 to 100,000

  20. eia-857.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    This report is mandatory under the Federal Energy Administration Act of 1974 (Public Law 93-275). Failure to comply may result in criminal fines, civil penalties and other ...

  1. a1.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    2003 Commercial Buildings Energy Consumption Survey Detailed Tables October 2006 Energy Information Administration 2003 Commercial Buildings Energy Consumption Survey Detailed...

  2. c1.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 67 5,443 1,017 1,011 335 47 1 634 District Chilled Water ... 33 2,853 538 580 192 35 2 309 Propane ......

  3. a1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Principal Building Activity Education ...... 7.1 6.0 ... Principal Building Activity Education ...... 7.1 15.1 ...

  4. EWA Summary.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Guidance Software: Behshad Behnam ph: 703-657-7208 behshad.behnam@guidancesoftware.com DOE PM: Robert Ciochon ph: 202-586-2586 Robert.ciochon@hq.doe.gov IntelMcAfee Anti-virus and ...

  5. web_comments.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    horizon) vs. long-term (planning time horizon) impacts of ... Networks Inc. 192004 Emergency plans Define "emergency" ... It would also minimize much of the inherent problems of the ...

  6. recommendations.xls

    Office of Environmental Management (EM)

    ... believe operations planning analysis is imperative as well. ... that needs to be under both normal and emergency conditions. ... -- so that may cause some problems when people are trying to ...

  7. Fig1.xls

    Gasoline and Diesel Fuel Update (EIA)

    Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 432,713 396,681 438,926 423,131 435,592 426,888 434,325 439,712 428,689 440,668 425,849 441,756 1998 443,757 398,519 448,486 438,144 457,815 435,237 439,093 443,144 336,241 421,315 414,058 434,518 1999 436,171 395,293 435,012 424,724 432,489 414,495 431,981 424,513 408,237 421,312 409,660 419,049 2000 411,264 385,685 418,062 398,966 413,434 405,362 422,701 423,114 411,610 428,272 415,005 434,219 2001 434,184 398,663

  8. Fig1.xls

    Gasoline and Diesel Fuel Update (EIA)

    June 2010 1 June 2010 Short-Term Energy Outlook June 8, 2010 Release Highlights  Crude oil prices fluctuated considerably last month, with the West Texas Intermediate (WTI) spot price ranging from a high of $86 per barrel on May 3 to a low of $65 on May 25, before ending the month at $74. According to some market analysts, uncertainty over the global economic recovery, particularly with respect to Europe's debt crisis and the tightening of credit by China, and liquidation of futures contracts

  9. Summer Tables.xls

    Gasoline and Diesel Fuel Update (EIA)

    8 1 September 2008 Short-Term Energy Outlook September 9, 2008 Release Highlights The monthly average price of West Texas Intermediate (WTI) crude oil decreased from over $133 per barrel in June and July to about $117 per barrel in August, reflecting expectations of a slowdown in world petroleum demand growth. WTI, which averaged $72 per barrel in 2007, is projected to average $116 per barrel in 2008. Projected stronger growth in world petroleum demand is expected to increase the annual average

  10. crib.xls

    Buildings Energy Data Book [EERE]

    August 2003 D I S C L A I M E R This document was designed for the internal use of the United States Department of Energy. This document will be occasionally updated and, therefore, this copy may not reflect the most current version. This document was prepared as account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or

  11. b33.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Propane Elec- tricity Natural Gas Propane All Buildings* ...... 4,645 801 410 457 108 64,783 22,237 13,161 15,438 1,460 Building Floorspace (Square ...

  12. section-a.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    . THIS CONTRACT IS A RATED ORDER RATING PAGE OF PAGES UNDER DPAS (15 CFR 700) > 1 1 [ ] SEALED BID (IFB) [X] NEGOTIATED (RFP) 7. ISSUED BY CODE 8. ADDRESS OFFER TO (If other than Item 7) U.S. Department of Energy Office of River Protection Same as Block 7 Office of Business Management and Administration, H6-60 ATTN: Michael K. Barrett, Contracting Officer 2440 Stevens Drive (or P. O. Box 450) Richland, WA 99352 NOTE: In sealed bid solicitations "offer" and "offeror" mean

  13. c25.xls

    Gasoline and Diesel Fuel Update (EIA)

    65 133 100 80 1,421 2,263 2,649 1,890 45.6 58.6 37.8 42.5 Energy-Related Space Functions (more than one may apply) Commercial Food Preparation ... 207 323...

  14. c37.xls

    Gasoline and Diesel Fuel Update (EIA)

    Distributed System ... 13,682 115.22 Q 145.6 1.23 10.64 Energy-Related Space Functions (more than one may apply) Commercial Food Preparation ... Q 113.68...

  15. c10.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    254 132 Q 1,073 1,766 1,966 1,573 1,282 Q 153.8 129.4 83.9 Q Principal Building Activity Education ... 141 238 131 186 123 1,537 2,800 1,403...

  16. c5.xls

    Gasoline and Diesel Fuel Update (EIA)

    Q 184 246 140 1,556 1,203 1,928 1,221 Q 153.2 127.8 115.0 Principal Building Activity Education ... 171 219 301 129 1,683 2,541 3,983 1,667...

  17. All Beams 2013.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    LaBonte About Us Alison LaBonte - Marine and Hydrokinetic Technology Manager Most Recent Wave Energy Prize Teams Make a Splash During Waterpower Week May 24 River Turbine Provides Clean Energy to Remote Alaskan Village August 18 Wave Energy Prize Narrowed from 92 Teams to Top 20 August 14

    Markovitz About Us Alison Markovitz - Director, National Laboratory Operations Board Alison Markovitz, Director of the National Laboratory Operations Board Alison Markovitz serves as Director of the National

  18. c6.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    1.44 1.60 1.84 Window and Interior Lighting Features (more than one may apply) Multipaned Windows ... 15,717 16,103 18,428 9,108 16.55 12.85 15.39 17.21 1.69...

  19. b32.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 56,478 27,490 28,820 1,880 3,088 1,422 Building Floorspace (Square Feet) 1,001 to ......... 1,654 1,654 482 1,127 Q Q Q Health Care ......

  20. J319.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    MISO Project Number J319 Point of Interconnection Entergy AR ANO-Pleasant Hill 500 kV line ... Page 1 of 3 February 10, 2014 MISO Project Number J319 Point of Interconnection Holland ...

  1. a4.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Both can be accessed from the CBECS web site http:www.eia.doe.govemeucbecs. QData withheld because the Relative Standard Error (RSE) was greater than 50 percent, ...

  2. RangeTables.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    (MeVcm²/mg) LET vs. Range in Si for 25 MeV SEE Beams (low LET) 4 He 14 N 0 0.5 1 1.5 0 600 1200 1800 2400 3000 3600 4 He 14 N 22 Ne 0 1 2 3 4 5 6 7 8 9 10 0 100 200 300 400 500 600 700 800 900 1000 1100 LET (MeVcm²/mg) Range in Silicon (µm) LET vs. Range in Si for 25 MeV SEE Beams (low LET) After aramica window and 30 mm of air 4 He 14 N 0 0.5 1 1.5 0 600 1200 1800 2400 3000 3600 Range in Silicon (µm) 129 Xe 30 40 50 60 (MeVcm²/mg) LET vs. Range in Si for 25 MeV SEE Beams After aramica

  3. Table1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Industrial 1605 0 Yes No Prince George Electric Cooperative Electric Providers 1605 1 No ... Providers 1605 9 No No Rangely Weber Sand Unit Industrial 1605 1 No No Rappahannock ...

  4. Table 4.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Sequestration 4,196 4,196 Prince George Electric Cooperative Electric Providers ... Direct 3 Indirect 241,557 Rangely Weber Sand Unit Industrial Indirect 1,014,955 ...

  5. Table 2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Public Utility District No. 1 of Snohomish County Rangely Weber Sand Unit Rappahannock Electric Cooperative Polar Technology, LLC Portland General Electric Co. Prince George ...

  6. c26.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Btu) per Square Foot (thousand Btu) per Worker (million Btu) per Building (thousand dollars) per Square Foot (dollars) per Thousand Pounds (dollars) All Buildings...

  7. a8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ......... 6,922 5,613 1,028 Q Q N 223 5,001 to 10,000 ...... 7,033 5,304 1,383 Q N Q Q 10,001 to 25,000 ......

  8. wf01.xls

    Gasoline and Diesel Fuel Update (EIA)

    Warm Base Cold Natural Gas Northeast Consumption (mcf**) 81.7 87.3 67.7 87.4 79.9 80.8 ... 17.5 31.6 47.6 Natural Gas (Midwest) Consumption (mcf) 88.3 99.1 78.2 92.3 85.7 88.7 ...

  9. June2010.XLS

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    7-2008 2009 2010 2011 CHIEF FINANCIAL OFFICER Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1. Federal Loan Guarantee for Mississippi Integrated Gasification Combined Cycle, Moss Point, MS (DOE/EIS-0428) 2. Federal Loan Guarantee for Indiana Integrated Gasification Combined Cycle, Rockport, IN (DOE/EIS-0429) 3. Federal Loan Guarantee to Support Construction of the Taylorville Energy Center,

  10. a2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    North east Mid- west South West All Buildings North- east Mid- west South West All Buildings ...... 4,859 761 1,305 1,873 920 71,658 13,995 18,103 ...

  11. a1.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table. Both can be accessed from the CBECS web site http:www.eia.doe.govemeucbecs. ...

  12. a1.xls

    Gasoline and Diesel Fuel Update (EIA)

    Both can be accessed from the CBECS web site http:www.eia.doe.govemeucbecs. Note: Due ... Both can be accessed from the CBECS web site http:www.eia.doe.govemeucbecs. QData ...

  13. table6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    .4 9.9 10.2 10.6 11.4 12.0 Household Characteristics Census Region and Division Northeast... 9.5 NA 10.3 10.9 11.3 11.9...

  14. Algorithms and architectures for high performance analysis of semantic graphs.

    SciTech Connect (OSTI)

    Hendrickson, Bruce Alan

    2005-09-01

    Semantic graphs offer one promising avenue for intelligence analysis in homeland security. They provide a mechanism for describing a wide variety of relationships between entities of potential interest. The vertices are nouns of various types, e.g. people, organizations, events, etc. Edges in the graph represent different types of relationships between entities, e.g. 'is friends with', 'belongs-to', etc. Semantic graphs offer a number of potential advantages as a knowledge representation system. They allow information of different kinds, and collected in differing ways, to be combined in a seamless manner. A semantic graph is a very compressed representation of some of relationship information. It has been reported that the semantic graph can be two orders of magnitude smaller than the processed intelligence data. This allows for much larger portions of the data universe to be resident in computer memory. Many intelligence queries that are relevant to the terrorist threat are naturally expressed in the language of semantic graphs. One example is the search for 'interesting' relationships between two individuals or between an individual and an event, which can be phrased as a search for short paths in the graph. Another example is the search for an analyst-specified threat pattern, which can be cast as an instance of subgraph isomorphism. It is important to note than many kinds of analysis are not relationship based, so these are not good candidates for semantic graphs. Thus, a semantic graph should always be used in conjunction with traditional knowledge representation and interface methods. Operations that involve looking for chains of relationships (e.g. friend of a friend) are not efficiently executable in a traditional relational database. However, the semantic graph can be thought of as a pre-join of the database, and it is ideally suited for these kinds of operations. Researchers at Sandia National Laboratories are working to facilitate semantic graph

  15. Mining Large Heterogeneous Graphs using Cray s Urika

    SciTech Connect (OSTI)

    Sukumar, Sreenivas R; Bond, Nathaniel A

    2013-01-01

    Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

  16. Fast Search for Dynamic Multi-Relational Graphs

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Feo, John T.

    2013-06-23

    Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.

  17. On the mixing time of geographical threshold graphs

    SciTech Connect (OSTI)

    Bradonjic, Milan

    2009-01-01

    In this paper, we study the mixing time of random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). We specifically study the mixing times of random walks on 2-dimensional GTGs near the connectivity threshold. We provide a set of criteria on the distribution of vertex weights that guarantees that the mixing time is {Theta}(n log n).

  18. Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

    Energy Science and Technology Software Center (OSTI)

    2012-05-31

    The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

  19. In-Memory Graph Databases for Web-Scale Data

    SciTech Connect (OSTI)

    Castellana, Vito G.; Morari, Alessandro; Weaver, Jesse R.; Tumeo, Antonino; Haglin, David J.; Villa, Oreste; Feo, John

    2015-03-01

    RDF databases have emerged as one of the most relevant way for organizing, integrating, and managing expo- nentially growing, often heterogeneous, and not rigidly structured data for a variety of scientific and commercial fields. In this paper we discuss the solutions integrated in GEMS (Graph database Engine for Multithreaded Systems), a software framework for implementing RDF databases on commodity, distributed-memory high-performance clusters. Unlike the majority of current RDF databases, GEMS has been designed from the ground up to primarily employ graph-based methods. This is reflected in all the layers of its stack. The GEMS framework is composed of: a SPARQL-to-C++ compiler, a library of data structures and related methods to access and modify them, and a custom runtime providing lightweight software multithreading, network messages aggregation and a partitioned global address space. We provide an overview of the framework, detailing its component and how they have been closely designed and customized to address issues of graph methods applied to large-scale datasets on clusters. We discuss in details the principles that enable automatic translation of the queries (expressed in SPARQL, the query language of choice for RDF databases) to graph methods, and identify differences with respect to other RDF databases.

  20. STRUCTURAL ANNOTATION OF EM IMAGES BY GRAPH CUT

    SciTech Connect (OSTI)

    Chang, Hang; Auer, Manfred; Parvin, Bahram

    2009-05-08

    Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e.,"topological" invariance and computational efficiency. The technique is extended to the multi-phase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).

  1. EIA - Electricity Generating Capacity

    U.S. Energy Information Administration (EIA) Indexed Site

    Electricity Generating Capacity Release Date: January 3, 2013 | Next Release: August 2013 Year Existing Units by Energy Source Unit Additions Unit Retirements 2011 XLS XLS XLS 2010 XLS XLS XLS 2009 XLS XLS XLS 2008 XLS XLS XLS 2007 XLS XLS XLS 2006 XLS XLS XLS 2005 XLS XLS XLS 2004 XLS XLS XLS 2003 XLS XLS XLS Source: Form EIA-860, "Annual Electric Generator Report." Related links Electric Power Monthly Electric Power Annual Form EIA-860 Source Data

  2. Cyber Graph Queries for Geographically Distributed Data Centers

    SciTech Connect (OSTI)

    Berry, Jonathan W.; Collins, Michael; Kearns, Aaron; Phillips, Cynthia A.; Saia, Jared

    2015-05-01

    We present new algorithms for a distributed model for graph computations motivated by limited information sharing we first discussed in [20]. Two or more independent entities have collected large social graphs. They wish to compute the result of running graph algorithms on the entire set of relationships. Because the information is sensitive or economically valuable, they do not wish to simply combine the information in a single location. We consider two models for computing the solution to graph algorithms in this setting: 1) limited-sharing: the two entities can share only a polylogarithmic size subgraph; 2) low-trust: the entities must not reveal any information beyond the query answer, assuming they are all honest but curious. We believe this model captures realistic constraints on cooperating autonomous data centers. We have algorithms in both setting for s - t connectivity in both models. We also give an algorithm in the low-communication model for finding a planted clique. This is an anomaly- detection problem, finding a subgraph that is larger and denser than expected. For both the low- communication algorithms, we exploit structural properties of social networks to prove perfor- mance bounds better than what is possible for general graphs. For s - t connectivity, we use known properties. For planted clique, we propose a new property: bounded number of triangles per node. This property is based upon evidence from the social science literature. We found that classic examples of social networks do not have the bounded-triangles property. This is because many social networks contain elements that are non-human, such as accounts for a business, or other automated accounts. We describe some initial attempts to distinguish human nodes from automated nodes in social networks based only on topological properties.

  3. LDRD final report : first application of geospatial semantic graphs to SAR image data.

    SciTech Connect (OSTI)

    Brost, Randolph C.; McLendon, William Clarence,

    2013-01-01

    Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.

  4. A Graph Analytic Metric for Mitigating Advanced Persistent Threat

    SciTech Connect (OSTI)

    Johnson, John R.; Hogan, Emilie A.

    2013-06-04

    This paper introduces a novel graph analytic metric that can be used to measure the potential vulnerability of a cyber network to specific types of attacks that use lateral movement and privilege escalation such as the well known Pass The Hash, (PTH). The metric is computed from an oriented subgraph of the underlying cyber network induced by selecting only those edges for which a given property holds between the two vertices of the edge. The metric with respect to a select node on the subgraph is defined as the likelihood that the select node is reachable from another arbitrary node in the graph. This metric can be calculated dynamically from the authorization and auditing layers during the network security authorization phase and will potentially enable predictive deterrence against attacks such as PTH.

  5. Codesign Lessons Learned from Implementing Graph Matching on Multithreaded Architectures

    SciTech Connect (OSTI)

    Halappanavar, Mahantesh; Pothen, Alex; Azad, Md Ariful; Manne, Fredrik; Langguth, Johannes; Khan, Arif

    2015-08-12

    Co-design of algorithms and architectures is an effective way to address the performance of irregular applications on multithreaded architectures. We explore the interplay between algorithm design and architectural features using graph matching as a case study. We present the key lessons that we have learnt as a means to influence co-design of algorithms and architecture for execution of data-intensive irregular workloads.

  6. Encoding and analyzing aerial imagery using geospatial semantic graphs

    SciTech Connect (OSTI)

    Watson, Jean-Paul; Strip, David R.; McLendon, William C.; Parekh, Ojas D.; Diegert, Carl F.; Martin, Shawn Bryan; Rintoul, Mark Daniel

    2014-02-01

    While collection capabilities have yielded an ever-increasing volume of aerial imagery, analytic techniques for identifying patterns in and extracting relevant information from this data have seriously lagged. The vast majority of imagery is never examined, due to a combination of the limited bandwidth of human analysts and limitations of existing analysis tools. In this report, we describe an alternative, novel approach to both encoding and analyzing aerial imagery, using the concept of a geospatial semantic graph. The advantages of our approach are twofold. First, intuitive templates can be easily specified in terms of the domain language in which an analyst converses. These templates can be used to automatically and efficiently search large graph databases, for specific patterns of interest. Second, unsupervised machine learning techniques can be applied to automatically identify patterns in the graph databases, exposing recurring motifs in imagery. We illustrate our approach using real-world data for Anne Arundel County, Maryland, and compare the performance of our approach to that of an expert human analyst.

  7. Proximity graphs based multi-scale image segmentation

    SciTech Connect (OSTI)

    Skurikhin, Alexei N

    2008-01-01

    We present a novel multi-scale image segmentation approach based on irregular triangular and polygonal tessellations produced by proximity graphs. Our approach consists of two separate stages: polygonal seeds generation followed by an iterative bottom-up polygon agglomeration into larger chunks. We employ constrained Delaunay triangulation combined with the principles known from the visual perception to extract an initial ,irregular polygonal tessellation of the image. These initial polygons are built upon a triangular mesh composed of irregular sized triangles and their shapes are ad'apted to the image content. We then represent the image as a graph with vertices corresponding to the polygons and edges reflecting polygon relations. The segmentation problem is then formulated as Minimum Spanning Tree extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal grids by an iterative graph contraction constructing Minimum Spanning Tree. The contraction uses local information and merges the polygons bottom-up based on local region-and edge-based characteristics.

  8. New Developments in MadGraph/MadEvent

    SciTech Connect (OSTI)

    Alwall, Johan; Artoisenet, Pierre; de Visscher, Simon; Duhr, Claude; Frederix, Rikkert; Herquet, Michel; Mattelaer, Olivier; /IBA, Louvain-la-Neuve

    2011-11-08

    We here present some recent developments of MadGraph/MadEvent since the latest published version, 4.0. These developments include: Jet matching with Pythia parton showers for both Standard Model and Beyond the Standard Model processes, decay chain functionality, decay width calculation and decay simulation, process generation for the Grid, a package for calculation of quarkonium amplitudes, calculation of Matrix Element weights for experimental events, automatic dipole subtraction for next-to-leading order calculations, and an interface to FeynRules, a package for automatic calculation of Feynman rules and model files from the Lagrangian of any New Physics model.

  9. Constructing compact and effective graphs for recommender systems via node and edge aggregations

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lee, Sangkeun; Kahng, Minsuk; Lee, Sang-goo

    2014-12-10

    Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we proposemore » node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.« less

  10. Discrete Mathematical Approaches to Graph-Based Traffic Analysis

    SciTech Connect (OSTI)

    Joslyn, Cliff A.; Cowley, Wendy E.; Hogan, Emilie A.; Olsen, Bryan K.

    2014-04-01

    Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In this paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.

  11. INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

    SciTech Connect (OSTI)

    Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P

    2012-10-01

    It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.

  12. A Space-Filling Visualization Technique for Multivariate Small World Graphs

    SciTech Connect (OSTI)

    Wong, Pak C.; Foote, Harlan P.; Mackey, Patrick S.; Chin, George; Huang, Zhenyu; Thomas, James J.

    2012-03-15

    We introduce an information visualization technique, known as GreenCurve, for large sparse graphs that exhibit small world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a sophisticated algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. We show in the paper that the GreenCurve technology is (1) theoretically sustainable by introducing an error estimation metric to measure the fidelity of the new graph representation, (2) empirically rigorous by conducting a usability study to investigate its strengths and weaknesses against the traditional graph layout, and (3) pragmatically feasible by applying it to analyze stressed conditions of the large scale electric power grid on the west coast.

  13. Multi-Level Anomaly Detection on Time-Varying Graph Data

    SciTech Connect (OSTI)

    Bridges, Robert A; Collins, John P; Ferragut, Erik M; Laska, Jason A; Sullivan, Blair D

    2015-01-01

    This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

  14. Have Green – A Visual Analytics Framework for Large Semantic Graphs

    SciTech Connect (OSTI)

    Wong, Pak C.; Chin, George; Foote, Harlan P.; Mackey, Patrick S.; Thomas, James J.

    2006-10-29

    A semantic graph is a network of heterogeneous nodes and links annotated with a domain ontology. In intelligence analysis, investigators use semantic graphs to organize concepts and relationships as graph nodes and links in hopes of discovering key trends, patterns, and insights. However, as new information continues to arrive from a multitude of sources, the size and complexity of the semantic graphs will soon overwhelm an investigator's cognitive capacity to carry out significant analyses. We introduce a powerful visual analytics framework designed to enhance investigators--natural analytical capabilities to comprehend and analyze large semantic graphs. The paper describes the overall framework design, presents major development accomplishments to date, and discusses future directions of a new visual analytics system known as Have Green.

  15. summer_peak_2004.xls

    Gasoline and Diesel Fuel Update (EIA)

    2009 (Megawatts and 2004 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC...

  16. summer_peak_2003.xls

    Gasoline and Diesel Fuel Update (EIA)

    2008 (Megawatts and 2003 Base Year) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC...

  17. C3DIV.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) per Building (million Btu) per Square Foot (thousand Btu) per Worker (million Btu) NEW...

  18. C16DIV.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    cubic feet) per Square Foot (cubic feet) per Worker (thousand cubic feet) per Building (thousand dollars) per Square Foot (dollars) per Thousand Cubic Feet (dollars) NEW...

  19. C4DIV.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Floorspace per Building (thousand square feet) Total (million dollars) per Building (thousand dollars) per Square Foot (dollars) per Million Btu (dollars) NEW ENGLAND...

  20. C10DIV.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Building (thousand kWh) per Square Foot (kWh) per Worker (thousand kWh) per Building (thousand dollars) per Square Foot (dollars) per kWh (dollars) NEW ENGLAND...

  1. C15DIV.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) Total (billion cubic feet) Total (million dollars) NEW ENGLAND ... 45...

  2. tablehc15.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    7.1 7.0 8.0 12.1 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 5.5 5.5 6.7 9.5 1.......................................................................... 28.6 1.8 2.0 2.3 2.8 2.......................................................................... 29.5 2.3 1.9 2.0 3.4 3.......................................................................... 14.7 0.7 0.8 0.9 1.4

  3. c10a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    (Square Feet) 1,001 to 5,000 ... 143 187 90 170 95 1,313 1,709 1,010 1,915 975 108.7 109.6 88.8 89.0 97.9 5,001 to 10,000 ......

  4. Attachment A -- Deliverables.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Postretirement Benefits (PRB) Other Than Pensions (JUL 2005) Ensure receipt of credit for pension fund asset reversions and ensure flowdown to subcontractors during contractor...

  5. EM Contractor List.xls

    Office of Environmental Management (EM)

    Vrain Facility Improvements Project Idaho National Laboratory CD3A Sandia Corporation Lockheed Martin 001035 ID-0030B.C4 Accelerated Retrieval Project IX Idaho National Laboratory ...

  6. EIA-803_20150504.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    comment from another, press ALT+ENTER.) For the PC Electronic Data Reporting Option (PEDRO) software, call (202) 586-9659. (See Form instructions, pg 1.) Crude Oil (including...

  7. EIA-809_20150504.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Code PART 3. OXYGENATE ACTIVITY (Barrels) For the PC Electronic Data Reporting Option (PEDRO) software, call (202) 586-9659. (See Form instructions, pg 1.) Mailing Address of...

  8. EIA895_update.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    OMB No. 1905-0175 Expiration Date: 12/31/2011 Version No.: 2009.01 PART 1. RESPONDENT IDENTIFICATION DATA REPORT PERIOD: 2 0 STATE NAME: If this is a resubmission, enter an "X" in the box: If any Respondent Identification Data has changed since the last report, enter an "X" in the box: Contact Name: Phone No.: - - Ext: - Address 1: Email: Address 2: Fax: City: State: Zip: - https://signon.eia.doe.gov/upload/noticeoog.jsp (8) ANNUAL QUANTITY AND VALUE OF NATURAL GAS PRODUCTION

  9. c30a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Floorspace (Square Feet) 1,001 to 5,000 ... 57 84 35 58 16 666 1,015 427 832 234 84.8 83.1 81.9 69.6 66.6 5,001 to 10,000 ......

  10. tablehc6.3.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Income Relative to Poverty Line Below 100 Percent......1.1 1.3 1.6 1.9 1. Below 150 percent of poverty line or 60 percent of median State ...

  11. tablehc3.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent......2.3 Q Q Q 0.4 1. Below 150 percent of poverty line or 60 percent of median State ...

  12. tablehc4.3.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Income Relative to Poverty Line Below 100 Percent......0.3 1.0 1.6 Q 1. Below 150 percent of poverty line or 60 percent of median State ...

  13. c13a.xls

    Gasoline and Diesel Fuel Update (EIA)

    1,040 344 101 6,782 Energy End Uses (more than one may apply) Buildings with Space Heating ... 4,171 66,410 15.9 10,365 3,433 1,006 78,955 Buildings with Cooling...

  14. 06 Run R1.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3-2006 Run Shutdown Maintenance / AP Injector / SPEAR Startup Spear Down University Holidays AP 28 AP 28 MA AP 15 22 11 13 Conf 12 User 1 5 4 3 2 28 31 30 29 20 18 17 27 26 25 19 21 AP 20 16 11 14 AP 7 8 8 15 16 13 AP 12 11 14 MA 1 6 AP 1 16 MA 6 8 9 2 9 12 MA AP 21 25 26 24 23 22 20 Dwn 4pm 24 26 29 28 27 27 28 29 30 2 4 3 10 3 8 9 26 27 23 24 25 3 MA 7 3 3 4 1 1 2 4 2 1 1 9 6 11 7 8 10 9 8 13 14 3 4 3 2 4 6 8 21 5 15 12 17 18 14 16 13 9 10 11 5 MA 15 14 5 11 12 10 AP 6 AP 10 MA 13 1 6 7 4 29

  15. c9a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Q Q Q Q Q 1,119 Q Q Q Principal Building Activity Education ... 74 53 76 1,198 640 1,027 61.4 82.9 74.3...

  16. c8a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Q 171 Q Q 1,572 Q Q 109.0 Q Principal Building Activity Education ... 45 198 Q 552 2,445 341 81.0 80.9 Q Food...

  17. c7a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Q Q Q Q 1,451 1,192 Q Q Q Principal Building Activity Education ... Q 143 175 Q 1,384 1,990 Q 103.1 87.7 Food...

  18. c24a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 803 42.0 17.9 37.4 71.0 6.3 0.33 7.86 Building Floorspace (Square Feet) 1,001 to 5,000 ... 220 78.6 23.8...

  19. c23a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    (thousand dollars) per Square Foot (dollars) per Thousand Cubic Feet (dollars) All Buildings ... 803 42.0 17.9 37.4 71.0 6.3 0.33 7.86 Building...

  20. c2a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 4,859 71,658 107,897 82,783 16,010 1,826 7,279 Building Floorspace (Square Feet) 1,001 to 5,000 ......

  1. c4a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 4,859 71,658 14.7 107,897 22.2 1.51 16.54 Building Floorspace (Square Feet) 1,001 to 5,000 ... 2,586...

  2. c11a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 1,248 2,553 2,721 13,955 32,332 25,371 89.4 79.0 107.3 Principal Building Activity Education ......

  3. c14a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 226 14.9 3.8 8.8 18.1 17.9 1.18 0.079 Building Floorspace (Square Feet) 1,001 to 5,000 ... 48 17.8...

  4. c22a.xls

    Gasoline and Diesel Fuel Update (EIA)

    Buildings ... 162 538 343 17,509 32,945 19,727 9.2 16.3 17.4 Building Floorspace (Square Feet) 1,001 to 5,000 ......

  5. c31a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Buildings ... 467 882 688 7,144 21,928 19,401 65.4 40.2 35.5 Principal Building Activity Education ... Q 137...

  6. summer_peak_2005.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Area Power Pool (MAPP) to Midwest Reliability Organization (MRO). * The MRO, SERC, and SPP regional boundaries were altered as utilities changed reliability organizations. ...

  7. o_al_05.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Destination by Method of Transportation Electricity Generation Coke Plants Industrial Plants (Except Coke) Residential and Commercial Total Alabama 770 851 1,739 * 3,360 Railroad...

  8. Pressure Data Within BOP- XLS

    Broader source: Energy.gov [DOE]

    This file describes the components within the BOP and the pressure readings taken during diagnostic operations on May 25.

  9. QTR4%2009.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Footnotes 1 Does not include mark-to-market adjustments required by derivative accounting guidance as amended or reflect the change in accounting for power "bookout"...

  10. EIA895_update.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    REPORT PERIOD: 2 0 STATE NAME: If this is a resubmission, enter an "X" in the box: If any Respondent Identification Data has changed since the last report, enter an "X" in the box: ...

  11. c38a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  12. c33a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  13. c13a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  14. c37a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  15. c36a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  16. c35a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  17. c1a.xls

    Gasoline and Diesel Fuel Update (EIA)

    EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey. See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  18. FY14 - Qtr1.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    0.00 0.00 1,543.25 43.70 87,682 2,483 Area 3 0.00 0.00 0.00 0.00 0 0 Area 5 0.00 0.00 0.00 0.00 230,052 6,514 Mixed 0.00 0.00 9,826.81 278.26 53,096 1,504 Area 3 0.00 0.00 0.00 ...

  19. FY16 Projects.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    FY16 Budget Rollout Fact Sheet FY16 Budget Rollout Fact Sheet Download the fact sheet below to read the full highlights of the President's FY 2016 Budget for the Department of Energy, which provides $30 billion to support the Department in the areas of nuclear security, clean energy, environmental cleanup, climate change response, science and innovation. This includes: Supporting a nearly $5 billion all-of-the-above transformational research and development portfolio in critical energy

  20. eia-757_b.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Call: (877) 800-5261 Plant Address 1: Plant Address 2: City: State: County: Zip: - Plant Owner Companies (Top Three): 1 2 3 Operator Company: Processing Plant Operations Contact: ...

  1. Beam Time Changes.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    He 45 55 55 55 55 55 55 55 70 70 70 14 N 40 40 40 40 40 40 40 40 50 50 50 20 Ne 50 40 25* 35 30 30 30 30 45 45 45 40 Ar 50 40 25* 35 30 30 30 30 45 45 45 63 Cu 50 40 35 35 35 35 35...

  2. d_al_05.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Origin by Method of Transportation Electricity Generation Coke Plants Industrial Plants (Except Coke) Residential and Commercial Total Alabama 770 851 1,739 3,360 Railroad 642 1...

  3. c34a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 14,248 0.02 14.7 0.02 1.03 Principal Building Activity Education ... 12,911 0.18 13.7 0.19 1.06 Food Sales...

  4. c21a.xls

    Gasoline and Diesel Fuel Update (EIA)

    201 412 431 13,124 31,858 25,200 15.3 12.9 17.1 Principal Building Activity Education ... 9 55 45 806 5,378 3,687 11.1 10.2 12.2...

  5. c29a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Q Q Q Q Q Q Q Q Q Principal Building Activity Education ... 16 21 28 797 420 802 20.6 48.8 34.8 Food...

  6. c3a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 8 7,660 937.6 906 110,855 118.2 Principal Building Activity Education ... 386 9,874 25.6 820 2,125 83.1 Food Sales...

  7. c28a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Q 20 Q Q Q Q Q 19.3 Q Principal Building Activity Education ... 14 25 Q 380 1,274 Q 38.1 19.6 Q Food...

  8. c17a.xls

    Gasoline and Diesel Fuel Update (EIA)

    41 131 168 3,430 10,469 12,202 12.0 12.5 13.8 Building Floorspace (Square Feet) 1,001 to 5,000 ... 5 9 20 369 662 921 12.9 13.9 21.9 5,001 to 10,000...

  9. c20a.xls

    Gasoline and Diesel Fuel Update (EIA)

    137 254 189 261 202 11,300 18,549 12,374 17,064 10,894 12.1 13.7 15.3 15.3 18.5 Building Floorspace (Square Feet) 1,001 to 5,000 ... 19 27 14 32 23...

  10. c18a.xls

    Gasoline and Diesel Fuel Update (EIA)

    66 254 57 5,523 13,837 3,546 12.0 18.3 16.2 Building Floorspace (Square Feet) 1,001 to 5,000 ... 10 28 7 821 1,233 481 12.4 22.4 15.4 5,001 to...

  11. c15a.xls

    Gasoline and Diesel Fuel Update (EIA)

    72 234 452 185 13,899 17,725 26,017 12,541 12.4 13.2 17.4 14.7 Building Floorspace (Square Feet) 1,001 to 5,000 ... 14 30 52 19 1,031 1,742 2,410...

  12. c6a.xls

    Gasoline and Diesel Fuel Update (EIA)

    24,395 23,398 38,398 21,706 17.47 13.01 16.95 20.42 1.74 1.29 1.44 1.69 Building Floorspace (Square Feet) 1,001 to 5,000 ... 2,398 3,255 4,899 2,530...

  13. Uranium calculations.xls.xml

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Nuclide t 1/2 Low Energy Yields (ions/sec on target) Re-Accelerated Yields (ions/sec on target) 83As 13.4s 6.30E+02 9.30E+01 83Se 22.3m 2.40E+02 3.80E+01 84As 5.5s 5.90E+02 8.30E+01 84Se 3.3m 6.90E+02 1.10E+02 85As 2.03s 7.00E+02 8.30E+01 85Se-m 19s 1.00E+03 1.50E+02 85Se 39s 1.00E+03 1.60E+02 85Br 2.87m 5.20E+02 8.10E+01 86As 0.9s 2.50E+02 2.10E+01 86Se 15s 1.80E+03 2.70E+02 86Br-m 4.5s 5.70E+02 7.80E+01 86Br 55.5s 5.70E+02 8.70E+01 87Se 5.6s 1.90E+03 2.70E+02 87Br 55.9s 3.00E+03 4.60E+02 87Kr

  14. monthly_peak_2003.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    O Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 1996 through 2003 and Projected 2004 through 2005 (Megawatts and 2003 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid ECAR FRCC MAAC MAIN MAPP/MR NPCC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW)

  15. monthly_peak_2005.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid FRCC MRO NPCC RFC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak

  16. monthly_peak_2006.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    6 Released: February 7, 2008 Next Update: October 2008 Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Corporation Region 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid FRCC MRO NPCC RFC SERC SPP ERCOT WECC Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak Hour Demand (MW) Peak

  17. peak_load_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    2. Noncoincident Peak Load, by North American Electric Reliability Corporation Assessment Area, 1990-2010 Actual, 2011-2015 Projected (Megawatts) Interconnection NERC Regional Assesment Area 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 FRCC 27,266 28,818 30,601 32,823 32,904 34,524 35,444 35,375 38,730 37,493 37,194 39,062 40,696 40,475 42,383 46,396 45,751 46,676 44,836 NPCC 44,116 46,594 43,658 46,706 47,581 47,705 45,094 49,269 49,566 52,855

  18. summer_capacity_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Interconnection NERC Regional Assesment Area 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 FRCC 27,162 27,773 28,898 29,435 30,537 31,649 31,868 32,874 34,562 34,832 35,666 38,932 37,951 40,387 42,243 45,950 45,345 46,434 44,660 46,263 NPCC 46,016 45,952 46,007 46,380 47,465 48,290 48,950 50,240 51,760 53,450 54,270 55,888 55,164 53,936 51,580 57,402 60,879 58,221 59,896 55,730 Balance of Eastern Region 332,679 337,297 341,869 349,984

  19. winter_capacity_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Table 4.B Winter Net Internal Demand, Capacity Resources, and Capacity Margins by North American Electric Reliability Corporation Region, 2001-2010 Actual, 2011-2015 Projected (Megawatts and Percent) Interconnection NERC Regional Assesment Area 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 2010/ 2011 2011/2012E 2012/2013E 2013/2014E 2014/2015E 2015/2016E FRCC 39,699 42,001 36,229 41,449 42,493 45,993 46,093 45,042 51,703 45,954 44,196 44,750 45,350

  20. winter_peak_2003.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ) Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 2b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 1990 through 2003 and Projected 2004 through 2008 (Megawatts and 2003 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP (U.S. NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990/1991 484,231 67,097

  1. winter_peak_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 1990 through 2004 and Projected 2005 through 2009 (Megawatts and 2004 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP/MRO (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990/1991 484,231 67,097 30,800 36,551 32,461 21,113 40,545 86,648 38,949 35,815 94,252 1991/1992 485,761

  2. winter_peak_2005.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    2b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year FRCC MRO (U.S.) NPCC (U.S.) RFC SERC SPP ERCOT WECC (U.S.) 2005/2006 626,365 42,657 33,748 46,828 151,600 164,638 31,260 48,141 107,493 Contiguous U.S. Projected FRCC MRO (U.S.) NPCC (U.S.)

  3. schedule6_2010.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 2010 US ERCOT - AC 300-399 3 2013 Gray Tesla 218 OH CTT 100 13TPIT005Planned ... US ERCOT - AC 300-399 9 2013 Silverton Tesla 170 OH CTT 100 13TPIT005Planned ...

  4. eia-191_Nov2014.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Failure to comply may result in criminal fines, civil penalties and other sanctions as provided by law. For the sanctions and the provisions concerning the confidentiality of ...

  5. c1a.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 3,825 63,560 6,149 10,402 3,445 1,987 181 536 Buildings with Water Heating ... 3,659 62,827 6,158 10,202 3,379 2,035 218 525 Notes: Site...

  6. Attachment A -- Deliverables.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    4: Unrevised SFO Paragraphs Reissued Attachment 4: Unrevised SFO Paragraphs Reissued Unrevised SFO Paragraphs Reissued (35.68 KB) More Documents & Publications Attachment 2: Solicitation for Offers with New and Revised Green Lease Text Attachment 1: Green Lease Policies and Procedures for Lease Acquisition 1

    B - J Deliverables Attachment A TOC Deliverables DE-AC27-08RV14800 SEC. Contract Section Description Action Timing TFP CO ESQ OPA IR/HR ORP MGR DCAA B B.2 Modify contract to obligate

  7. Attachment A -- Deliverables.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    I Deliverables Attachment A TOC Deliverables DE-AC27-08RV14800 SEC. FAR/DEAR Clause Reference Description Action Timing TFP CO ESQ OPA IR/HR ORP MGR DCAA I.2 FAR 52.202-1 Definitions (JUL 2004) as supplemented by DEAR 952.202-1 (Mar 2002) Verify compliance As Required L I.3 FAR 52.203-3 Gratuities (APR 1984) Verify compliance As Required L I.4 FAR 52.203-5 Covenant Against Contingent Fees (APR 1984) Verify compliance As Required L I.5 FAR 52.203-6 Restrictions on Subcontractor Sales to the

  8. FY16 Projects.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    LinnLujndquist Assessing Impacts of Wind Turbines and Wind Farms CU Boulder ReisnerGuimond Hurricane Intensity & Structure U Maryland ElliottWingenter Aspects of the Ocean-Ice-...

  9. tablehc1.3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    27.7 26.0 17.6 10.0 7 7.8 11.6 No Main Space Heating Equipment...... 1.2 N N N N N N N Have Main Space Heating Equipment...... 109.8 6.1 27.7 26.0 17.6 10.0 7 7.8 11.6 ...

  10. monthly_peak_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    83,670 37,521 42,442 39,238 22,995 45,227 132,964 26,415 39,075 95,673 2001 573,509 ... 80,476 34,073 38,812 38,157 21,241 41,544 132,318 28,191 43,837 93,195 2003 551,505 ...

  11. c27a.xls

    Gasoline and Diesel Fuel Update (EIA)

    53.1 Building Floorspace (Square Feet) 1,001 to 5,000 ... Q 42 69 Q 427 741 Q 98.4 92.9 5,001 to 10,000 ... Q 32 49 Q...

  12. c32a.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    . 580 986 471 12,407 22,762 13,304 46.8 43.3 35.4 Building Floorspace (Square Feet) 1,001 to 5,000 ... 86 103 61 1,245 1,271 659 69.0 81.0 92.1 5,001...

  13. EM Contractor List.xls

    Office of Environmental Management (EM)

    Oak Ridge CD1 TBD 001012 OR-0040.C5 K-31 Facility Demolition Oak Ridge CD3 URS CH2M Hill Oak Ridge, LLC 001021 14-D-403 Outfall 200 Mercury Treatment Facility Oak Ridge CD1 URS...

  14. schedule6_2002.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... I 100 U SERC ENT AC 230 230 884 Jun-05 China Porter 63 OH P B 954 ACSR 2 2 2 25251 I 100 U SERC ENT AC 230 230 521 Jun-05 Rankin South Jackson 17.9 OH P S 1272 ACSR 1 2 2 25251 ...

  15. schedule6_2005.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... I 100 U RFC 0 AC 345 345 1195 512008 JK Smith Spurlock Avon 17.5 OH H S 954 ACSR 2 2 2 ... U SERC TVA AC 345 345 1195 612007 J.K. Smith North Clark 18 - Overh H-frame steel 954 ...

  16. schedule6_2004.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    I 0 U ECAR AC 345 345 1195 5202008 JK Smith Spurlock Avon 17.5 OH 954 ACSR 2 1 1 ... 2600 12202012 Joshua Falls - AEP Lady Smith 85 OH T S 1033 ACSR 3 1 1 19876 I 0 U SERC ...

  17. schedule6_2006.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... U SERC TVA AC 345 345 1947 6-2007 J.K. Smith North Clark 18 - Overh H-frame steel 954 ... U SERC TVA AC 345 345 1947 6-2009 J.K. Smith West Garrard County 36 - Overh H-frame ...

  18. schedule6_2003.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... -19 I1 0 U SERC SOU AC 230 230 807 Jun-11 Smith Laguna Beach 14 OH P S 1351 OT 1 2 2 7801 ... 500 2600 May-12 Joshua Falls ( AEP) Lady Smith 85 OH T S 1033 ACSR 3 1 1 19876 I1 0 U SPP ...

  19. schedule6_2001.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Tipsico Lake Blackfoot - Madrid 0 0 0 0 4254 I 0 U ECAR AC 345 345 0 Jun-04 Avon J.K. Smith 17 0 0 0 5580 C 0 U ECAR AC 230 230 0 Jun-04 Urbana MDM Tap - Montgomery 4 0 0 0 550 I ...

  20. winter_peak_2006.xls

    Gasoline and Diesel Fuel Update (EIA)

    entity that oversee electric reliability. * NERC Regional names may be found on the EIA web page for electric reliability. * Regional name has changed from Mid-Continent Area Power ...

  1. summer_peak_2006.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    entity that oversee electric reliability. * NERC Regional names may be found on the EIA web page for electric reliability. * Regional name has changed from Mid-Continent Area Power ...

  2. A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

    SciTech Connect (OSTI)

    Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.; Pebay, Philippe Pierre; Gentile, Ann C.; Thompson, David C.; Roe, Diana C.; De Sapio, Vincent; Brandt, James M.

    2010-08-01

    The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in job queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.

  3. Exotic equilibria of Harary graphs and a new minimum degree lower bound for synchronization

    SciTech Connect (OSTI)

    Canale, Eduardo A.; Monzn, Pablo

    2015-02-15

    This work is concerned with stability of equilibria in the homogeneous (equal frequencies) Kuramoto model of weakly coupled oscillators. In 2012 [R. Taylor, J. Phys. A: Math. Theor. 45, 115 (2012)], a sufficient condition for almost global synchronization was found in terms of the minimum degreeorder ratio of the graph. In this work, a new lower bound for this ratio is given. The improvement is achieved by a concrete infinite sequence of regular graphs. Besides, non standard unstable equilibria of the graphs studied in Wiley et al. [Chaos 16, 015103 (2006)] are shown to exist as conjectured in that work.

  4. A weak zero-one law for sequences of random distance graphs

    SciTech Connect (OSTI)

    Zhukovskii, Maksim E

    2012-07-31

    We study zero-one laws for properties of random distance graphs. Properties written in a first-order language are considered. For p(N) such that pN{sup {alpha}}{yields}{infinity} as N{yields}{infinity}, and (1-p)N{sup {alpha}} {yields} {infinity} as N {yields} {infinity} for any {alpha}>0, we succeed in refuting the law. In this connection, we consider a weak zero-one j-law. For this law, we obtain results for random distance graphs which are similar to the assertions concerning the classical zero-one law for random graphs. Bibliography: 18 titles.

  5. Omega: an Overlap-graph de novo Assembler for Meta-genomics

    SciTech Connect (OSTI)

    Haider, Bahlul; Ahn, Tae-Hyuk; Bushnell, Brian; Chai, JJ; Copeland, Alex; Pan, Chongle

    2014-01-01

    Motivation: Metagenomic sequencing allows reconstruction of mi-crobial genomes directly from environmental samples. Omega (overlap-graph metagenome assembler) was developed here for assembling and scaffolding Illumina sequencing data of microbial communities. Results: Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming small branches. Unitigs were generat-ed based on minimum cost flow analysis of the overlap graph. Obtained unitigs were merged to contigs and scaffolds using mate-pair information. Omega was compared with two de Bruijn graph assemblers, SOAPdenovo and IDBA-UD, using a publically-available Illumina sequencing dataset of a 64-genome mock com-munity. The assembly results were verified by their alignment with reference genomes. The overall performances of the three assem-blers were comparable and each assembler provided best results for a subset of genomes.

  6. How do I display the Map of Wind Farms csv coordinates in ArcMap...

    Open Energy Info (EERE)

    display and how it is stored. I agree that it is not an ideal format for using with ArcGIS, but this can be easily modified using your favorite spreadsheet editor. Since Excel...

  7. Structure Discovery in Large Semantic Graphs Using Extant Ontological Scaling and Descriptive Statistics

    SciTech Connect (OSTI)

    al-Saffar, Sinan; Joslyn, Cliff A.; Chappell, Alan R.

    2011-07-18

    As semantic datasets grow to be very large and divergent, there is a need to identify and exploit their inherent semantic structure for discovery and optimization. Towards that end, we present here a novel methodology to identify the semantic structures inherent in an arbitrary semantic graph dataset. We first present the concept of an extant ontology as a statistical description of the semantic relations present amongst the typed entities modeled in the graph. This serves as a model of the underlying semantic structure to aid in discovery and visualization. We then describe a method of ontological scaling in which the ontology is employed as a hierarchical scaling filter to infer different resolution levels at which the graph structures are to be viewed or analyzed. We illustrate these methods on three large and publicly available semantic datasets containing more than one billion edges each. Keywords-Semantic Web; Visualization; Ontology; Multi-resolution Data Mining;

  8. Wedge sampling for computing clustering coefficients and triangle counts on large graphs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Seshadhri, C.; Pinar, Ali; Kolda, Tamara G.

    2014-05-08

    Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of such graphs. Some of the most useful graph metrics are based on triangles, such as those measuring social cohesion. Despite the importance of these triadic measures, algorithms to compute them can be extremely expensive. We discuss the method of wedge sampling. This versatile technique allows for the fast and accurate approximation of various types of clustering coefficients and triangle counts. Furthermore, these techniques are extensible to counting directed triangles in digraphs. Our methods come with provable andmore » practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state of the art, while providing nearly the accuracy of full enumeration.« less

  9. Graph of Total Number of Oligos Within Windows of a Sequence

    Energy Science and Technology Software Center (OSTI)

    1995-11-28

    SEQWIN is user-friendly software which graphs the total number of oligos present in a sequence. The sequence is scanned one window at a time; windows can be overlapping. Each bar on the graph represents a single window down the sequence. The user specifies the sequence of interest and a list of oligos as program input. If the sequence is known, locations of specific structure or sequences can be specified and compared with the bars onmore » a graph. The window size, amount of overlap of the windows, number of windows to be considered, and the starting position of the first window used can be adjusted at the user's discretion.« less

  10. Synthetic graph generation for data-intensive HPC benchmarking: Scalability, analysis and real-world application

    SciTech Connect (OSTI)

    Powers, Sarah S.; Lothian, Joshua

    2014-12-01

    The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allows the emulation of a broad spectrum of application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report described the in-depth analysis of the generated synthetic graphs' properties at a variety of scales using different generator implementations and examines their applicability to replicating real world datasets.

  11. EIA - Gulf of Mexico Energy Data

    Gasoline and Diesel Fuel Update (EIA)

    Electricity Generating Capacity Release Date: January 3, 2013 | Next Release: August 2013 Year Existing Units by Energy Source Unit Additions Unit Retirements 2011 XLS XLS XLS 2010 XLS XLS XLS 2009 XLS XLS XLS 2008 XLS XLS XLS 2007 XLS XLS XLS 2006 XLS XLS XLS 2005 XLS XLS XLS 2004 XLS XLS XLS 2003 XLS XLS XLS Source: Form EIA-860, "Annual Electric Generator Report." Related links Electric Power Monthly Electric Power Annual Form EIA-860 Source Data

    Gulf of Mexico Fact Sheet

  12. Absolutely continuous spectrum implies ballistic transport for quantum particles in a random potential on tree graphs

    SciTech Connect (OSTI)

    Aizenman, Michael; Warzel, Simone

    2012-09-15

    We discuss the dynamical implications of the recent proof that for a quantum particle in a random potential on a regular tree graph absolutely continuous (ac) spectrum occurs non-perturbatively through rare fluctuation-enabled resonances. The main result is spelled in the title.

  13. Company Level Imports Archives

    U.S. Energy Information Administration (EIA) Indexed Site

    Company Level Imports Company Level Imports Archives 2015 Imports by Month January XLS February XLS March XLS April XLS May XLS June XLS July XLS August XLS September XLS October...

  14. Exact scattering matrix of graphs in magnetic field and quantum noise

    SciTech Connect (OSTI)

    Caudrelier, Vincent; Mintchev, Mihail; Ragoucy, Eric

    2014-08-15

    We consider arbitrary quantum wire networks modelled by finite, noncompact, connected quantum graphs in the presence of an external magnetic field. We find a general formula for the total scattering matrix of the network in terms of its local scattering properties and its metric structure. This is applied to a quantum ring with N external edges. Connecting the external edges of the ring to heat reservoirs, we study the quantum transport on the graph in ambient magnetic field. We consider two types of dynamics on the ring: the free Schrdinger and the free massless Dirac equations. For each case, a detailed study of the thermal noise is performed analytically. Interestingly enough, in presence of a magnetic field, the standard linear Johnson-Nyquist law for the low temperature behaviour of the thermal noise becomes nonlinear. The precise regime of validity of this effect is discussed and a typical signature of the underlying dynamics is observed.

  15. Quantum dynamics via Planck-scale-stepped action-carrying 'Graph Paths'

    SciTech Connect (OSTI)

    Chew, Geoffrey F.

    2003-05-05

    A divergence-free, parameter-free, path-based discrete-time quantum dynamics is designed to not only enlarge the achievements of general relativity and the standard particle model, by approximations at spacetime scales far above Planck scale while far below Hubble scale, but to allow tackling of hitherto inaccessible questions. ''Path space'' is larger than and precursor to Hilbert-space basis. The wave-function-propagating paths are action-carrying structured graphs-cubic and quartic structured vertices connected by structured ''fermionic'' or ''bosonic'' ''particle'' and ''nonparticle'' arcs. A Planck-scale path step determines the gravitational constant while controlling all graph structure. The basis of the theory's (zero-rest-mass) elementary-particle Hilbert space (which includes neither gravitons nor scalar bosons) resides in particle arcs. Nonparticle arcs within a path are responsible for energy and rest mass.

  16. The d-edge shortest-path problem for a Monge graph

    SciTech Connect (OSTI)

    Bein, W.W.; Larmore, L.L.; Park, J.K.

    1992-07-14

    A complete edge-weighted directed graph on vertices 1,2,...,n that assigns cost c(i,j) to the edge (i,j) is called Monge if its edge costs form a Monge array, i.e., for all i < k and j < l, c[i, j]+c[k,l]{le} < c[i,l]+c[k,j]. One reason Monge graphs are interesting is that shortest paths can be computed quite quickly in such graphs. In particular, Wilber showed that the shortest path from vertex 1 to vertex n of a Monge graph can be computed in O(n) time, and Aggarwal, Klawe, Moran, Shor, and Wilber showed that the shortest d-edge 1-to-n path (i.e., the shortest path among all 1-to-n paths with exactly d edges) can be computed in O(dn) time. This paper`s contribution is a new algorithm for the latter problem. Assuming 0 {le} c[i,j] {le} U and c[i,j + 1] + c[i + 1,j] {minus} c[i,j] {minus} c[i + 1, j + 1] {ge} L > 0 for all i and j, our algorithm runs in O(n(1 + 1g(U/L))) time. Thus, when d {much_gt} 1 + 1g(U/L), our algorithm represents a significant improvement over Aggarwal et al.`s O(dn)-time algorithm. We also present several applications of our algorithm; they include length-limited Huffman coding, finding the maximum-perimeter d-gon inscribed in a given convex n-gon, and a digital-signal-compression problem.

  17. 2007 CBECS Large Hospital Building FAQs: 2003-2007 Comparison Graphs

    Gasoline and Diesel Fuel Update (EIA)

    FAQs: 2003-2007 Comparison Graphs Main Report | Methodology | FAQ | List of Tables CBECS 2007 - Release date: August 17, 2012 Jump to: Figure 1 | Figure 2 | Figure 3 | Figure 4 | Figure 5 Figure 1 Number of Large Hospital Buildings and 95% Confidence Intervals by Census Region, 2003 and 2007 Figure 2 Total Floorspace and 95% Confidence Intervals in Large Hospital Buildings by Census Region, 2003 and 2007 Figure 3 Major Fuel Intensity and 95% Confidence Intervals by Census Region, 2003 and 2007

  18. Compact Graph Representations and Parallel Connectivity Algorithms for Massive Dynamic Network Analysis

    SciTech Connect (OSTI)

    Madduri, Kamesh; Bader, David A.

    2009-02-15

    Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing large-scale information networks, encapsulating dynamic interaction data in the order of billions of entities. We present new data structures to represent dynamic interaction networks, and discuss algorithms for processing parallel insertions and deletions of edges in small-world networks. With these new approaches, we achieve an average performance rate of 25 million structural updates per second and a parallel speedup of nearly28 on a 64-way Sun UltraSPARC T2 multicore processor, for insertions and deletions to a small-world network of 33.5 million vertices and 268 million edges. We also design parallel implementations of fundamental dynamic graph kernels related to connectivity and centrality queries. Our implementations are freely distributed as part of the open-source SNAP (Small-world Network Analysis and Partitioning) complex network analysis framework.

  19. Scaling Graph Community Detection on the Tilera Many-core Architecture

    SciTech Connect (OSTI)

    Chavarría-Miranda, Daniel; Halappanavar, Mahantesh; Kalyanaraman, Anantharaman

    2014-12-01

    In an era when power constraints and data movement are proving to be significant barriers for the application of high-end computing, the Tilera many-core architecture offers a low-power platform exhibiting many important characteristics of future systems, including a large number of simple cores, a sophisticated network-on-chip, and fine-grained control over memory and caching policies. While this emerging architecture has been previously studied for structured compute-intensive kernels, benchmarking the platform for data-bound, irregular applications present significant challenges that have remained unexplored. Community detection is an advanced prototypical graph-theoretic operation with applications in numerous scientific domains including life sciences, cyber security, and power systems. In this work, we explore multiple design strategies toward developing a scalable tool for community detection on the Tilera platform. Using several memory layout and work scheduling techniques we demonstrate speedups of up to 46x on 36 cores of the Tilera TileGX36 platform over the best serial implementation, and also show results that have comparable quality and performance to mainstream x86 platforms. To the best of our knowledge this is the first work addressing graph algorithms on the Tilera platform. This study demonstrates that through careful design space exploration, low-power many-core platforms like Tilera can be effectively exploited for graph algorithms that that embody all the essential characteristics of an irregular application.

  20. A three-colour graph as acomplete topological invariant for gradient-like diffeomorphisms of surfaces

    SciTech Connect (OSTI)

    Grines, V Z; Pochinka, O V; Kapkaeva, S Kh

    2014-10-31

    In apaper of Oshemkov and Sharko, three-colour graphs were used to make the topological equivalence of Morse-Smale flows on surfaces obtained by Peixoto more precise. In the present paper, in the language of three-colour graphs equipped with automorphisms, we obtain acomplete (including realization) topological classification of gradient-like cascades on surfaces. Bibliography: 25 titles.

  1. Petroleum Supply Monthly September 2004

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Ranges in Inventory Graphs XLS HTML Entire . The entire report as a single file. PDF 1.2MB . . Front Matter . Petroleum Supply Monthly Cover Page, Preface, and Table of...

  2. TableHC10.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    0.1 Housing Unit Characteristics by U.S. Census Region, 2005 Total......................................................................... 111.1 20.6 25.6 40.7 24.2 Census Region and Division Northeast.............................................................. 20.6 20.6 N N N New England..................................................... 5.5 5.5 N N N Middle Atlantic................................................... 15.1 15.1 N N N

  3. TableHC10.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    25.6 40.7 24.2 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 16.8 21.7 33.8 19.5 1.......................................................................... 28.6 5.0 6.3 11.2 6.1 2.......................................................................... 29.5 6.2 6.5 10.5 6.3 3.......................................................................... 14.7 2.5 4.0 5.0 3.1

  4. TableHC10.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Number of Water Heaters 1............................................................................... 106.3 19.6 24.5 39.0 23.2 2 or More.................................................................. 3.7 0.3 0.9 1.5 1.0 Do Not Use Hot Water.............................................. 1.1 0.7 Q Q Q Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 16.1 23.5 38.2 21.9 Two or More Housing

  5. TableHC11.12.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    15.1 5.5 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.9 5.3 1.6 Use a Personal Computer.............................................. 75.6 13.7 9.8 3.9 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 10.4 7.3 3.1 Laptop Model............................................................. 16.9 3.3 2.6 0.7 Hours Turned on Per Week Less than 2

  6. TableHC11.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    15.1 5.5 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 16.8 12.2 4.6 1.......................................................................... 28.6 5.0 3.5 1.5 2.......................................................................... 29.5 6.2 4.8 1.4 3.......................................................................... 14.7 2.5 1.7 0.8

  7. TableHC11.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Number of Water Heaters 1............................................................................... 106.3 19.6 14.4 5.2 2 or More.................................................................. 3.7 0.3 Q Q Do Not Use Hot Water.............................................. 1.1 0.7 0.6 Q Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 16.1 11.7 4.5 Two or More Housing Units....................................... 10.3 3.7

  8. TableHC12.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    2.1 Housing Unit Characteristics by Midwest Census Region, 2005 Total......................................................................... 111.1 25.6 17.7 7.9 Urban/Rural Location (as Self-Reported) City....................................................................... 47.1 9.7 7.3 2.4 Town..................................................................... 19.0 5.0 2.9 2.1 Suburbs................................................................ 22.7 5.7 4.3 1.4

  9. TableHC12.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    5.6 17.7 7.9 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 21.7 14.5 7.2 1.......................................................................... 28.6 6.3 4.4 1.9 2.......................................................................... 29.5 6.5 4.2 2.3 3.......................................................................... 14.7 4.0 2.8 1.2

  10. TableHC12.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Number of Water Heaters 1............................................................................... 106.3 24.5 17.1 7.4 2 or More.................................................................. 3.7 0.9 0.5 0.4 Do Not Use Hot Water.............................................. 1.1 Q Q Q Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 23.5 16.2 7.3 Two or More Housing Units....................................... 10.3 1.9

  11. TableHC13.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    3.1 Housing Unit Characteristics by South Census Region, 2005 Total......................................................................... 111.1 40.7 21.7 6.9 12.1 Urban/Rural Location (as Self-Reported) City....................................................................... 47.1 17.8 10.5 2.2 5.1 Town..................................................................... 19.0 4.9 2.2 0.7 2.0 Suburbs................................................................ 22.7 7.6 4.1 1.1 2.4

  12. TableHC13.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    0.7 21.7 6.9 12.1 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 33.8 17.5 6.1 10.3 1.......................................................................... 28.6 11.2 6.5 1.5 3.2 2.......................................................................... 29.5 10.5 5.4 2.0 3.1 3.......................................................................... 14.7 5.0 2.1 1.2 1.7

  13. TableHC13.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Number of Water Heaters 1............................................................................... 106.3 39.0 21.1 6.6 11.3 2 or More.................................................................. 3.7 1.5 0.5 0.3 0.7 Do Not Use Hot Water.............................................. 1.1 Q Q N Q Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 38.2 20.2 6.7 11.3 Two or More Housing

  14. TableHC14.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4.1 Housing Unit Characteristics by West Census Region, 2005 Total......................................................................... 111.1 24.2 7.6 16.6 Urban/Rural Location (as Self-Reported) City....................................................................... 47.1 12.8 3.2 9.6 Town..................................................................... 19.0 3.0 1.1 1.9 Suburbs................................................................ 22.7 4.9 1.6 3.3

  15. TableHC14.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4.2 7.6 16.6 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 19.5 6.1 13.4 1.......................................................................... 28.6 6.1 1.7 4.4 2.......................................................................... 29.5 6.3 1.8 4.5 3.......................................................................... 14.7 3.1 1.1 2.0

  16. TableHC14.5.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4.2 7.6 16.6 Do Not Have Heating Equpment............................ 1.2 0.7 Q 0.7 Have Space Heating Equpment............................. 109.8 23.4 7.5 16.0 Use Space Heating Equpment.............................. 109.1 22.9 7.4 15.4 Have But Do Not Use Equipment.......................... 0.8 0.6 Q 0.5 Space Heating Usage During 2005 Heated Floorspace (Square Feet) None................................................................. 3.6 2.1 Q 1.9 1 to

  17. TableHC14.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Number of Water Heaters 1............................................................................... 106.3 23.2 7.1 16.1 2 or More.................................................................. 3.7 1.0 0.4 0.6 Do Not Use Hot Water.............................................. 1.1 Q Q N Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 21.9 7.1 14.8 Two or More Housing Units....................................... 10.3 2.3

  18. TableHC15.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    8 Water Heating Characteristics by Four Most Populated States, 2005 Total............................................................................. 111.1 7.1 7.0 8.0 12.1 Number of Water Heaters 1............................................................................... 106.3 6.5 6.9 7.4 11.7 2 or More.................................................................. 3.7 Q Q 0.5 0.4 Do Not Use Hot Water.............................................. 1.1 0.5 N Q N Housing Units Served by

  19. TableHC2.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

  20. TableHC2.11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Million U.S. Housing Units Total................................................................... 111.1 72.1 7.6 7.8 16.7 6.9 Personal Computers Do Not Use a Personal Computer ............... 35.5 17.8 3.1 3.7 7.3 3.6 Use a Personal Computer............................. 75.6 54.2 4.5 4.0 9.4 3.4 Number of Desktop PCs 1.............................................................. 50.3 33.9 3.1 3.0 7.6 2.7 2.............................................................. 16.2 12.7 0.9 0.7 1.4

  1. TableHC2.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

  2. TableHC2.3.xls

    U.S. Energy Information Administration (EIA) Indexed Site

  3. TableHC2.4.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    81.5 72.1 7.6 N N 1.9 For Two Housing Units............................. 18.1 N N 1.4 16.7 N Heat Pump.................................................. 9.2 7.4 0.3 Q 0.7 0.5 Other Equipment......................................... 1.3 0.6 Q Q Q N Fuel Oil........................................................... 7.7 5.5 0.4 0.8 0.9 0.2 Steam or Hot Water System........................ 4.7 2.9 Q 0.7 0.8 N For One Housing Unit.............................. 3.3 2.9 Q Q Q N For Two Housing

  4. TableHC2.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

  5. TableHC2.9.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    9 Home Appliances Characteristics by Type of Housing Unit, 2005 Million U.S. Housing Units Total U.S............................................................ 111.1 72.1 7.6 7.8 16.7 6.9 Cooking Appliances Conventional Ovens Use an Oven............................................... 109.6 71.3 7.4 7.7 16.4 6.8 1.............................................................. 103.3 66.2 7.2 7.4 15.9 6.7 2 or More................................................. 6.2 5.1 Q 0.3 0.5 Q Do Not Use an

  6. TableHC3.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    78.1 64.1 4.2 1.8 2.3 5.7 Census Region and Division Northeast.................................................... 20.6 13.4 10.4 1.4 1.0 0.3 0.4 New England........................................... 5.5 3.8 3.1 Q 0.3 Q Q Middle Atlantic........................................ 15.1 9.6 7.3 1.3 0.6 Q Q Midwest...................................................... 25.6 19.4 16.9 1.0 0.5 0.4 0.7 East North Central.................................. 17.7 13.6 11.7 0.7 0.5 Q 0.3 West North

  7. TableHC3.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    8.1 64.1 4.2 1.8 2.3 5.7 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day......................... 91.8 65.0 54.3 3.3 1.5 1.6 4.4 1........................................................................ 28.6 17.9 14.0 0.9 0.6 0.7 1.7 2........................................................................ 29.5 20.5 17.0 1.1 0.5 0.4 1.5 3........................................................................ 14.7 11.1 9.6 0.6 Q Q 0.6

  8. TableHC3.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    78.1 64.1 4.2 1.8 2.3 5.7 Number of Water Heaters 1.............................................................................. 106.3 74.5 60.9 4.0 1.8 2.2 5.5 2 or More................................................................. 3.7 3.3 3.0 Q Q Q Q Do Not Use Hot Water............................................ 1.1 0.3 Q Q N Q Q Housing Units Served by Main Water Heater One Housing Unit.................................................... 99.7 76.2 63.7 4.1 1.3 1.6 5.6 Two or More

  9. TableHC4.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    33.0 8.0 3.4 5.9 14.4 1.2 Census Region and Division Northeast.................................................... 20.6 7.2 0.8 0.9 1.6 3.8 Q New England........................................... 5.5 1.7 0.2 Q 0.6 0.9 Q Middle Atlantic........................................ 15.1 5.5 0.7 0.9 1.0 2.9 Q Midwest...................................................... 25.6 6.2 1.8 0.5 1.0 2.7 Q East North Central.................................. 17.7 4.2 1.2 0.4 0.7 1.8 Q West North

  10. TableHC4.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    .. 111.1 33.0 8.0 3.4 5.9 14.4 1.2 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day......................... 91.8 26.8 6.7 2.8 4.8 11.7 0.9 1........................................................................ 28.6 10.7 1.9 1.2 2.0 5.2 0.4 2........................................................................ 29.5 9.0 2.4 0.7 1.8 3.7 0.3 3........................................................................ 14.7 3.6 1.1 0.4 0.5 1.5 Q

  11. TableHC4.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    33.0 8.0 3.4 5.9 14.4 1.2 Number of Water Heaters 1.............................................................................. 106.3 31.9 7.9 3.4 5.8 13.7 1.1 2 or More................................................................. 3.7 0.4 Q Q Q Q N Do Not Use Hot Water............................................ 1.1 0.7 Q Q Q 0.6 Q Housing Units Served by Main Water Heater One Housing Unit.................................................... 99.7 23.5 8.0 3.1 4.0 7.3 1.1 Two or More Housing

  12. TableHC5.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    . 111.1 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Census Region and Division Northeast.................................................. 20.6 5.6 1.8 3.3 2.4 2.7 2.3 1.5 0.9 New England......................................... 5.5 1.9 0.4 0.7 0.6 0.6 0.6 0.4 0.3 Middle Atlantic....................................... 15.1 3.8 1.4 2.6 1.9 2.1 1.7 1.1 0.6 Midwest..................................................... 25.6 4.1 2.3 3.2 3.2 4.0 2.8 4.1 1.8 East North Central.................................

  13. TableHC5.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    111.1 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day....................... 91.8 12.0 6.2 10.0 10.3 15.3 15.9 14.5 7.6 1..................................................................... 28.6 3.5 2.1 3.8 3.3 5.2 5.0 3.6 2.2 2..................................................................... 29.5 4.2 2.2 3.5 3.3 4.9 5.0 4.5 2.0 3..................................................................... 14.7

  14. TableHC5.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Number of Water Heaters 1......................................................................... 106.3 14.0 7.2 12.2 12.0 18.4 17.7 16.1 8.8 2 or More............................................................ 3.7 0.6 Q Q 0.3 0.3 0.7 1.1 0.4 Do Not Use Hot Water........................................ 1.1 Q Q Q Q Q Q Q Q Housing Units Served by Main Water Heater One Housing Unit................................................ 99.7 12.7 6.4 11.4 10.8 16.3

  15. TableHC6.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    30.0 34.8 18.4 15.9 12.0 Census Region and Division Northeast.............................................................. 20.6 5.5 6.5 3.4 3.0 2.1 New England..................................................... 5.5 1.7 1.7 1.1 0.7 0.3 Middle Atlantic................................................... 15.1 3.8 4.8 2.4 2.4 1.7 Midwest................................................................. 25.6 7.3 8.4 4.1 3.2 2.5 East North Central............................................. 17.7 5.0

  16. TableHC6.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    111.1 30.0 34.8 18.4 15.9 12.0 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day....................... 91.8 22.9 29.2 15.6 13.8 10.3 1..................................................................... 28.6 10.9 8.4 4.2 3.1 2.1 2..................................................................... 29.5 7.4 10.1 4.4 4.1 3.5 3..................................................................... 14.7 2.5 5.0 2.9 2.6 1.7

  17. TableHC6.6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    6 Air Conditioning Characteristics by Number of Household Members, 2005 Total...................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Cooling Equipment........................ 17.8 5.4 5.3 2.7 2.5 2.0 Have Coolling Equipment.................................... 93.3 24.6 29.6 15.7 13.4 10.0 Use Cooling Equipment...................................... 91.4 24.0 29.1 15.5 13.2 9.7 Have Equipment But Do Not Use it..................... 1.9 0.6

  18. TableHC6.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    8 Water Heating Characteristics by Number of Household Members, 2005 Total....................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Number of Water Heaters 1......................................................................... 106.3 28.8 33.4 17.4 15.3 11.4 2 or More............................................................ 3.7 0.6 1.1 0.8 0.5 0.6 Do Not Use Hot Water........................................ 1.1 0.6 0.3 Q Q Q Housing Units Served

  19. TableHC8.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    HC8.1 Housing Unit Characteristics by Urban/Rural Location, 2005 Total......................................................................... 111.1 47.1 19.0 22.7 22.3 Census Region and Division Northeast.............................................................. 20.6 6.9 6.0 4.4 3.2 New England..................................................... 5.5 2.2 1.9 0.5 0.9 Middle Atlantic................................................... 15.1 4.7 4.2 4.0 2.3

  20. TableHC8.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    7.1 19.0 22.7 22.3 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day........................... 91.8 38.6 15.3 19.5 18.3 1.......................................................................... 28.6 14.3 4.6 4.8 5.0 2.......................................................................... 29.5 12.1 4.9 6.2 6.3 3.......................................................................... 14.7 5.7 2.6 3.4 2.9

  1. TableHC9.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Census Region and Division Northeast............................................................... 20.6 1.9 9.8 8.9 N N New England...................................................... 5.5 1.3 4.1 Q N N Middle Atlantic.................................................... 15.1 Q 5.7 8.8 N N Midwest................................................................. 25.6 6.9 12.3 6.4 N N East North Central.............................................. 17.7 4.9 9.9 3.0 N N West North

  2. TableHC9.13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    111.1 10.9 26.1 27.3 24.0 22.8 Indoor Lights Turned On During Summer Number of Lights Turned On Between 1 and 4 Hours per Day......................... 91.8 8.2 22.3 23.1 19.7 18.4 1........................................................................ 28.6 2.3 7.1 6.7 6.4 6.2 2........................................................................ 29.5 2.6 6.9 7.7 6.6 5.7 3........................................................................ 14.7 1.3 4.1 3.4 2.9 3.0

  3. TableHC9.8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    8 Water Heating Characteristics by Climate Zone, 2005 Million U.S. Housing Units Total........................................................................... 111.1 10.9 26.1 27.3 24.0 22.8 Number of Water Heaters 1.............................................................................. 106.3 10.3 25.2 26.2 23.0 21.7 2 or More................................................................. 3.7 0.4 0.7 0.6 0.9 1.1 Do Not Use Hot Water............................................ 1.1 0.3

  4. table3.3_02.xls

    Gasoline and Diesel Fuel Update (EIA)

    Fuel Consumption, 2002; Level: National and Regional Data; ... Characteristic(a) Total Electricity(b) Fuel Oil Fuel Oil(c) ... of Energy Markets and End Use, Energy Consumption ...

  5. table10.1_021.xls

    Gasoline and Diesel Fuel Update (EIA)

    Consumption Consumption(a) Consumption(b) Factors Total United States RSE Column Factors: 1 1 1 Electricity ... of Energy Markets and End Use, Energy Consumption ...

  6. table4.1_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Offsite-Produced Fuel Consumption, 2002; Level: National ... Breeze RSE NAICS Total Electricity(b) Fuel Oil Fuel Oil(c) ... of Energy Markets and End Use, Energy Consumption ...

  7. table4.3_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Offsite-Produced Fuel Consumption, 2002; Level: National ... Characteristic(a) Total Electricity(b) Fuel Oil Fuel Oil(c) ... of Energy Markets and End Use, Energy Consumption ...

  8. table5.7_02.xls

    Gasoline and Diesel Fuel Update (EIA)

    End Uses of Fuel Consumption, 2002; Level: National and Regional Data; Row: End Uses; Column: Energy Sources, including Net Demand for Electricity; Unit: Physical Units or Btu. ...

  9. table7.7_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... of utilities; for the case of electricity sources, also include small power ... Source: Energy Information Administration, Office of Energy Markets and End Use, Energy Consumption ...

  10. table7.1_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Office of Energy Markets and End Use, Energy Consumption Division, Form EIA-846, '2002 Manufac Energy Consumption Survey.' Total Electricity Diesel Fuel Electricity from Sources ...

  11. table4.2_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Offsite-Produced Fuel Consumption, 2002; Level: National ... and Industry Total Electricity(b) Fuel Oil Fuel Oil(c) ... of Energy Markets and End Use, Energy Consumption ...

  12. table7.2_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Office of Energy Markets and End Use, Energy Consumption Division, Form EIA-846, '2002 Manufac Energy Consumption Survey.' Total Electricity Diesel Fuel Electricity from Sources ...

  13. table2.4_02.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... products (e.g., crude oil converted to residual and distillate fuel oils) are excluded. ... Notes: To obtain the RSE percentage for any table cell, multiply the cell's corresponding ...

  14. table8.2_02.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Notes: To obtain the RSE percentage for any table cell, multiply the cell's corresponding ... Oxy - Fuel Firing Waste Heat Recovery Adjustable - Speed Motors 389 3,468 309 1,507 2,200 ...

  15. table3.4_02.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Notes: To obtain the RSE percentage for any table cell, multiply the cell's corresponding ... were Table 3.4 Number of Establishments by Fuel Consumption, 2002; Level: National Data; ...

  16. table1.5_02.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... It is the total amount of first use of energy for all (fuel and nonfuel) purposes. NFNo ... Notes: To obtain the RSE percentage for any table cell, multiply the cell's corresponding ...

  17. table7.9_02.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Notes: To obtain the RSE percentage for any table cell, multiply the cell's corresponding ... example, LPG and residual and distillate fuel oil) purchased, and associated ...

  18. TableHC15.1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  19. Minerva Assembly Construction Structure.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    layer module type plane type material section Module No. Scint Plane No. Group No. 0 iron 1" steel iron 0 0 veto veto thick scint 0 0 veto veto thick scint 0 1 Target 1 Target Pb...

  20. SSRL_2003_Run_Sched.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    /26/02 Run Shutdown Weekends Maintenance / AP Injector Startup University Holidays PPS Certification Injector / SPEAR Startup SLAC Closed Edited - Robleto, Scott 10 11 12 AP 13 14 12 AP MA/AP 13 14 15 8 9 7 3 L A 11 12 8 9 I S N 30 11 O 12 13 14 18 A I T 31 29 2002 2003 1 2 3 13 4 2002 2003 1 2 3 4 25 26 29 30 28 30 5 6 5 6 8 9 22 16 17 15 16 N 23 24 25 5 17 18 19 Startup 23 24 23 22 21 1 2 3 MA/AP 10 4 5 AP 6 7 8 9 20 22 18 24 24 17 22 23 20 21 14 15 11 16 10 12 9 13 7 8 S T A 1 2 3 15 4 5 5 6

  1. FY2011 2nd QTR.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sandia National Laboratory Brookhaven National Laboratory Portsmouth Gaseous Diffusion Plant Savannah River Site UT BatelleOak Ridge National Laboratory DuratekEnergy Solutions ...

  2. table8.3_02.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    3 1,369 145 3122 Tobacco 78 4 4 72 2 0 76 2 313 Textile Mills 2,247 29 8 2,141 98 W 2,147 W 314 Textile Product Mills 3,457 W 0 2,973 484 W 2,972 W 315 Apparel 5,500 W 0 4,480 ...

  3. table11.3_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total United States RSE Column Factors: 0.9 0.8 1.1 1.3 311 Food 5,622 ... W 0 * 3.7 324 Petroleum and Coal Products 17,503 16,868 0 635 1.3 324110 Petroleum Refineries 16,273 15,709 ...

  4. table2.1_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1 0.7 1.2 ... 0 0 0 0 * 324 Petroleum and Coal Products 3,689 * * * 0 Q * 3,407 324110 Petroleum Refineries 3,307 0 0 0 0 ...

  5. table11.5_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total United States RSE Column Factors: 1 0.9 1 311 Food 708 380 328 31 ... W W 0 1 324 Petroleum and Coal Products 4,123 2,744 1,379 1 324110 Petroleum Refineries 3,148 2,574 574 ...

  6. table2.2_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1 0.7 1.2 ... 0 0 * 0 324 Petroleum and Coal Products 3,689 * 2 * 0 Q 2 3,407 4.4 324110 Petroleum Refineries 3,307 0 0 0 ...

  7. winter_schedule3_2006.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    13,286 15,905 18,499 20,088 7b1 UNDER CONSTRUCTION - 2,058 2,559 3,838 3,838 3,838 3,838 ... Only - - - - - - - - - 7b2 NOT UNDER CONSTRUCTION - - 191 1,671 3,934 4,252 6,580 9,448 ...

  8. summer_schedule3_2006.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    11,372 14,329 16,444 18,491 7b1 UNDER CONSTRUCTION - 1,464 2,377 3,536 3,536 3,536 3,536 ... Only - - - - - - - - - 7b2 NOT UNDER CONSTRUCTION - - - 1,402 3,267 3,900 5,398 7,836 ...

  9. Book2.xls?attach=1

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Units 1&2 Off, Units 3,4,5 on 12 hrs @ 100% load and 12 hrs at 35% load. (100% load 107 MW). SO2 0.22 lbMBtu on all three units. Run 0600 - 1800 at 100% load, and the rest at ...

  10. TableHC10.3.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Income Relative to Poverty Line Below 100 Percent...... 16.6 3.2 3.5 6.5 3.4 100 to 150 Percent......

  11. TableHC2.7.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Table HC7.7 Air-Conditioning Usage Indicators by Household Income, 2005 Below Poverty Line ... Table HC7.7 Air-Conditioning Usage Indicators by Household Income, 2005 Below Poverty Line ...

  12. TableHC2.2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Living Space Characteristics Below Poverty Line Eligible for Federal Assistance 1 Million ... Living Space Characteristics Below Poverty Line Eligible for Federal Assistance 1 Million ...

  13. TableHC7.8.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... Table HC7.8 Water Heating Characteristics by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 Million U.S. Housing Units 80,000 or ...

  14. TableHC7.1.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Housing Units (millions) 2005 Household Income 40,000 to 59,999 Below Poverty Line ... Housing Units (millions) 2005 Household Income 40,000 to 59,999 Below Poverty Line ...

  15. TableHC2.8.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Table HC7.8 Water Heating Characteristics by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 Million U.S. Housing Units 80,000 or ...

  16. TableHC2.12.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Table HC7.12 Home Electronics Usage Indicators by Household Income, 2005 Below Poverty ... Below Poverty Line Eligible for Federal Assistance 1 2005 Household Income Housing Units ...

  17. TableHC15.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent...... 16.6 1.5 1.0 1.5 1.7 100 to 150 Percent......

  18. TableHC2.13.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Table HC7.13 Lighting Usage Indicators by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 Million U.S. Housing Units 2005 ...

  19. TableHC2.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent......2.3 Q Q Q 0.4 1. Below 150 percent of poverty line or 60 percent of median State ...

  20. TableHC2.5.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Space Heating Usage Indicators Below Poverty Line Eligible for Federal Assistance 1 ... Space Heating Usage Indicators Below Poverty Line Eligible for Federal Assistance 1 ...

  1. TableHC2.10.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line ... Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line ...

  2. TableHC7.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent...... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More Table HC7.3 Household ...

  3. TableHC9.3.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Income Relative to Poverty Line Below 100 Percent......weather station. 2. Below 150 percent of poverty line or 60 percent of median State ...

  4. TableHC8.3.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Income Relative to Poverty Line Below 100 Percent...... 16.6 8.9 2.6 1.6 3.5 100 to 150 Percent......

  5. TableHC2.1.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Housing Units (millions) 2005 Household Income 40,000 to 59,999 Below Poverty Line ... Housing Units (millions) 2005 Household Income 40,000 to 59,999 Below Poverty Line ...

  6. TableHC13.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent...... 16.6 6.5 3.2 1.1 2.2 100 to 150 Percent......

  7. TableHC2.6.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More 60,000 to 79,999 ... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More 60,000 to 79,999 ...

  8. TableHC7.13.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... Table HC7.13 Lighting Usage Indicators by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 Million U.S. Housing Units 2005 ...

  9. winter_schedule3_2010.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    meter ge - - - - - - - - - - 2010 US WIN FRCC - 1e Non-Controllable Demand-Side Demand Response - - - - - - - - - - 2010 US WIN FRCC - 2 Total Internal Demand 46,135 47,613 ...

  10. summer_schedule3_2010.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    meter ge - - - - - - - - - - 2010 US SUM FRCC - 1e Non-Controllable Demand-Side Demand Response - - - - - - - - - - 2010 US SUM FRCC - 2 Total Internal Demand 45,722 46,091 ...

  11. 06 Run 6-16-05.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    for Users Accel. Downtime University Holidays Maintenance / AP Accel startup Accel. down Beam line commissiong 10-24-05 (Rev. 6-16-06) User 26 22 20 22 26 24 24 25 13 AP 19 6 MA Conf Dwn 4pm 18 22 21 19 AP AP AP 15 17 13 10 11 15 14 18 19 10 15 12 12 14 8 9 MA 13 AP 7 12 11 10 6 3 9 8 AP 4 MA MA 15 11 AP 7 14 12 5 26 16 16 18 20 22 21 Conf 17 18 19 20 21 13 14 User 16 15 21 22 23 1 1 5 AP 2 14 5 6 13 9 18 14 17 16 15 16 23 24 27 30 27 28 31 Oct Sep 26 BL May Aug Jun Jul Jan Feb Mar Apr 31 25 26

  12. 07-08 Run R3.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    -22-2007 Run Shutdown Maintenance / AP Injector / SPEAR Startup Spear Down University Holidays 23 MA AP AP 27 MA 24 22 24 22 26 20 22 22 24 25 21 18 17 21 15 23 18 3 4 15 14 13 6 8 11 2 5 AP 1 22 21 18 20 19 26 23 13 12 15 14 16 9 13 9 15 10 14 10 8 4 3 MA 5 5 3 7 19 18 21 22 23 24 16 17 10 13 12 11 15 16 14 1 7 5 9 10 7 9 8 10 AP 13 12 11 18 19 20 17 16 AP 27 28 31 17 18 20 31 29 30 Oct Sep 31 30 May Aug Jun Jul Jan Feb Mar Apr 30 20 18 17 2 Line 3 2 Beam Oct Sep Nov Dec 18 22 31 30 21 29 18 19

  13. net_energy_load_2006.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Area Power Pool (MAPP) to Midwest Reliability Organization (MRO). * The MRO, SERC, and SPP regional boundaries were altered as utilities changed reliability organizations. ...

  14. net_energy_load_2005.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Area Power Pool (MAPP) to Midwest Reliability Organization (MRO). * The MRO, SERC, and SPP regional boundaries were altered as utilities changed reliability organizations. ...

  15. Fuel Consumption per Vehicle.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Selected Survey Years (Gallons) Survey Years Household Composition Households With Children... NA NA 609 597 625 665 Age of Oldest Child Under...

  16. QTR4_11 (External).xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Footnotes 1 Does not include mark-to-market adjustments required by derivative accounting guidance as amended or reflect the change in accounting for power "bookout"...

  17. FY2010 3rd QTR.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    NY NY NV OH TN TN TN TN, WA, CA TN TN TN TX Total Shipments by Route Lawrence Livermore National Laboratory Batelle Energy Alliance Idaho National Laboratory Advanced Mixed Waste Treatment Project Energx Argonne National Laboratory Argonne National Laboratory Paducah Gaseous Diffusion Plant Los Alamos National Laboratory Brookhaven National Laboratory West Valley Environmental Services National Security Technologies, Inc. Portsmouth Gaseous Diffusion Plant UT Batelle/Oak Ridge National

  18. FY2010 4th QTR.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    NJ NM NY NV OH TN TN TN TN, WA, CA TN TN TN TX Total Shipments by Route Lawrence Livermore National Laboratory Batelle Energy Alliance Idaho National Laboratory Advanced Mixed Waste Treatment Project Energx Argonne National Laboratory Argonne National Laboratory Paducah Gaseous Diffusion Plant Princeton Plasma Physics Laboratory Sandia National Laboratory Brookhaven National Laboratory Navarro-Interra, LLC Portsmouth Gaseous Diffusion Plant UT Batelle/Oak Ridge National Laboratory Duratek/Energy

  19. FY2011 3rd QTR.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    NM NY NY SC TN TN TN, WA, CA TN TN TN TX Total Shipments by Route Lawrence Livermore National Laboratory Batelle Energy Alliance Idaho National Laboratory Advanced Mixed Waste Treatment Project Energx Argonne National Laboratory Argonne National Laboratory Paducah Gaseous Diffusion Plant Sandia National Laboratory Los Alamos National Laboratory West Valley Environmental Services Brookhaven National Laboratory Savannah River Site Duratek/Energy Solutions Babcox & Wilcox Technical Services

  20. Capital Asset Project List.xls

    Broader source: Energy.gov (indexed) [DOE]

    as defined by DOE Order 413.3B, Program and Project Management for the Acquisition of Capital Assets. CD0 Approve Mission Need CD1 Approve Alternative Selection and Cost Range CD2...

  1. Fig 9-revised Flowchart.xls

    Office of Legacy Management (LM)

    Figure 9 Soil Nature and Extent AOI Identification Process d The PRG value for lead is not a calculated PRG, but rather is taken from the EPA guidance document Revised Interim Soil Lead Guidance for CERCLA Sites and RCRA Correction Action Facilities (1994). e For surface soil (0 to 0.5 ft), WRW surface soil (0 to 0.5 ft) PRGs are used. For subsurface soil (0.5 ft to a maximum depth of 209 ft), WRW subsurface soil (0.5 to 8 ft) PRGs are used. a Soil "superset" for soil samples collected

  2. FY2003 Run Sched.xls

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Robleto, B. Scott 31 29 2002 2003 1 2 3 N 13 4 2002 2003 1 2 3 29 30 31 10 5 6 5 6 7 8 9 22 23 MAAP AP A E 5 17 18 19 10 11 12 9 MAAP 18 Startup 24 23 22 21 16 17 15 1 2 3 15 10...

  3. EM Contractor List-Aug.xls

    Office of Environmental Management (EM)

    Collaborates on Best Practices for Reviews EM Collaborates on Best Practices for Reviews September 30, 2013 - 12:00pm Addthis Members of the Interagency Five-Year Review Workgroup gather for a photo after receiving the Excellence in Partnership award from the EPA's Office of Solid Waste and Emergency Response on Sept. 18. The award recognizes the workgroup's collaborative effort in developing tools for the five-year reviews. DOE members of the workgroup include, front row, fourth from left,

  4. EM Current Project Performance_July.xls

    Office of Environmental Management (EM)

    Energy Convenes Historic Meeting with Leaders of Tribal Nations EM Convenes Historic Meeting with Leaders of Tribal Nations June 20, 2012 - 12:00pm Addthis Senior Advisor for Environmental Management David Huizenga, fifth from left, and EM Office of External Affairs Director Paul Seidler, first from left, stand for a photo with leaders and staff members of the Tribal Nations while on a tour of the Rocky Flats site following the Tribal Leader Dialogue in Denver on Tuesday. Senior Advisor for

  5. monthly_peak_byarea_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    B.1. FRCC Monthly Peak Hour Demand, by North American Electric Reliability Corporation Assesment Area, 1996-2010 Actual, 2011-2012 Projected (Megawatts) FRCC Year January February March April May June July August September October November December 1996 39,860 41,896 32,781 28,609 32,059 33,886 35,444 34,341 34,797 30,037 29,033 34,191 1997 37,127 28,144 27,998 28,458 33,859 34,125 35,356 35,375 33,620 31,798 27,669 31,189 1998 27,122 28,116 29,032 28,008 32,879 37,153 36,576 38,730 34,650

  6. monthly_peak_bymonth_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    A.1. January Monthly Peak Hour Demand, by North American Electric Reliability Corporation Assesment Area, 1996-2010 Actual, 2011-2012 Projected (Megawatts) January NERC Regional Assesment Area 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011E 2012E FRCC 39,860 37,127 27,122 38,581 37,521 40,258 39,675 45,033 35,545 41,247 34,464 38,352 41,705 44,945 53,093 46,839 47,613 NPCC 41,680 41,208 40,009 44,199 45,227 43,553 42,039 45,987 66,215 47,041 43,661 45,002 46,803

  7. net_energy_load_2003.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    3 and Projected 2004 through 2008 (Thousands of Megawatthours and 2003 Base Year) Net Energy For Load (Annual) Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990 2,886,496 442,507 142,502 221,099 197,326 127,102 250,681 485,205 252,037 209,789 558,248 1991 2,941,669 450,586 146,903 228,588 205,880 129,826 253,701 501,794 257,434 211,568 555,389 1992 2,942,910 450,853 147,464

  8. net_energy_load_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4 and Projected 2005 through 2009 (Thousands of Megawatthours and 2004 Base Year) Net Energy For Load (Annual) Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Projected Year Base Year ECAR FRCC MAAC MAIN MAPP/MRO (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990 2,886,496 442,507 142,502 221,099 197,326 127,102 250,681 485,205 252,037 209,789 558,248 1991 2,941,669 450,586 146,903 228,588 205,880 129,826 253,701 501,794 257,434 211,568 555,389 1992 2,942,910 450,853

  9. net_energy_load_2010.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    1. Net Energy For Load, Actual and Projected by North American Electric Reliability Corporation Assessment Area, 1990-2010 Actual, 2011-2015 Projected (Thousands of Megawatthours) Interconnection NERC Regional Assesment Area 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 FRCC 142,502 146,903 147,464 153,468 159,861 169,021 173,377 175,557 188,384 188,598 196,561 200,134 211,116 NPCC 250,681 253,701 252,256 257,447 259,947 261,235 263,125 264,464 268,309 277,902 281,518 282,670

  10. summer_nid_cr_cm_2003.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    t Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 4. Summer Historic and Projected Net Internal Demand, Capacity Resources, and Capacity Margins by North American Electric Reliability Council Region, 1990 (Megawatts and Percent) Projected Year Base Year Summer Contiguous U.S. ECAR FRCC MAAC Net Internal Demand (MW) Capacity Resources (MW) Capacity Margin (percent) Net Internal Demand (MW) Capacity Resources (MW) Capacity Margin (percent) Net Internal Demand (MW)

  11. summer_nid_cr_cm_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 4. Summer Historic and Projected Net Internal Demand, Capacity Resources, and Capacity Margins by North American Electric Reliability Council Regio (Megawatts and Percent) Projected Year Base Year Summer Contiguous U.S. ECAR FRCC MAAC Net Internal Demand (MW) Capacity Resources (MW) Capacity Margin (percent) Net Internal Demand (MW) Capacity Resources (MW) Capacity Margin (percent) Net Internal Demand (MW) Capacity

  12. summer_nid_cr_cm_2005.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    d Form EIA-411 for 2005 Released: February 7, 2008 Next Update: October 2007 Table 4. Summer Historic and Projected Net Internal Demand, Capacity Resources, and Capacity Margins by North American Electric Reliability Council Region, 2005 and 2006 through 2010 (Megawatts and Percent) Projected Year Base Year Summer Eastern Power Grid Contiguous U.S. FRCC MRO NPCC RFC Net Internal Demand (MW) Capacity Resources (MW) Capacity Margin (percent) Net Internal Demand (MW) Capacity Resources (MW)

  13. summer_peak_1990_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    c . Historical Noncoincident Summer Peak Load, Actual by North American Electric Reliability Council Region, 1990 through 2004 (Megawatts) Summer Noncoincident Peak Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Year ECAR FRCC MAAC MAIN MAPP/MRO (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990 546,331 79,258 27,266 42,613 40,740 24,994 44,116 94,677 52,541 42,737 97,389 1991 551,418 81,224 28,818 45,937 41,598 25,498 46,594 95,968 51,885 41,870 92,026 1992 548,707

  14. winter_peak_1990_2004.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    d . Historical Noncoincident Winter Peak Load, Actual by North American Electric Reliability Council Region, 1990 through 2004 (Megawatts) Winter Noncoincident Peak Load Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid Year ECAR FRCC MAAC MAIN MAPP/MRO (U.S.) NPCC (U.S.) SERC SPP ERCOT WECC (U.S.) 1990/1991 484,231 67,097 30,800 36,551 32,461 21,113 40,545 86,648 38,949 35,815 94,252 1991/1992 485,761 71,181 31,153 37,983 33,420 21,432 41,866 88,422 38,759 35,448 86,097

  15. table1.3_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    3 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources and Shipments; Unit: Trillion Btu. Shipments RSE Economic Net Residual Distillate Natural LPG and Coke and of Energy Sources Row Characteristic(a) Total(b) Electricity(c) Fuel Oil Fuel Oil(d) Gas(e) NGL(f) Coal Breeze Other(g) Produced Onsite(h) Factors Total United States RSE Column Factors: 0.8 0.9 1.4 2.7 0.8 0.6 2 1.4 1.1

  16. table11.4_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Electricity: Components of Onsite Generation, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Onsite-Generation Components; Unit: Million Kilowatthours. Renewable Energy (excluding Wood RSE Economic Total Onsite and Row Characteristic(a) Generation Cogeneration(b) Other Biomass)(c) Other(d) Factors Total United States RSE Column Factors: 0.8 0.8 1.1 1.4 Value of Shipments and Receipts (million dollars) Under 20 609 379 W W 25.2 20-49 4,155 4,071 27

  17. table11.6_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    .6 Electricity: Sales to Utility and Nonutility Purchasers, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Utility and Nonutility Purchasers; Unit: Million Kilowatthours. Total of RSE Economic Sales and Utility Nonutility Row Characteristic(a) Transfers Offsite Purchaser(b) Purchaser(c) Factors Total United States RSE Column Factors: 0.9 1.3 0.9 Value of Shipments and Receipts (million dollars) Under 20 251 99 152 11.3 20-49 2,975 372 2,602 1.6

  18. table2.3_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: Trillion Btu. RSE Economic Residual Distillate Natural LPG and Coke and Row Characteristic(a) Total Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal Breeze Other(e) Factors Total United States RSE Column Factors: 1 0.4 6.4 0.6 0.5 1.1 1.7 0.8 Value of Shipments and Receipts (million dollars) Under 20 94 * 6 19 W W W W 9 20-49 135 19 3 8 W W

  19. table7.4_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4 Average Prices of Selected Purchased Energy Sources, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: U.S. Dollars per Physical Units. Residual Distillate Natural LPG and RSE Economic Electricity Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal Row Characteristic(a) (kWh) (gallons) (gallons) (1000 cu ft) (gallons) (short tons) Factors Total United States RSE Column Factors: 0.7 1.2 2.2 0.7 0.5 1.6 Value of Shipments and Receipts

  20. table7.5_02.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Average Prices of Selected Purchased Energy Sources, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: U.S. Dollars per Million Btu. RSE Economic Residual Distillate Natural LPG and Row Characteristic(a) Electricity Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal Factors Total United States RSE Column Factors: 0.7 1.2 2.2 0.7 0.5 1.6 Value of Shipments and Receipts (million dollars) Under 20 19.67 3.98 7.29 4.91 9.79 2.57 11.3 20-49