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Sample records for xls csv graph

  1. CSV's | NISAC

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

    NISACPublicationsCSV's content top CSV's RRReports All_Publications More about tmanzan Written by: tmanzan on August 6, 2013. Last revised by: AdminFebruary 10, 2014. CSV's

  2. 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.

  3. CSV File Documentation: Consumption

    Gasoline and Diesel Fuel Update (EIA)

    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. There are two data files for thermal conversion

  4. Monthly Energy Review - Energy Information Administration

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

    ... PDF (entire section) 3.1 Overview PDF XLS CSV GRAPH INTERACTIVE 3.2 Refinery and blender net inputs and net production PDF XLS CSV GRAPH INTERACTIVE 3.3 Trade: 3.3a Overview ...

  5. 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.

  6. SEDS CSV File Documentation: Price and Expenditure

    Gasoline and Diesel Fuel Update (EIA)

    Price and Expenditure Estimates The State Energy Data System (SEDS) comma-separated value (CSV) files contain the price and expenditure estimates shown in the tables located on the SEDS website. There are three files 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 file, Adjusted Consumption for Expenditure Calculations contains adjusted consumption estimates used in

  7. 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...

  8. Annual Energy Review - Energy Information Administration

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

    ... 5.7 Petroleum net imports by country of origin, 1960- PDF XLS GRAPH 5.8 Refinery and blender net inputs and net production, 1949- PDF XLS CSV INTERACTIVE 5.9 Refinery capacity and ...

  9. 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...

  10. 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.

  11. 3REV2004DOEFAIR.xls | Department of Energy

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

    REV2004DOEFAIR.xls� 3REV2004DOEFAIR.xls� PDF icon 3REV2004DOEFAIR.xls� More Documents & Publications N:\My Documents\porfin.pdf� 2003 DOE IGCA Inventory Data for web.xls� 2002 DOE Final Inherently Governmental and Commercial Activities Inventory

  12. 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.

  13. 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...

  14. hd_hydrogen_2007.xls | Department of Energy

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

    hd_hydrogen_2007.xls hd_hydrogen_2007.xls hd_hydrogen_2007.xls Office spreadsheet icon hd_hydrogen_2007.xls More Documents & Publications Santa Clara Valley Transportation Authority and San Mateo County Transit District -- Fuel Cell Transit Buses: Evaluation Results Joint Fuel Cell Bus Workshop Summary Report Fuel Cell Buses in U.S. Transit Fleets: Summary of Experiences and Current Status

  15. SSL Selections Descriptions v6.xls | Department of Energy

    Energy Savers [EERE]

    SSL Selections Descriptions v6.xls SSL Selections Descriptions v6.xls PDF icon SSL Selections Descriptions v6.xls More Documents & Publications Solid-State Lighting Recovery Act Award Selections 2015 Project Portfolio 2014 Solid-State Lighting Project Portfolio

  16. Project_Descriptions_ITP_ARRA_Awards.xls | Department of Energy

    Energy Savers [EERE]

    Project_Descriptions_ITP_ARRA_Awards.xls Project_Descriptions_ITP_ARRA_Awards.xls PDF icon Project_Descriptions_ITP_ARRA_Awards.xls More Documents & Publications Capturing Waste Gas: Saves Energy, Lower Costs - Case Study, 2013 Combined Heat and Power Market Potential for Opportunity Fuels, August 2004 Combined Heat and Power Webinar

  17. supplemental_lists.xls | Department of Energy

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

    lists.xls More Documents & Publications updated_supplemental_lists_1g-2g-3f_10-6-2011.xlsx updated_supplemental_lists_1n-2n-3m_07-06-2012.xlsx updated_supplemental_lists_1p_2p_3o_04302013

  18. Weekly Petroleum Status Report

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

    CSV XLS PDF 2 U.S. Inputs and Production by PAD District CSV XLS PDF 3 Refiner and Blender Net Production CSV XLS PDF 4 Stocks of Crude Oil by PAD District, and Stocks of ...

  19. combined_supplemental_hud_multifamily_weatherization_list_3-2A.xls |

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

    Department of Energy A.xls More Documents & Publications supplemental_lists_1d-2d-3c_06-24-2011.xls hud_list-2_07-01-11.xls hud_list-2

  20. combined_supplemental_hud_multifamily_weatherization_list_3-2_lihtc.xls |

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

    Department of Energy _lihtc.xls More Documents & Publications list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls rd_mfh_low_and_very_low.xls hud_list-1

  1. hud_doe_supplemental_list_of_eligible_properties_list_1.xls | Department of

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

    Energy 1.xls More Documents & Publications hud_doe_supplemental_list_of_eligible_properties_list_1.xls rd_mfh_low_and_very_low

  2. U.S. Energy Information Administration (EIA) - Data

    Gasoline and Diesel Fuel Update (EIA)

    New Addition: Use EIA's new interactive Coal Data Browser to find, graph, and map coal data. Data sets include production, imports and exports, shipments to electricity plants, and individual mine-level data. Maps include world trade, national, basin, state, and supplier networks. Find statistics on coal production, consumption, exports, imports, stocks, mining, and prices. + EXPAND ALL Summary Additional formats Coal overview: PDF CSV XLS INTERACTIVEMonthly PDF XLS Annual Coke overview PDF XLS

  3. 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

  4. rd_mfh_low_and_very_low.xls | Department of Energy

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

    Office spreadsheet icon rd_mfh_low_and_very_low.xls More Documents & Publications list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls hud_list-1_07-01-11.xls hud_list-1_07-01-11.xls

  5. hud_list-2_07-01-11.xls | Department of Energy

    Energy Savers [EERE]

    hud_list-2_07-01-11.xls hud_list-2_07-01-11.xls Office spreadsheet icon hud_list-2_07-01-11.xls More Documents & Publications hud_list-2_07-01-11.xls supplemental_lists_1d-2d-3c_06-24-2011.xls updated_supplemental_lists_1m-2m-3l-04-05-2012

  6. Methods of visualizing graphs

    DOE Patents [OSTI]

    Wong, Pak C. (Richland, WA); Mackey, Patrick S. (Kennewick, WA); Perrine, Kenneth A. (Richland, WA); Foote, Harlan P. (Richland, WA); Thomas, James J. (Richland, WA)

    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. 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.

  8. hud_doe_supplemental_list_of_eligible_properties_list_2.xls | Department of

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

    Energy 2.xls More Documents & Publications hud_doe_supplemental_list_of_eligible_properties_list_2.xls list2_eligible_multifamily_buildings_10-cfr-440-22b4ii

  9. 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

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

    SciTech Connect (OSTI)

    Kepner, Jeremy; Bader, David; Bulu, Ayd?n; 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. FINAL Combined SGIG Selections - By State for Press -5.xls | Department of

    Office of Environmental Management (EM)

    Energy FINAL Combined SGIG Selections - By State for Press -5.xls FINAL Combined SGIG Selections - By State for Press -5.xls PDF icon FINAL Combined SGIG Selections - By State for Press -5.xls 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

  12. 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� 2003 DOE IGCA Inventory Data for web.xls� PDF icon 2003 DOE IGCA Inventory Data for web.xls� More Documents & Publications 3REV2004DOEFAIR.xls� N:\My Documents\porfin.pdf� 2002 DOE Final Inherently Governmental and Commercial Activities Inventory

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

    Office of Environmental Management (EM)

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

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

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

    icon hud_list-1_07-01-11.xls More Documents & Publications hud_list-1_07-01-11.xls list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls rd_mfh_low_and_very_low

  15. list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls | Department of

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

    Energy Office spreadsheet icon list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls More Documents & Publications hud_list-1_07-01-11.xls hud_list-1_07-01-11.xls rd_mfh_low_and_very_low.xls

  16. 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.

  17. FY 2007 Operating Plan for DOE--March 16, 2007.xls | Department of Energy

    Office of Environmental Management (EM)

    7 Operating Plan for DOE--March 16, 2007.xls 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 Documents & Publications FY 2007 Operating Plan for DOE--March 16, 2007 The FY 2006 Budget Request The FY 2005 Budget Request

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

    Office of Environmental Management (EM)

    Energy Copy of FINAL SG Demo Project List 11 13 09-External.xls Copy of FINAL SG Demo Project List 11 13 09-External.xls PDF icon Copy 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 in the United States Electricity Grid. May 2011 Fact Sheet: Grid-Scale Energy Storage Demonstration Using UltraBattery Technology (August 2013)

  19. 2011 Cost Symposium Agenda 4-28-11 web draft.xls | Department of Energy

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

    1 Cost Symposium Agenda 4-28-11 web draft.xls 2011 Cost Symposium Agenda 4-28-11 web draft.xls PDF icon 2011 Cost Symposium Agenda 4-28-11 web draft.xls More Documents & Publications 2011 Cost Symposium Agenda for web (2)-OPAM 2011 Workshop Agenda_Ver_9.xlsx 2011_Workshop_Agenda_Ver_16(1).pdf

  20. 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 -...

  1. combined_supplemental_hud_multifamily_weatherization_list_1b.xls |

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

    Department of Energy 1b.xls More Documents & Publications combined_supplemental_hud_multifamily_weatherization_list_2b

  2. combined_supplemental_hud_multifamily_weatherization_list_2b.xls |

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

    Department of Energy 2b.xls More Documents & Publications combined_supplemental_hud_multifamily_weatherization_list_1b

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. updated_supplemental_lists_1e-2e_20110803.xls | Department of Energy

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

    icon updated_supplemental_lists_1e-2e_20110803.xls More Documents & Publications supplemental_lists.xls updated_supplemental_lists_1n-2n-3m_07-06-2012.xlsx updated_supplemental_lists_1i-2i-3h_12-06-2011.xlsx

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

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

    lists_1d-2d-3c_06-24-2011.xls More Documents & Publications hud_list-2_07-01-11.xls hud_list-2_07-01-11.xls updated_supplemental_lists_1g-2g-3f_10-6-2011

  9. 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.

  10. 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%.

  11. 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

  12. 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.

  13. 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.

  14. Graph modeling systems and methods

    DOE Patents [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.

  15. 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

  16. csv.cfm

    Gasoline and Diesel Fuel Update (EIA)

  17. 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 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

  18. 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

  19. 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.

  20. 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

  1. update4_supplemental_lists_1c_2c_2c3b_041411updated_051711.xls | Department

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

    of Energy icon update4_supplemental_lists_1c_2c_2c3b_041411updated_051711.xls More Documents & Publications updated_supplemental_lists_1h-2h-3g- 11-4-2011.xlsx updated_supplemental_lists_1j-2j-3i_12-22-2011.xlsx list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls

  2. 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.

  3. 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.

  4. 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 ...

  5. 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.

  6. 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

  7. 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.

  8. 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...

  9. U.S. Energy Information Administration (EIA) - Data

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

    storage withdrawals. PDF CSV XLS Monthly (more on natural gas) PDF XLSAnnual (more on natural gas) Natural gas statistics by country Query detailed country and regional energy ...

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. bbnp_bb_an_0003562_pmc_dashboard_y13-q3_0.xls

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

    7-16T12:28:14Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  16. bbnp_bb_an_0003566_pmc_dashboard_y13-q3.xls

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

    4:32Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  17. bbnp_bb_an_0003569_pmc_dashboard_y13-q3.xls

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

    8-05T11:35:03Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  18. bbnp_bb_an_0003572_pmc_dashboard_y13-q3.xls

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

    6T16:37:53Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  19. bbnp_bb_an_0003576_pmc_dashboard_y13-q3.xls

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

    7T21:46:17Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  20. bbnp_bb_an_0003578_pmc_dashboard_y13-q3.xls

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

    7T21:49:33Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  1. bbnp_bb_an_0003580_pmc_dashboard_y13-q3.xls

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

    13T12:12:14Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  2. bbnp_bb_an_0003809_pmc_dashboard_y13-q3.xls

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

    6T16:37:28Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  3. bbnp_bb_an_0004442_pmc_dashboard_y13-q3.xls

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

    6:01Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure Graph

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. connecticut_bb_an_0003806_pmc_dashboard_y13-q3.xls

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

    4.0300  docProps/core.xml Dale HoffmeyerAayush Daftari2013-05-10T14:58:05Z2014-09-09T18:12:11Z  docProps/  xl/_rels/workbook.xml.rels  xl/_rels/  xl/calcChain.xml  xl/charts/chart1.xml Better Buildings Neighborhood Program Grantee ExpendituresCumulative Data - Award Start to September 30, 2013 'Expenditure Graph Data'!$D$4BBNP Award Spending'Expenditure Graph Data'!$B$5:$B$162010-Q42011-Q12011-Q22011-Q32011-Q42012-Q12012-Q22012-Q32012-Q42013-Q12013-Q22013-Q3'Expenditure

  14. 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.

  15. 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.

  16. 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.'

  17. 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.

  18. 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.

  19. 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.

  20. 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...

  1. 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...

  2. 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

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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).

  9. Gasoline and Diesel Fuel Update (EIA)

    Review revision February 25, 2016 Table 6.1, "Coal Overview," .xls and .csv files have been revised from "NA" (not available) to numeric values for coal imports, exports, and net imports for December 2015. The .pdf, .xls, and .csv files have been revised from "NA" (not available) to numeric values for coal imports, exports, and net imports for the 2015 annual total.

  10. 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.

  11. 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).

  12. 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.

  13. Sequoia supercomputer tops Graph 500 | National Nuclear Security

    National Nuclear Security Administration (NNSA)

    Administration Sequoia supercomputer tops Graph 500 | National Nuclear Security Administration Facebook Twitter Youtube Flickr RSS People Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Countering Nuclear Terrorism About Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Library Bios Congressional Testimony Fact Sheets Newsletters Press Releases Photo Gallery Jobs Apply

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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 ......

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  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. 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...

  11. 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...

  12. eia-857.xls

    Gasoline and Diesel Fuel Update (EIA)

    8 5 7 ATTN: EIA-857 Address 2: City: State: Zip: - Total operational sendout to consumers of gas owned and not owned Residential Industrial Electric Power Other (not included in above categories) Residential Commercial (excluding vehicle fuel) Vehicle Fuel Industrial Electric Power Other (not included in above categories) Total of all deliveries (Lines 3.0 through 12.0) Does any information provided in lines 1-13 include prior period adjustments? Heat content of gas delivered to consumers

  13. eia-910.xls

    Gasoline and Diesel Fuel Update (EIA)

    9 1 0 Address 2: City: State: Zip: - 1. Report State (Enter one of the following States in the box): Georgia, New York, 2. To how many end-use customers did you sell natural gas? 3. 4. For companies reporting sales in all States except Georgia: 5. For companies reporting sales in Georgia: PART 2. SUBMISSION INFORMATION (Dollars) Do not report negative numbers or decimals. You may report in either Thousand cubic feet (Mcf) or in Therms. Indicate unit of measure by placing an "X" in the

  14. Table 2.xls

    Gasoline and Diesel Fuel Update (EIA)

    Project-level Reductions and Sequestration Reported, Data Year 2005 (Metric Tons Carbon Dioxide Equivalent) 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Indirect 1 85 621 699 3,129 3,411 4,120 3,850 5,988 4,211 6,193 4,890 4,102 6,243 Sequestration 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 1,540,000 Direct 16 Indirect 16,191 14,656 17,745 17,748 17,859 19,897 18,925 21,070 85,711

  15. Table 4.xls

    Gasoline and Diesel Fuel Update (EIA)

    Emission Reductions and Sequestration Reported at Project and Entity Levels, Data Year 2005 (Metric Tons Carbon Dioxide Equivalent) Report Name Sector Reduction Type Project Level Entity Level A&N Electric Cooperative Electric Providers Indirect 6,243 AES Hawaii, Inc. Electric Providers Sequestration 1,540,000 1,540,000 AES SeaWest, Inc. Electric Providers Direct 16 Indirect 220,420 AES Shady Point, LLC Electric Providers Sequestration 4,150,000 4,150,000 AES Thames, LLC Electric Providers

  16. Table1.xls

    Gasoline and Diesel Fuel Update (EIA)

    Reporting Entities, Data Year 2005 Reporter Name Sector Type of Form Number of Projects Reported (Schedule II) Entity-Wide Report (Schedule III) Commitments (Schedule IV) A&N Electric Cooperative Electric Providers 1605 2 No Yes Abe Krasne Home Furnishings, Inc. Services and Retail 1605 0 Yes No AES Hawaii, Inc. Electric Providers 1605 1 Yes No AES SeaWest, Inc. Electric Providers 1605 11 No No AES Shady Point, LLC Electric Providers 1605 1 Yes No AES Thames, LLC Electric Providers 1605 1

  17. J319.xls

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

    NA indicates no constriants have been found based on the scope of the feasilbity screening. Page 1 of 3 February 10, 2014 MISO Project Number J319 Point of Interconnection Holland ...

  18. 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...

  19. 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 ......

  20. 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...

  1. EWA Summary.xls

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

    Blake Weiss ph: 571-419-6423 bweiss@emergent360.com YES Adobe: Mina Pham ph: 571-765-5485 minpham@adobe.com Carrie Whalen 571.765.5371 (tel) whalen@adobe.com Rob Gettings ...

  2. 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...

  3. natgas1980.xls

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

    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

  4. 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

  5. Fig1.xls

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

    December 2009 1 December 2009 Short-Term Energy Outlook December 8, 2009 Release Highlights  EIA expects the price of West Texas Intermediate (WTI) crude oil will average about $76 per barrel this winter (October-March). The forecast for the monthly average WTI price dips to $75 early next year then rises to $82 per barrel by December 2010, assuming U.S. and world economic conditions continue to improve. EIA's forecast assumes that U.S. real gross domestic product (GDP) grows by 1.9 percent

  6. Fig1.xls

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

    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

  7. b38.xls

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

    ... ...... 3,625 3,469 469 1,581 685 53 485 932 154 Buildings with Water Heating ...... 3,472 3,337 397 1,585 657 59 546 828 172 Buildings with Cooking ...

  8. a1.xls

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

    Number of Buildings RSEs for Total Floorspace RSEs for Mean Square Feet per Building RSEs Not Available for Medians All Buildings .................................... 3.8 3.1 4.0 _ Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 5.7 5.6 1.3 _ 5,001 to 10,000 ................................. 5.6 5.5 0.8 _ 10,001 to 25,000 ............................... 4.9 4.9 0.9 _ 25,001 to 50,000 ............................... 5.5 5.8 1.2 _ 50,001 to 100,000

  9. 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

  10. 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

  11. 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

  12. 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*

  13. 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*

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. b22.xls

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

    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 than 5

  1. 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 .............................

  2. 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

  3. b28.xls

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

    4,645 3,982 1,258 1,999 282 63 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,100 699 955 171 Q 5,001 to 10,000 ................................. 889 782 233 409 58 Q 10,001 to 25,000 ............................... 738 659 211 372 32 Q 25,001 to 50,000 ............................... 241 225 63 140 8 9 50,001 to 100,000 ............................. 129 123 32 73 6 8 100,001 to 200,000 ........................... 65 62 15 33 Q 9 200,001 to 500,000

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. b34.xls

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

    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 (thousand) Total

  10. 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

  11. b37.xls

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

    Floor- space a Heated Floor- space b Total Floor- space a Cooled Floor- space b Total Floor- space a Lit Floor- space b All Buildings* .................................. 64,783 60,028 53,473 56,940 41,788 62,060 51,342 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,668 4,988 5,007 4,017 6,038 4,826 5,001 to 10,000 ................................. 6,585 5,786 5,010 5,408 3,978 6,090 4,974 10,001 to 25,000 ............................... 11,535 10,387

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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)

  19. 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*

  20. 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

  1. 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

  2. 2010 APS.xls

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

    ... H- Canyon for vitrification in the Defense Waste Processing Facility, disposition using the Mixed Oxide Fuel Fabrication Facility, a can-in-canister vitrification project, and ...

  3. b45.xls

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

    297 296 283 239 90 219 118 53 Health Care ............ 195 131 25 Q Q 18 124 19 Health Care Complex ...... 39 ...

  4. 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 ...

  5. b41.xls

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

    1,654 1,583 468 217 234 Q Q 988 Q Q Health Care ......2,347 2,089 806 340 487 Q Q 1,267 Q Q Health Care Complex ......

  6. 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 ...

  7. b39.xls

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

    1,654 1,608 168 675 186 Q 328 591 Q Health Care ......2,347 2,245 320 731 302 Q 988 406 Q Health Care Complex ......

  8. b20.xls

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

    1,654 1,375 246 Q N N N Health Care ......Complex ...... 2,347 2,027 Q Q Q N Q Health Care Complex ......

  9. 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 ...

  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. 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 ...

  12. b16.xls

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

    Q Q Q N Food Service ...... 1,654 336 413 421 367 Q Q N Health Care ...... 3,163 Q 254 Q 335 111 683 1,539 Inpatient ...

  13. b21.xls

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

    297 27 Q 1,654 264 Q Health Care ............ 195 195 Q 2,347 2,347 Q Health Care Complex ...... 39 ...

  14. b7.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 ...

  15. b12.xls

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

    ... Education Food Sales Food Service Health Care Total Floorspace (million square feet) All ... Education Food Sales Food Service Health Care Total Floorspace (million square feet) All ...

  16. b46.xls

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

    1,654 1,651 1,616 1,495 491 1,234 605 426 Health Care ......2,347 1,940 870 Q Q 320 1,743 763 Health Care Complex ......

  17. a8.xls

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

    Q N N Q N Food Service ...... 1,654 1,375 246 Q N N N Health Care ...... 3,163 2,004 735 Q Q Q N Inpatient ...

  18. 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 ...

  19. wf01.xls

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

    WF01.Selected U.S. Average Consumer Prices* and Expenditures for Heating Fuels During the Winter (Energy Information AdministrationShort-Term Energy Outlook -- October 2005) Fuel...

  20. a2.xls

    Gasoline and Diesel Fuel Update (EIA)

    1,396 Currently Unoccupied ... 157 Q 47 84 Q 2,161 Q 892 652 Q See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  1. a4.xls

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

    Q 1,023 Currently Unoccupied ... 2,161 Q Q 756 Q 241 Q 346 Q Q See "Guide to the Tables" or "Glossary" for further explanations of the terms used in this table....

  2. b36.xls

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

    Food Service ... 297 Q 43 61 192 1,654 Q 276 362 1,004 Health Care ... 129 N Q 45 72 3,163 N Q 1,230 1,841...

  3. b18.xls

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

    ... Water-Heating Energy Sources (more than one may apply) Electricity ...... 27,490 22,196 11,450 10,431 Q 5,294 465 1,015 3,814 Natural Gas ...

  4. b1.xls

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

    ... Water-Heating Energy Sources (more than one may apply) Electricity ...... 1,910 27,490 32,638 14.4 842 59 Natural Gas ......

  5. b27.xls

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

    ... Space-Heating Energy Sources Used (more than one may apply) All Buildings* Buildings with Space Heating Water-Heating Energy Sources (more than one may apply) Electricity ...

  6. b17.xls

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

    ... Water-Heating Energy Sources (more than one may apply) Electricity ...... 1,910 1,657 847 796 Q 253 20 61 172 Natural Gas ......

  7. b33.xls

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

    ... Water-Heating Energy Sources (more than one may apply) Electricity ...... 1,910 316 219 116 40 27,490 8,295 6,222 4,537 398 Natural Gas ...

  8. b22.xls

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

    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 ...

  9. b26.xls

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

    ... Space-Heating Energy Sources Used (more than one may apply) All Buildings* Buildings with Space Heating Water-Heating Energy Sources (more than one may apply) Electricity ...

  10. b32.xls

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

    ... Table B32. Water-Heating Energy Sources, Floorspace for Non-Mall Buildings, 2003 Total Floorspace (million square feet) Water-Heating Energy Sources Used (more than one may apply) ...

  11. EIA-912.xls

    Gasoline and Diesel Fuel Update (EIA)

    Month 2 0 Address 2: City: State: Zip: - to meet the due date. Report volumes in million cubic feet (MMcf) @14.73 psia-60⁰ F.) No East Region (Million Cubic Feet) South Central Region (Million Cubic Feet) Midwest Region (Million Cubic Feet) Mountain Region (Million Cubic Feet) (AZ, CO, ID, MT, NE, ND, NM, NV, SD, UT, & WY) Midwest Region (Million Cubic Feet) (IL, IN, IA, KY, MI, MN, MO, TN, & WI) South Central Region (Million Cubic Feet) (AL, AR, KS, LA, MS, OK, & TX) Mountain

  12. oil1980.xls

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

    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...

  13. 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. ...

  14. 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 ...

  15. oil2001.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 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 ...

  16. 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. ...

  17. 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 ...

  18. oil1982.xls

    Annual Energy Outlook [U.S. Energy Information Administration (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 ...

  19. oil1990.xls

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

    ... 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 ...

  20. oil1987.xls

    Annual Energy Outlook [U.S. Energy Information Administration (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 ...

  1. 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 ...

  2. 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 ...

  3. AWGagenda_033009.xls

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

    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 1:30 Kassianov...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. b10.xls

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

    ... 2,044 1,181 550 221 87 6 32,817 11,171 8,286 4,955 6,221 2,183 Concrete (Block or Poured) ... 786 586 153 29 15 2 10,832 4,865 3,076 885 1,399...

  10. 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...

  11. June2010.XLS

    Office of Environmental Management (EM)

    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,

  12. RangeTables.xls

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

    (MeVcmmg) 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...

  13. All Beams 2013.xls

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

    Mass (amu) A MeV Total Energy (MeV) Energy at Bragg Peak (MeV) Range in Si (m) Range at Bragg (m) Range to Bragg Peak (m) Initial LET (vacuum) Initial LET (air) LET at...

  14. b23.xls

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

    ... 599 Activities with Large Amounts of Hot Water ...... 19,482 19,482 ... square feet. a "Other" includes wood, coal, solar, and all other energy sources. ...

  15. recommendations.xls

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

    6, 2003 Electric System Working Group Technical Conference, Philadelphia PA Rec Type Recommendations/Comments Name Organization Communication The reliability coordinator needs an understanding from others, from a broad perspective, what's going on. Sometimes you may not have all the information, and this is what happens most times in blackout situations. Michael Calimano New York ISO System Operations Reliability coordination needs to have authority in real time to order actions to be taken by

  16. web_comments.xls

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

    Date Rec Type Recommendations/ Comments Name Organization 1/9/2004 Reliability Standards Future reliability standards must strike a balance between detailed, rigid requirements, which provide little or no latitude for deviation, and standards, which are objective-based and allow for innovation and invention to achieve intended goals. Each standard should identify its importance on the BPS reliability in terms of the potential short-term (operating time horizon) vs. long-term (planning time

  17. 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

  18. 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.

  19. 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.

  20. 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 onmorea 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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 35 45 45 45 84 Kr 50 40 30 30 35 25* 25 30 45 45 45 109 Ag 50 40 30 30* 35 15 15 30 45 45 45 129 Xe 50 40 30 30 35 25 25* 30 45 45 45 141 Pr 50 40 25 20* 35 25 25 25 45 45 45 165 Ho 60 50 45 45 45 45 45 45 45 30 30 181 Ta 60 50 45 45 45 45 45 45 45 30 20 197 Au 60 55 50 50 50 50 50 50 50 30 20 He N Ne Ar Cu Kr Ag Xe

  13. 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...

  14. 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 ......

  15. Attachment A -- Deliverables.xls

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

    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 funds (DOE action). Modify Contract As Required L S B B.3 Modify Table B-1 as Required by Equitable Adjustments (DOE Action). Modify Contract As Required S L S C C.2.1.1-1 Transition Plan Review/Approve 10 Days after NTP L S S C C.2.1.1-2 Statement of Material Differences Review/Approve/Mod 60 Days after NTP L S S S

  16. 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

  17. eia-191_Nov2014.xls

    Gasoline and Diesel Fuel Update (EIA)

    ATTN: EIA-191 Ben Franklin Station Address 2: City: State: Zip: - Storage Field Name Reservoir Name Location State Location County Total Storage Field Capacity (Mcf) Maximum Deliverability (Mcf/day) Base Gas Working Gas Injections Withdrawals Total Gas in Storage (sum of base gas + working gas) Inactive PART 4. MONTHLY GAS STORAGE as of 9:00 a.m. on the last day of report month (Report all volumes in Thousand Cubic Feet (Mcf) @14.73 psia - 60 o Fahrenheit) Inactive explain below in Comments.

  18. eia-757_b.xls

    Gasoline and Diesel Fuel Update (EIA)

    Fax: (202) 586-1076 If any Plant Identification Data has changed since the last report, Secure File Transfer: Plant Name: Questions? 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: Contact Name: Contact Name: Title: Title: Company: Company: Secondary Phone No.: Secondary Phone No.: Page 1 Comments: (To separate one comment from another, press ALT+ENTER) Ext: Ext:

  19. 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 ......

  20. Uranium calculations.xls.xml

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

    (ionssec 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. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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"...

  9. 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...

  10. 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...

  11. tablehc1.3.xls

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

    6.1 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 Use Main Space Heating Equipment........... 109.1 6.1 27.7 26.0 17.6 10.0 7 7.8 11.6 Have Equipment But Do Not Use it.............. 0.8 N N N N N N N Main Space Heating Usage During 2005 Total Number of Rooms (Excluding Bathrooms) None........................................................ 2.1 N Q Q N N N N 1 or

  12. tablehc15.13.xls

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

    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

  13. FY14 - Qtr1.xls

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

    1 DISPOSAL VOLUME REPORT Run Date and Time: 1/6/2014 8:42 AM DOE APPROVED Disposal Volume Volume Volume Volume Volume Volume GENERATORS Location (Ft 3 ) (M 3 ) (Ft 3 ) (M 3 ) (Ft 3 ) (M 3 ) Area 3 0.00 0.00 0.00 0.00 0 0 Area 5 0.00 0.00 1,094.75 31.00 90,629 2,566 Mixed 0.00 0.00 0.00 0.00 0 0 Area 3 0.00 0.00 0.00 0.00 159,977 4,530 Area 5 0.00 0.00 35,122.20 994.55 1,075,034 30,442 Mixed 0.00 0.00 0.00 0.00 3,940 112 Area 3 0.00 0.00 0.00 0.00 0 0 Area 5 0.00 0.00 0.00 0.00 0 0 Mixed 0.00

  14. FY16 Projects.xls

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

    PI/ Univ PI Title University /Internal H. Li/Dean Linn Close-in Exoplanets: Origin and Dynamics of Hot Jupiters and Super-Earths UCSC M. Paris/Fuller Towards a Unitary & Self-consistent Treatment of Big Bang Nucleosynthesis UCSD Stamatikos/Fryer Spectral Variation Studies of Gamma-ray Bursts OH Univ J Smidt/A Cooray Primordial Explosions and BlackHoles: Direct and indrect Signatures in deep Sky Image (gave only what was requested) UC Irvin Hui Li/Shengtai Li Planet Formation in the ALMA Era:

  15. 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.

  16. 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...

  17. 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...

  18. 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...

  19. winter_peak_2006.xls

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

    b . Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base ...

  20. 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....

  1. 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....

  2. 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....

  3. 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....

  4. 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....

  5. 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....

  6. 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....

  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. 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...

  9. 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...

  10. 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 ......

  11. 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...

  12. 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 ......

  13. 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...

  14. 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 ......

  15. 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...

  16. schedule6_2006.xls

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

    ... U SERC Southern AC 230 230 602 6-2010 Jim Moore Road Sharon Church 11 - Overh single pole ... 230 602 6-2010 Old Freeman Mill Road Jim Moore Road 4 - Overh single pole concrete 1351 ...

  17. schedule6_2002.xls

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

    ... 145 OH W W 795 ACSR 2 1 1 17718 I 100 U SPP AC 230 230 558 Jun-05 Pringle Intg Moore Co. ... C 100 U SPP AC 345 345 956 Jun-10 JEC Moore 115 OH W W 1192 ACSR 2 1 1 10000 I 50 U ...

  18. 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...

  19. 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)

  20. monthly_peak_2004.xls

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

    Table 3a . January Monthly Peak Hour Demand, Actual and Projected by North American Electric Reliability Council Region, 1996 through 2004 and Projected 2005 through 2006 (Megawatts and 2004 Base Year) Projected Monthly Base Year Contiguous U.S. Eastern Power Grid Texas Power Grid Western Power Grid ECAR FRCC MAAC MAIN MAPP/MRO NPCC 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

  1. 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

  2. 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

  3. 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

  4. schedule6_2001.xls

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

    1, by North American Electric Reliability Council, 2002 Through 2011 (Various) Geographic Area Voltage Capacity Rating (MVa) In- Service Date Electrical Connection Locations Line Information Conductor Characteristics Circuits Company Information Country NERC Region NERC Sub-region Type Operating (kV) Design (kV) From Terminal To Terminal Length (Miles) Type Pole Type Pole Material Size (MCM) Material Bundling Arrangement Present Ultimate Company Code Organizational Type Ownership (Percent) U

  5. schedule6_2003.xls

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

    3, by North American Electric Reliability Council, 2003 Through 2008 (Various) Geographic Area Voltage Capacity Rating (MVa) In- Service Date Electrical Connection Locations Line Information Conductor Characteristics Circuits Company Information Country NERC Region NERC Sub-region Type Operating (kV) Design (kV) From Terminal To Terminal Length (Miles) Type Pole Type Pole Material Size (MCM) Material Bundling Arrangement Present Ultimate Company Code Organizational Type Ownership (Percent) U

  6. schedule6_2004.xls

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

    e Form EIA-411 for 2005 Released: February 07, 2008 Next Update: October 2008 Table 6. Existing and Proposed High-voltage Transmission Line Additions Filed For Calendar Year 2004, by North American Electric Reliability Council, 2004 Through 2009 (Various) Geographic Area Voltage Capacity Rating (MVa) In-Service Date Electrical Connection Locations Line Information Conductor Characteristics Circuits Company Information Country NERC Region NERC Sub-region Type Operating (kV) Design (kV) From

  7. schedule6_2005.xls

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

    n r r r r r r r r r r r r r r r Form EIA-411 for 2005 Released: February 07, 2008 Next Update: October 2008 Table 6. Existing and Proposed High-voltage Transmission Line Additions Filed Covering Calendar Year 2005, by North American Electric Reliability Council, 2006 Through 2011 (Various) Geographic Area Voltage Capacity Rating (MVa) In-Service Date Electrical Connection Locations Line Information Conductor Characteristics Circuits Company Information Country - with Total (T) for sub- regions

  8. schedule6_2010.xls

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

    PlannedCircuitMiles Electric Power Annual 2010 Released: December 2011 Next Update: November 2012 (Various) Capacity Conductor Characteristics Data Year Country NERC Region NERC Sub- region Type Operatin g (kV) Design (kV) Rating (MVa) Month Year From Terminal To Terminal Length (Miles) Type Pole Type Pole Material Size (MCM) Material Bundling Arrange ment Present Ultimate Company Code Company Name Organizatio nal Type Ownership (Percent) Project Name Level of Certainty Primary Driver 1 Primary

  9. 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

  10. summer_peak_2003.xls

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

    3 and Projected 2004 through 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 MAIN MAPP (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 78,550 30,601 43,658 38,819 22,638 43,658 97,635 51,324 42,619

  11. summer_peak_2004.xls

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

    4 and Projected 2005 through 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 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 78,550 30,601 43,658 38,819 22,638 43,658 97,635 51,324

  12. summer_peak_2005.xls

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

    a . Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Council Region, 2005 and Projected 2006 through 2010 (Megawatts and 2005 Base Year) Summer Noncoincident Peak 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 758,876 46,396 39,918 58,960 190,200 190,705 41,727 60,210 130,760 Projected Contiguous U.S. FRCC MRO (U.S.) NPCC (U.S.) RFC SERC SPP

  13. summer_peak_2006.xls

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

    a . Noncoincident Summer Peak Load, Actual and Projected by North American Electric Reliability Corporation Region, 2006 and Projected 2007 through 2011 (Megawatts and 2006 Base Year) Summer Noncoincident Peak 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.) 2006 789,475 45,751 42,194 63,241 191,920 199,052 42,882 62,339 142,096 Projected Contiguous U.S. FRCC MRO (U.S.) NPCC (U.S.) RFC SERC

  14. 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

  15. 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

  16. tablehc4.3.xls

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

    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 ...

  17. tablehc6.3.xls

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

    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 ...

  18. tablehc3.3.xls

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

    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 ...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. EIA895_update.xls

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

    Phone No.: - - Ext: - Address 1: Email: Address 2: Fax: City: State: Zip: - https:signon.eia.doe.govuploadnoticeoog.jsp (8) ANNUAL QUANTITY AND VALUE OF NATURAL GAS PRODUCTION ...

  4. 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...

  5. 06 Run R1.xls

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

    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...

  6. 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...

  7. 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)...

  8. 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...

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

    SciTech Connect (OSTI)

    Chavarra-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.

  10. 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.

  11. Contig Graph Tool: A graphical interface for Contig Physical Map assembly

    SciTech Connect (OSTI)

    Pecherer, R.M.

    1992-01-01

    A Contig Physical Map of a chromosome is a collection of DNA clones organized into ordered, overlapping sets called contigs which cover contiguous regions of the chromosome. Contigs may be assembled from a knowledge of the binary overlap relation between all clone pairs in a clone set which covers all or part of the chromosome, and contigs may be positioned along the chromosome by in situ hybridization using unique probes from each contig. Clone overlap is determined experimentally by factoring each clone into restriction fragments that are characterized by size and hybridization probe signals. Clones which overlap therefore share common restriction fragments, making overlap detectable. However, non-uniqueness of restriction fragments and experimental error lead to incorrect determination of the overlap relation and errors in the contig map. The Contig Graph Tool was developed to detect and correct overlap errors using editable visualizations of an abstract graph representation for clones and overlaps. This interactive tool is integrated with an electronic, laboratory notebook and introduces several concepts useful for solving problems with discrete, scientific visualization.

  12. A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

    SciTech Connect (OSTI)

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

    2015-02-02

    Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving net- works spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with promi- nent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named Relative Selectivity" that is used to se- lect between different query processing strategies. Our experiments performed on real online news, network traffic stream and a syn- thetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.

  13. Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

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

    Stracuzzi, David John; Brost, Randolph C.; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane M. -K.

    2015-09-26

    Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match qualitymore » scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.« less

  14. Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

    SciTech Connect (OSTI)

    Stracuzzi, David John; Brost, Randolph C.; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane M. -K.

    2015-09-26

    Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.

  15. PyDecay/GraphPhys: A Unified Language and Storage System for Particle Decay Process Descriptions

    SciTech Connect (OSTI)

    Dunietz, Jesse N.; /MIT /SLAC

    2011-06-22

    To ease the tasks of Monte Carlo (MC) simulation and event reconstruction (i.e. inferring particle-decay events from experimental data) for long-term BaBar data preservation and analysis, the following software components have been designed: a language ('GraphPhys') for specifying decay processes, common to both simulation and data analysis, allowing arbitrary parameters on particles, decays, and entire processes; an automated visualization tool to show graphically what decays have been specified; and a searchable database storage mechanism for decay specifications. Unlike HepML, a proposed XML standard for HEP metadata, the specification language is designed not for data interchange between computer systems, but rather for direct manipulation by human beings as well as computers. The components are interoperable: the information parsed from files in the specification language can easily be rendered as an image by the visualization package, and conversion between decay representations was implemented. Several proof-of-concept command-line tools were built based on this framework. Applications include building easier and more efficient interfaces to existing analysis tools for current projects (e.g. BaBar/BESII), providing a framework for analyses in future experimental settings (e.g. LHC/SuperB), and outreach programs that involve giving students access to BaBar data and analysis tools to give them a hands-on feel for scientific analysis.

  16. 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...

  17. Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

    DOE Patents [OSTI]

    Hively, Lee M.

    2014-09-16

    Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

  18. U.S. Energy Information Administration (EIA) - Data

    Gasoline and Diesel Fuel Update (EIA)

    Find statistics on electric power plants, capacity, generation, fuel consumption, sales, prices and customers. EXPAND ALL Summary Electricity overview PDF CSV XLS INTERACTIVE Interactive data from Total Energy Data Browser Total electric power industry summary statistics Monthly XLSLatest month XLSYear-to-date Annual (back to 2003) XLSSummary statistics XLSSupply and disposition of electricity More national-level summary data International electricity data data from: International Energy Portal

  19. U.S. Energy Information Administration (EIA) - Data

    Gasoline and Diesel Fuel Update (EIA)

    Find statistics on nuclear operable units, nuclear electricity net generation, nuclear share of electricity net generation, and capacity factor. + EXPAND ALL Summary Additional formats Nuclear Overview: PDF CSV XLS Monthly statistics on nuclear operable units, nuclear electricity net generation, nuclear share of electricity net generation, and capacity factor. PDFXLS Annual statistics on nuclear generating units, power plants operations, and uranium. › Nuclear generating units, 1955-2011 ›

  20. 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...

  1. DOE FAIR 2007 (OMB).xls

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

    I Z 1999 40 19-05 AL NNSA NM Albuquerque US 1 P119 I Z 1999 41 19-05 AL NNSA AZ Fort Smith US 1 T999 C B 1999 42 19-05 AL NNSA AZ Fort Smith US 1 T999 I Z 1999 43 19-05 AL NNSA...

  2. SSRL_2003_Run_Sched.xls

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

    6/02 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 8

  3. EM Current Project Performance.xls

    Office of Environmental Management (EM)

    0 Hanford Site VL-SPRU-0040.C1 Nuclear Facility D&D - Separations Process Research Unit CD-3 78,600,000 9302012 Pending 78,600,000 9302012 Knolls Atomic Power Laboratory...

  4. 2005_Run 1-21-05.xls

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

    21-05 Run Shutdown Maintenance / AP Injector Startup Spear Down Injector / SPEAR Startup University Holidays AP 7 24 26 MA 13 10 11 9 25 29 30 30 27 28 28 29 31 3 4 3 7 8 2 1 1 1 5 1 4 5 7 6 9 10 11 8 3 1 12 3 AP 4 7 30 2 20 19 14 17 30 2 1 4 5 9 10 7 13 9 12 14 4 2 3 2 2 1 6 3 7 5 19 9 10 6 1 3 5 5 16 3 13 6 7 9 8 9 15 11 14 12 29 17 25 17 16 23 24 25 18 30 27 28 27 25 26 24 23 29 31 28 27 31 30 29 2004 2005 31 18 19 20 12 15 16 17 14 9 8 14 15 13 11 13 11 12 10 8 5 3 8 13 10 12 11 4 6 5 4 2 2

  5. 2005_Run 3-29-05.xls

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

    SLAC Shutdown SSRL 2004-2005 SPEAR RUN SCHEDULE AP 7 14 13 10 11 9 30 27 26 28 24 23 25 30 27 28 26 28 29 31 29 2 1 3 4 3 7 1 8 2 1 1 1 4 1 2 4 6 9 10 11 8 3 12 3 AP 4 1 5 7 5 30 19 20 16 17 14 14 20 19 14 17 2 13 5 30 9 10 9 12 3 13 11 10 12 2 1 2 3 10 6 3 7 5 4 MA 10 11 11 9 7 18 12 15 6 1 3 5 5 4 9 8 9 7 3 13 6 7 15 11 14 12 29 User Conf. 17 25 17 16 23 24 30 27 28 26 29 29 29 31 30 8 2004 2005 31 9 15 13 25 22 11 13 11 12 8 5 3 6 MA 8 13 10 12 11 4 6 5 4 2 2 7 24 23 14 31 29 30 27 29 29 30

  6. Q2 External Package.xls

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

    376,031 285,072 76% 214,494 57% Interest Expense and (Income) 33 Interest Expense 352,982 351,730 331,697 94% 158,351 45% 34 AFUDC (43,062) (43,204) (45,230) 105% (26,819)...

  7. TableHC10.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  8. TableHC10.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  9. TableHC11.12.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  10. TableHC11.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  11. TableHC11.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    0.6 15.1 5.5 Household Size 1 Person............................................................... 30.0 5.5 3.8 1.7 2 Persons.............................................................. 34.8 6.5 4.8 1.7 3 Persons.............................................................. 18.4 3.4 2.4 1.1 4 Persons.............................................................. 15.9 3.0 2.4 0.7 5 Persons.............................................................. 7.9 1.4 1.2 0.2 6 or More

  12. TableHC11.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  13. TableHC12.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  14. TableHC12.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  15. TableHC12.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    5.6 17.7 7.9 Household Size 1 Person............................................................... 30.0 7.3 5.0 2.3 2 Persons.............................................................. 34.8 8.4 5.7 2.7 3 Persons.............................................................. 18.4 4.1 3.0 1.1 4 Persons.............................................................. 15.9 3.2 2.2 1.0 5 Persons.............................................................. 7.9 1.8 1.4 0.4 6 or More

  16. TableHC12.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  17. TableHC13.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  18. TableHC13.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  19. TableHC13.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  20. TableHC14.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  1. TableHC14.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  2. TableHC14.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    4.2 7.6 16.6 Household Size 1 Person............................................................... 30.0 5.7 1.5 4.2 2 Persons.............................................................. 34.8 7.4 2.9 4.5 3 Persons.............................................................. 18.4 3.9 1.2 2.7 4 Persons.............................................................. 15.9 4.0 1.1 2.9 5 Persons.............................................................. 7.9 1.7 0.5 1.3 6 or More

  3. TableHC14.5.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  4. TableHC14.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  5. TableHC15.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    5.1 Housing Unit Characteristics by Four Most Populated States, 2005 Total......................................................................... 111.1 7.1 7.0 8.0 12.1 Census Region and Division Northeast.............................................................. 20.6 7.1 N N N New England..................................................... 5.5 N N N N Middle Atlantic................................................... 15.1 7.1 N N N

  6. TableHC15.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    7.1 7.0 8.0 12.1 Household Size 1 Person.......................................................................... 30.0 1.8 1.9 2.0 3.2 2 Persons........................................................................ 34.8 2.2 2.3 2.4 3.2 3 Persons........................................................................ 18.4 1.1 1.3 1.2 1.8 4 Persons........................................................................ 15.9 1.0 0.9 1.0 2.3 5

  7. TableHC15.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  8. TableHC2.1.xls

    Gasoline and Diesel Fuel Update (EIA)

  9. TableHC2.10.xls

    Gasoline and Diesel Fuel Update (EIA)

    Coventional Oven Use an Oven......................................................... 109.6 71.3 7.4 7.7 16.4 6.8 More Than Once a Day..................................... 8.9 5.7 0.5 0.6 1.3 0.7 Once a Day....................................................... 19.2 13.3 1.3 1.4 2.1 1.0 Between Once a Day and Once a Week........... 32.0 22.7 2.1 1.8 4.0 1.5 Once a Week.................................................... 19.1 12.2 1.2 1.3 3.0 1.4 Less than Once a

  10. TableHC2.12.xls

    Gasoline and Diesel Fuel Update (EIA)

    Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or More Units Mobile Homes Type of Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Home Electonics Usage Indicators Detached Attached 2 to 4 Units Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing Units Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or

  11. TableHC2.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    Million U.S. Housing Units Total U.S. Housing Units........................................ 111.1 78.1 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

  12. TableHC2.13.xls

    Gasoline and Diesel Fuel Update (EIA)

  13. TableHC2.3.xls

    Gasoline and Diesel Fuel Update (EIA)

    Total................................................................... 111.1 78.1 64.1 4.2 1.8 2.3 5.7 Household Size 1 Person......................................................... 30.0 18.6 13.2 1.4 0.7 1.3 2.1 2 Persons........................................................ 34.8 26.8 22.9 1.3 0.5 0.7 1.4 3 Persons........................................................ 18.4 12.8 10.7 0.5 0.4 Q 1.0 4 Persons........................................................ 15.9 11.5 9.8 0.6 Q Q 0.9

  14. TableHC2.3.xls

    Gasoline and Diesel Fuel Update (EIA)

  15. TableHC2.4.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  16. TableHC2.5.xls

    Gasoline and Diesel Fuel Update (EIA)

    111.1 72.1 7.6 7.8 16.7 6.9 Do Not Have Heating Equpment...................... 1.2 0.4 Q Q 0.4 Q Have Space Heating Equpment....................... 109.8 71.7 7.5 7.6 16.3 6.8 Use Space Heating Equpment........................ 109.1 71.5 7.4 7.4 16.0 6.7 Have But Do Not Use Equipment.................... 0.8 Q Q Q Q Q Space Heating Usage During 2005 Heated Floorspace (Square Feet) None............................................................ 3.6 1.1 Q 0.5 1.3 0.4 1 to

  17. TableHC2.6.xls

    Gasoline and Diesel Fuel Update (EIA)

    Coolling Equipment................................ 93.3 61.2 6.1 6.3 13.9 5.8 Use Cooling Equipment................................... 91.4 60.3 6.0 6.1 13.5 5.5 Have Equipment But Do Not Use it................. 1.9 1.0 Q 0.2 0.4 Q Air-Conditioning Equipment 1, 2 Central System............................................... 65.9 47.5 4.0 2.8 7.9 3.7 Without a Heat Pump.................................. 53.5 37.8 3.4 2.2 7.0 3.1 With a Heat Pump....................................... 12.3 9.7 0.6

  18. TableHC2.7.xls

    Gasoline and Diesel Fuel Update (EIA)

    Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 47.5 4.0 2.8 7.9 3.7 Without a Heat Pump.................................. 53.5 37.8 3.4 2.2 7.0 3.1 With a Heat Pump....................................... 12.3 9.7 0.6 0.5 1.0 0.6 Window/Wall Units........................................ 28.9 14.9 2.3 3.5 6.0 2.1 1 Unit........................................................... 14.5 6.6 1.0 1.6 4.2 1.2 2

  19. TableHC2.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    Total........................................................................... 111.1 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

  20. TableHC2.8.xls

    Gasoline and Diesel Fuel Update (EIA)

  1. TableHC2.9.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  2. TableHC3.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  3. TableHC3.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  4. TableHC3.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  5. TableHC4.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  6. TableHC4.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    .. 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

  7. TableHC4.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  8. TableHC5.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  9. TableHC6.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  10. TableHC6.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  11. TableHC6.6.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  12. TableHC6.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  13. TableHC7.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    Number of Water Heaters 1................................................................. 106.3 25.8 28.0 19.6 12.7 20.2 16.0 37.3 2 or More.................................................... 3.7 0.3 0.5 0.9 0.4 1.7 Q 0.5 Do Not Use Hot Water................................ 1.1 0.6 0.3 Q N Q 0.5 0.8 Housing Units Served by Main Water Heater One Housing Unit....................................... 99.7 21.8 25.5 19.0 12.5 21.0 13.3 32.3 Two or More Housing Units........................ 10.3 4.3

  14. TableHC8.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  15. TableHC8.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    Number of Water Heaters 1............................................................................... 106.3 45.5 18.2 21.6 21.0 2 or More.................................................................. 3.7 1.0 0.6 0.9 1.1 Do Not Use Hot Water.............................................. 1.1 0.6 Q Q Q Housing Units Served by Main Water Heater One Housing Unit..................................................... 99.7 39.4 17.4 21.0 21.8 Two or More Housing

  16. TableHC9.1.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  17. TableHC9.8.xls

    Gasoline and Diesel Fuel Update (EIA)

    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

  18. 3REV2004DOEFAIR.xls

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

    4 Commercial and Inherently Governmental FTE Inventory Worksheet Seq. No. Agy_Bur Abbreviation State City Country Total FTEs Activity Fct Code Status Reason First Year On Inventory Reserve Reserve Reserve Reserve 1600 019-60 AB DC Washington US 1 Y000 C B 1999 1601 019-60 AB DC Washington US 0 Y815 C B 2003 1602 019-60 AB DC Washington US 1 Y815 C A 1999 1603 019-60 AB DC Washington US 1 Y815 C A 2002 7395 019-60 AB DC Washington US 1 Y210 I 1999 7396 019-60 AB DC Washington US 1 Y210 I 1999

  19. PSD Organization Functional v56.xls

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

    Directors Proposal Review Panel Hastings, J Sub-Picosecond Pulse Source Guerra, E Carlson, S Pianetta, P Pianetta, P Scott, B Operations & Systems Chemical & Materials...

  20. TableHC8.1.xls

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

    HC8.1 Housing Unit Characteristics by UrbanRural Location, 2005 Total... 111.1 47.1 19.0 22.7 22.3 Census...

  1. TableHC5.8.xls

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

    Main Water Heating Fuel Electric...... 43.1 4.0 1.4 3.8 4.1 8.6 8.6 8.5 4.0 For One Housing Unit......

  2. 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...

  3. LM Annual NEPA Summary 2014.xls

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

    Title, Location, Document Number Estimated Cost Description EA Determination Date: 1/23/2013 Transmittal to State: TBD EA Approval: TBD FONSI: TBD EA Determination Date: TBD Transmittal to State: EA Approval: FONSI: EA Determination Date: TBD Transmittal to State: EA Approval: FONSI: EA Determination Date: TBD Transmittal to State: EA Approval: FONSI: EA Determination Date: TBD Transmittal to State: EA Approval: FONSI: Total Estimated Cost $35,000 UMTRCA Title II Split Rock, WY Disposal Site

  4. September 2015 Project Dashboard.xls

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

    338,000,000 G 0.97 0.99 21 SC Brookhaven Science Associates, LLC 11-SC-30YD LHC ATLAS Detector Upgrade 33,250,000 33,250,000 G 1.01 0.98 22 SC Fermi Research Alliance,...

  5. November 2015 Project Dashboard.xls

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

    Dashboard - November 2015 22 SC Brookhaven Science Associates, LLC 11-SC-30YD LHC ATLAS Detector Upgrade 33,250,000 33,250,000 G 23 SC Fermi Research Alliance, LLC...

  6. 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"...

  7. ARRA Project Info Combined 0112110.xls

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

    Organization Location (State) Description Partners National Alliance for Advanced Biofuels and Bioproducts (NAABB) Led by the Donald Danforth Plant Science Center 44,036,473...

  8. Table3_EntityReductions.xls

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

    Carbon Dioxide Equivalent) Reduction Type 1991 1992 1993 1994 1995 1996 1997 1998 1999 ... CONNECTIVITY SOLUTONS MANUFACTURING Inc. Reduction Type 1991 1992 1993 1994 1995 1996 1997 ...

  9. Table 5_EntityEmissions.xls

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

    Reduction Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 ... AES Shady Point, LLC AES Thames, LLC Reduction Type 1990 1991 1992 1993 1994 1995 1996 ...

  10. 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

  11. 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

  12. FY2011 2nd QTR.xls

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

    IL KY MD NM NY OH SC TN TN TN TN, WA, CA TN TN TN TX Total Shipments by Route Lawrence Livermore National Laboratory Boeing Company Batelle Energy Alliance Idaho National Laboratory Advanced Mixed Waste Treatment Project Energx Argonne National Laboratory Argonne National Laboratory Paducah Gaseous Diffusion Plant Aberdeen Proving Ground Sandia National Laboratory Brookhaven National Laboratory Portsmouth Gaseous Diffusion Plant Savannah River Site UT Batelle/Oak Ridge National Laboratory

  13. 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

  14. Project_Descriptions_ITP_ARRA_Awards.xls

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

    Selections for Industrial Technologies Program Recovery Act Funding Deployment of Combined Heat and Power (CHP) Systems, District Energy Systems, Waste Energy Recovery Systems, and Efficient Industrial Equipment Award Winners City and State Project Description Total DOE Funding Air Products and Chemicals, Inc. Middletown, OH Waste Energy Project at the AK Steel Corporation Middletown Works. The project will construct a combined cycle power generation plant at the Middletown, OH, works of AK

  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. table1.5_02.xls

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

    5 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National Data; Row: Energy Sources and Shipments, including Further Classification of 'Other' Energy Sources; Column: First Use per Energy Sources and Shipments; Unit: Trillion Btu. RSE Total Row Energy Source First Use Factors Total United States RSE Column Factor: 1.0 Coal 1,959 10.0 Natural Gas 6,468 1.3 Net Electricity 2,840 1.4 Purchases 2,882 1.4 Transfers In 35 2.6 Onsite Generation from Noncombustible Renewable

  17. table10.10_02.xls

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

    0 Capability to Switch Coal to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Thousand Short Tons. RSE NAICS Total Not Electricity Natural Distillate Residual Row Code(a) Subsector and Industry Consumed(c) Switchable Switchable Receipts(d) Gas Fuel Oil Fuel Oil LPG Other(e) Factors Total United States RSE Column Factors: 1.4 1.1 1.5 0.7 1.1 0.8 1.2 1.5 0.5 311 Food 8,290 1,689

  18. table10.12_02.xls

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

    2 Capability to Switch LPG to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Thousand Barrels. Coal Coke RSE NAICS Total Not Electricity Natural Distillate Residual and Row Code(a) Subsector and Industry Consumed(c) Switchable Switchable Receipts(d) Gas Fuel Oil Fuel Oil Coal Breeze Other(e) Factors Total United States RSE Column Factors: 1 1 1 1.1 0.8 0.9 0.5 4.3 0 0.5 311 Food

  19. table10.1_021.xls

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

    Nonswitchable Minimum and Maximum Consumption, 2002; Level: National and Regional Data; Row: Energy Sources; Column: Consumption Potential; Unit: Physical Units. RSE Actual Minimum Maximum Row Energy Sources Consumption Consumption(a) Consumption(b) Factors Total United States RSE Column Factors: 1 1 1 Electricity Receipts(c) (million kilowatthours) 855,160 668,467 894,613 2 Natural Gas (billion cubic feet) 5,641 3,536 6,108 2 Distillate Fuel Oil (thousand barrels) 24,446 13,621 118,299 5

  20. table10.2_02.xls

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

    2 Capability to Switch Natural Gas to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Billion Cubic Feet. Coal Coke RSE NAICS Total Not Electricity Distillate Residual and Row Code(a) Subsector and Industry Consumed(c) Switchable Switchable Receipts(d) Fuel Oil Fuel Oil Coal LPG Breeze Other(e) Factors Total United States RSE Column Factors: 0.8 1 0.9 1.6 1 1 1.1 1.1 0.5 1.3 311

  1. table10.4_02.xls

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

    4 Capability to Switch Residual Fuel Oil to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Thousand Barrels. Coal Coke RSE NAICS Total Not Electricity Natural Distillate and Row Code(a) Subsector and Industry Consumed(c) Switchable Switchable Receipts(d) Gas Fuel Oil Coal LPG Breeze Other(e) Factors Total United States RSE Column Factors: 1.9 1.4 1.9 0.6 1.5 0.6 0.6 0.9 0 0.7 311

  2. table10.5_02.xls

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

    5 Number of Establishments with Capability to Switch Residual Fuel Oil to Alternative Energy Sources, 2002; Level: National Data; Row: NAICS Codes; Column: Energy Sources; Unit: Establishment Counts. Coal Coke RSE NAICS Total Not Electricity Natural Distillate and Row Code(a) Subsector and Industry Consumed(d) Switchable Switchable Receipts(e) Gas Fuel Oil Coal LPG Breeze Other(f) Factors Total United States RSE Column Factors: 1.3 1 1.5 0.7 1 0.8 0.6 1.2 1.4 0.8 311 Food 274 183 108 0 119 72 W

  3. table10.6_02.xls

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

    Capability to Switch Electricity to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Million Kilowatthours. Coal Coke RSE NAICS Total Not Natural Distillate Residual and Row Code(a) Subsector and Industry Receipts(c) Switchable Switchable Gas Fuel Oil Fuel Oil Coal LPG Breeze Other(d) Factors Total United States RSE Column Factors: 0.9 1.4 0.9 1.6 1.7 0.6 0.8 1.7 0.5 0.9 311 Food

  4. table10.7_02.xls

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

    Number of Establishments with Capability to Switch Electricity to Alternative Energy Sources, 2002; Level: National Data; Row: NAICS Codes; Column: Energy Sources; Unit: Establishment Counts. Coal Coke RSE NAICS Total Not Natural Distillate Residual and Row Code(a) Subsector and Industry Receipts(d) Switchable Switchable Gas Fuel Oil Fuel Oil Coal LPG Breeze Other(e) Factors Total United States RSE Column Factors: 0.6 1.2 0.6 1.2 1.3 1 0.8 1.4 1.3 1.2 311 Food 15,045 582 14,905 185 437 30 W 170

  5. table10.8_02.xls

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

    Capability to Switch Distillate Fuel Oil to Alternative Energy Sources, 2002; Level: National Data and Regional Totals; Row: NAICS Codes, Value of Shipments and Employment Sizes; Column: Energy Sources; Unit: Thousand Barrels. Coal Coke RSE NAICS Total Not Electricity Natural Residual and Row Code(a) Subsector and Industry Consumed(c) Switchable Switchable Receipts(d) Gas Fuel Oil Coal LPG Breeze Other(e) Factors Total United States RSE Column Factors: 1.7 1.6 1.7 0.9 1.5 0.6 0.7 1.7 0.3 0.8

  6. table10.9_02.xls

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

    Number of Establishments with Capability to Switch Distillate Fuel Oil to Alternative Energy Sources, 2002; Level: National Data; Row: NAICS Codes; Column: Energy Sources; Unit: Establishment Counts. Coal Coke RSE NAICS Total Not Electricity Natural Residual and Row Code(a) Subsector and Industry Consumed(d) Switchable Switchable Receipts(e) Gas Fuel Oil Coal LPG Breeze Other(f) Factors Total United States RSE Column Factors: 1 1.3 1 0.9 1.2 1 0.8 1.3 0.8 0.9 311 Food 2,418 789 1,899 129 447

  7. table11.1_02.xls

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

    Electricity: Components of Net Demand, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Electricity Components; Unit: Million Kilowatthours. Total Sales and Net Demand RSE NAICS Transfers Onsite Transfers for Row Code(a) Subsector and Industry Purchases In(b) Generation(c) Offsite Electricity(d) Factors Total United States RSE Column Factors: 1.3 1.1 0.9 0.6 1.2 311 Food W W 5,622 708 73,143 5.1 311221 Wet Corn Milling W W 2,755 248 9,606 2.6 31131 Sugar 733 * 1,126 8 1,851 1

  8. table11.3_02.xls

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

    Electricity: Components of Onsite Generation, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Onsite-Generation Components; Unit: Million Kilowatthours. Renewable Energy (excluding Wood RSE NAICS Total Onsite and Row Code(a) Subsector and Industry Generation Cogeneration(b) Other Biomass)(c) Other(d) Factors Total United States RSE Column Factors: 0.9 0.8 1.1 1.3 311 Food 5,622 5,375 0 247 12.5 311221 Wet Corn Milling 2,755 2,717 0 38 2.6 31131 Sugar 1,126 1,077 0 48 1 311421

  9. 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

  10. table11.5_02.xls

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

    Electricity: Sales to Utility and Nonutility Purchasers, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Utility and Nonutility Purchasers; Unit: Million Kilowatthours. Total of RSE NAICS Sales and Utility Nonutility Row Code(a) Subsector and Industry Transfers Offsite Purchaser(b) Purchaser(c) Factors Total United States RSE Column Factors: 1 0.9 1 311 Food 708 380 328 31 311221 Wet Corn Milling 248 W W 20.1 31131 Sugar 8 8 0 1 311421 Fruit and Vegetable Canning 28 W W 1 312

  11. 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

  12. table2.1_02.xls

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

    1 Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Physical Units or Btu. Coke Residual Distillate Natural LPG and Coal and Breeze NAICS Total Fuel Oil Fuel Oil(b) Gas(c) NGL(d) (million (million Other(e) Code(a) Subsector and Industry (trillion Btu) (million bbl) (million bbl) (billion cu ft) (million bbl) short tons) short tons) (trillion Btu) Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1

  13. table2.2_02.xls

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

    Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Trillion Btu. RSE NAICS Residual Distillate Natural LPG and Coke Row Code(a) Subsector and Industry Total Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal and Breeze Other(e) Factors Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1 0.7 1.2 311 Food 8 * Q 7 0 0 * * 10.2 311221 Wet Corn Milling * 0 * 0 0 0 0 * 0.7 31131 Sugar * 0 * * 0 0 * * 0.9 311421

  14. 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

  15. table2.4_02.xls

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

    Number of Establishments by Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National Data; Row: NAICS Codes (3-Digit Only); Column: Energy Sources; Unit: Establishment Counts. Any Combustible RSE NAICS Energy Residual Distillate Natural LPG and Coke Row Code(a) Subsector and Industry Source(b) Fuel Oil Fuel Oil(c) Gas(d) NGL(e) Coal and Breeze Other(f) Factors Total United States RSE Column Factors: 1.5 0.6 1.1 1 1.1 0.7 1 1.4 311 Food 406 W 152 185 0 0 4 83 9.6 311221 Wet Corn

  16. table3.3_02.xls

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

    Fuel Consumption, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: Trillion Btu. RSE Economic Net Residual Distillate Natural LPG and Coke and Row Characteristic(a) Total Electricity(b) Fuel Oil Fuel Oil(c) Gas(d) NGL(e) Coal Breeze Other(f) Factors Total United States RSE Column Factors: 0.6 0.7 1.3 2.1 0.7 1.4 1.5 0.7 0.9 Value of Shipments and Receipts (million dollars) Under 20 1,312 436 15 50 598 W 47 W 132 13.9 20-49

  17. table4.1_02.xls

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

    Offsite-Produced Fuel Consumption, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Physical Units or Btu. Coke Residual Distillate Natural LPG and Coal and Breeze RSE NAICS Total Electricity(b) Fuel Oil Fuel Oil(c) Gas(d) NGL(e) (million (million Other(f) Row Code(a) Subsector and Industry (trillion Btu) (million kWh) (million bbl) (million bbl) (billion cu ft) (million bbl) short tons) short tons) (trillion Btu) Factors Total United States RSE Column

  18. table4.2_02.xls

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

    Offsite-Produced Fuel Consumption, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Trillion Btu. RSE NAICS Residual Distillate Natural LPG and Coke Row Code(a) Subsector and Industry Total Electricity(b) Fuel Oil Fuel Oil(c) Gas(d) NGL(e) Coal and Breeze Other(f) Factors Total United States RSE Column Factors: 0.8 0.8 1.1 1.6 0.9 1.8 0.7 0.7 1.2 311 Food 1,079 233 13 19 575 5 184 1 50 8 311221 Wet Corn Milling 217 24 * * 61 * 121 0 11 1.1 31131 Sugar 74

  19. table4.3_02.xls

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

    Offsite-Produced Fuel Consumption, 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 Electricity(b) Fuel Oil Fuel Oil(c) Gas(d) NGL(e) Coal Breeze Other(f) Factors Total United States RSE Column Factors: 0.6 0.6 1.3 2.2 0.7 1.4 1.5 0.6 1 Value of Shipments and Receipts (million dollars) Under 20 1,276 437 15 50 598 W 47 W 97 14.5

  20. table5.7_02.xls

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

    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. Distillate Net Demand Fuel Oil Coal for Residual and Natural LPG and (excluding Coal RSE Electricity(a) Fuel Oil Diesel Fuel(b) Gas(c) NGL(d) Coke and Breeze) Row End Use (million kWh) (million bbl) (million bbl) (billion cu ft) (million bbl) (million short tons) Factors Total United States RSE Column Factors: 0.3 2.4

  1. table6.1_02.xls

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

    1 Consumption Ratios of Fuel, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy-Consumption Ratios; Unit: Varies. Consumption Consumption per Dollar Consumption per Dollar of Value RSE NAICS per Employee of Value Added of Shipments Row Code(a) Subsector and Industry (million Btu) (thousand Btu) (thousand Btu) Factors Total United States RSE Column Factors: 1.1 0.9 1 311 Food 867.8 6.0 2.6 5.9 311221 Wet Corn Milling 24,113.7 65.7 26.2 1.8 31131 Sugar 8,414.5 54.2 17.9 1

  2. table6.2_02.xls

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

    2 Consumption Ratios of Fuel, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy-Consumption Ratios; Unit: Varies. Consumption Consumption per Dollar Consumption per Dollar of Value RSE Economic per Employee of Value Added of Shipments Row Characteristic(a) (million Btu) (thousand Btu) (thousand Btu) Factors Total United States RSE Column Factors: 1.1 1 0.9 Value of Shipments and Receipts (million dollars) Under 20 281.0 3.9 2.2 3 20-49 583.7

  3. table6.3_02.xls

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

    3 Consumption Ratios of Fuel, 2002; Level: National Data; Row: Values of Shipments within NAICS Codes; Column: Energy-Consumption Ratios; Unit: Varies. Consumption Consumption per Dollar Consumption per Dollar of Value RSE NAICS per Employee of Value Added of Shipments Row Code(a) Economic Characteristic(b) (million Btu) (thousand Btu) (thousand Btu) Factors Total United States RSE Column Factors: 1 1 1 311 - 339 ALL MANUFACTURING INDUSTRIES Value of Shipments and Receipts (million dollars)

  4. table6.4_02.xls

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

    4 Consumption Ratios of Fuel, 2002; Level: National Data; Row: Employment Sizes within NAICS Codes; Column: Energy-Consumption Ratios; Unit: Varies. Consumption Consumption per Dollar Consumption per Dollar of Value RSE NAICS per Employee of Value Added of Shipments Row Code(a) Economic Characteristic(b) (million Btu) (thousand Btu) (thousand Btu) Factors Total United States RSE Column Factors: 1.1 1 1 311 - 339 ALL MANUFACTURING INDUSTRIES Employment Size Under 50 395.7 4.3 2.3 3.6 50-99 663.4

  5. table7.10_02.xls

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

    0 Expenditures for Purchased Electricity, Natural Gas, and Steam, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Supplier Sources of Purchased Electricity, Natural Gas, and Steam; Unit: Million U.S. Dollars. Electricity Components Natural Gas Components Steam Components Electricity Natural Gas Steam Electricity from Sources Natural Gas from Sources Steam from Sources RSE NAICS Electricity from Local Other than Natural Gas from Local Other than Steam from Local Other than Row

  6. table7.1_02.xls

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

    Average Prices of Purchased Energy Sources, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: All Energy Sources Collected; Unit: U.S. Dollars per Physical Units. Bituminous and Coal Subbituminous Coal Petroleum NAICS TOTAL Acetylene Breeze Total Anthracite Coal Lignite Coke Coke Code(a) Subsector and Industry (million Btu) (cu ft) (short tons) (short tons) (short tons) (short tons) (short tons) (short tons) (gallons) Total United States RSE Column Factors: 1.1 2.1 0.6 1 0.6

  7. table7.2_02.xls

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

    Average Prices of Purchased Energy Sources, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: All Energy Sources Collected; Unit: U.S. Dollars per Million Btu. Bituminous and NAICS Coal Subbituminous Coal Petroleum Code(a) Subsector and Industry TOTAL Acetylene Breeze Total Anthracite Coal Lignite Coke Coke Total United States RSE Column Factors: 1.1 2.1 0.6 0.9 0.6 0.9 1.4 0.7 0.9 311 Food 6.42 113.78 0 1.46 W 1.46 0 5.18 0 311221 Wet Corn Milling 3.11 106.84 0 1.32 0 1.32 0 0

  8. table7.3_02.xls

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

    3 Average Prices of Purchased Electricity, Natural Gas, and Steam, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Supplier Sources of Purchased Electricity, Natural Gas, and Steam; Unit: U.S. Dollars per Physical Units. Electricity Components Natural Gas Components Steam Components Electricity Natural Gas Steam Electricity from Sources Natural Gas from Sources Steam from Sources Electricity from Local Other than Natural Gas from Local Other than Steam from Local Other than

  9. 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

  10. 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

  11. table7.6_02.xls

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

    6 Quantity of Purchased Energy Sources, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Physical Units or Btu. Coke Residual Distillate Natural LPG and Coal and Breeze RSE NAICS Total Electricity Fuel Oil Fuel Oil(b) Gas(c) NGL(d) (million (million Other(e) Row Code(a) Subsector and Industry (trillion Btu) (million kWh) (million bbl) (million bbl) (billion cu ft) (million bbl) short tons) short tons) (trillion Btu) Factors Total United States RSE Column

  12. table7.7_02.xls

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

    Quantity of Purchased Electricity, Natural Gas, and Steam, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Supplier Sources of Purchased Electricity, Natural Gas, and Steam; Unit: Physical Units or Btu. Electricity Components Natural Gas Components Steam Components Electricity Natural Gas Steam Electricity from Sources Natural Gas from Sources Steam from Sources Electricity from Local Other than Natural Gas from Local Other than Steam from Local Other than RSE NAICS Total

  13. table7.9_02.xls

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

    Expenditures for Purchased Energy Sources, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Million U.S. Dollars. RSE NAICS Residual Distillate Natural LPG and Coke Row Code(a) Subsector and Industry Total Electricity Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal and Breeze Other(e) Factors Total United States RSE Column Factors: 0.9 0.9 1.1 1.5 0.9 1.4 0.8 0.7 1.2 311 Food 6,943 3,707 58 135 2,546 38 276 8 175 8 311221 Wet Corn Milling 683 252 2 1 237 * 165 0

  14. table8.1_02.xls

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

    1 Number of Establishments by Participation in Energy-Management Activity, 2002 Level: National Data; Row: Energy-Management Activities within NAICS Codes; Column: Participation and Source of Financial Support for Activity; Unit: Establishment Counts. RSE NAICS Row Code(a) Energy-Management Activity No Participation Participation(b) In-house Other Don't Know Factors Total United States RSE Column Factors: 0.9 1.4 0.9 0.9 1 311 - 339 ALL MANUFACTURING INDUSTRIES Participation in One or More of

  15. table8.2_02.xls

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

    Number of Establishments by Usage of General Energy-Saving Technologies, 2002 Level: National Data; Row: NAICS Codes; Column: Usage within General Energy-Saving Technologies Unit: Establishment Counts. NAICS Code(a) Subsector and Industry Establishments(b) In Use(e) Not in Use Don't Know In Use(e) Not in Use Don't Know Total United States RSE Column Factors: 0 1.1 0.7 1.2 1 0.9 1.3 311 Food 15,089 1,546 12,347 1,196 4,360 9,442 1,287 311221 Wet Corn Milling 49 14 34 1 38 10 1 31131 Sugar 77 4

  16. table8.3_02.xls

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

    Number of Establishments by Usage of Cogeneration Technologies, 2002; Level: National Data; Row: NAICS Codes; Column: Usage within Cogeneration Technologies; Unit: Establishment Counts. NAICS Code(a) Subsector and Industry Establishments(b) Establishments with Any Cogeneration Technology in Use(c) In Use(d) Not in Use Don't Know In Use(d) Not in Use Don't Know Total United States RSE Column Factors: 0 1 0.7 0.8 1.7 0.6 0.8 1.7 311 Food 15,089 443 131 13,850 1,109 80 13,729 1,280 311221 Wet Corn

  17. table9.1_02.xls

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

    Enclosed Floorspace and Number of Establishment Buildings, 2002; Level: National Data; Row: NAICS Codes; Column: Floorspace and Buildings; Unit: Floorspace Square Footage and Building Counts. Approximate Approximate Average Enclosed Floorspace Average Number Number of All Buildings Enclosed Floorspace of All Buildings of Buildings Onsite RSE NAICS Onsite Establishments(b) per Establishment Onsite per Establishment Row Code(a) Subsector and Industry (million sq ft) (counts) (sq ft) (counts)

  18. TableHC9.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... 0.3 Q Q Q Q Q Less than 4,000 HDD Housing Units (millions) Climate Zone 1 Table HC9.13 Lighting Usage Indicators by Climate Zone, 2005 Lighting Usage...

  19. 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 ...

  20. TableHC10.8.xls

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

    ... Census Region, 2005 Million U.S. Housing Units Water Heating Characteristics U.S. Census Region Northeast Midwest South West Energy Information Administration 2005 Residential ...