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1

Rainfall-River Forecasting  

E-Print Network [OSTI]

;2Rainfall-River Forecasting Joint Summit II NOAA Integrated Water Forecasting Program · Minimize losses due management and enhance America's coastal assets · Expand information for managing America's Water Resources, Precipitation and Water Quality Observations · USACE Reservoir Operation Information, Streamflow, Snowpack

US Army Corps of Engineers

2

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological forecast regimes at the wind energy site and fits a conditional predictive model for each regime

Washington at Seattle, University of

3

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2014-07-09T23:59:59.000Z

4

Savannah River Technology Center, monthly report  

SciTech Connect (OSTI)

This is the monthly report to detail the research currently being conducted at the Savannah River Technology Center. The areas of research are in Tritium, Seperation processes, Environmental Engineering, and Waste Management.

Not Available

1994-04-01T23:59:59.000Z

5

Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River Basin  

SciTech Connect (OSTI)

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003-2007. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium Range Weather Forecasts (ECMWF) analysis temperatures and wind, and Tropical Rainfall Monitoring Mission Multi Satellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF ensemble prediction system (EPS) 10-day ensemble forecast. A parallel set up was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The flood prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. Initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

Voisin, Nathalie; Pappenberger, Florian; Lettenmaier, D. P.; Buizza, Roberto; Schaake, John

2011-08-15T23:59:59.000Z

6

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching  

E-Print Network [OSTI]

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center

Genton, Marc G.

7

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

storms 17 4. Genesis Forecasts 17 5. Summary and Concluding Remarks 18 a. Atlantic Summary 18 statistical models, provided the best intensity guidance at each time period. The 2007 season marked the first

8

Products and Service of Center for Weather Forecast and Climate Studies  

E-Print Network [OSTI]

LOG O Products and Service of Center for Weather Forecast and Climate Studies Simone Sievert da products Supercomputer Facilities DSA/CPTEC-INPE Monitoring products based on remote sensing Training products Numerical Forecast Products Weather discussion Colleting data platform #12;Atmospheric Chemistry

9

SAVANNAH RIVER TECHNOLOGY CENTER MONTHLY REPORT AUGUST 1992  

SciTech Connect (OSTI)

'This monthly report summarizes Programs and Accomplishments of the Savannah River Technology Center in support of activities at the Savannah River Site. The following categories are addressed: Reactor, Tritium, Separations, Environmental, Waste Management, General, and Items of Interest.'

Ferrell, J.M.

1999-06-21T23:59:59.000Z

10

Savannah River Technology Center monthly report, January 1994  

SciTech Connect (OSTI)

This is the monthly progress report for the Savannah River Technology Center, which covers the following areas of interest, Tritium, Separation processes, Environmental Issues, and Waste Management.

Not Available

1994-01-01T23:59:59.000Z

11

New Climate Research Centers Forecast Changes and Challenges | Department  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomen OwnedofDepartment ofJaredOak Ridge’s EMGeothermalEnergy NEWSDepartmentof

12

Device-oriented telecommunications customer call center demand forecasting  

E-Print Network [OSTI]

Verizon Wireless maintains a call center infrastructure employing more than 15,000 customer care representatives across the United States. The current resource management process requires a lead time of several months to ...

Koul, Ashish, 1979-

2014-01-01T23:59:59.000Z

13

Savannah River Technology Center. Monthly report  

SciTech Connect (OSTI)

This document contains information about the research programs being conducted at the Savannah River Plant. Topics of discussion include: thermal cycling absorption process, development of new alloys, ion exchange, oxalate precipitation, calcination, environmental research, remedial action, ecological risk assessments, chemical analysis of salt cakes, natural phenomena hazards assessment, and sampling of soils and groundwater.

Not Available

1994-02-01T23:59:59.000Z

14

Savannah River Technology Center. Monthly report  

SciTech Connect (OSTI)

This is a monthly progress report from the Savannah River Laboratory for the month of January 1993. It has sections with work in the areas of reactor safety, tritium processes and absorption, separations programs and wastes, environmental concerns and responses, waste management practices, and general concerns.

Not Available

1993-01-01T23:59:59.000Z

15

River Valley Technology Center | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, searchVirginia Blue RidgeUniversityMedio, New Mexico:LimitedEdge,Technology

16

Conference Center | Savannah River Ecology Laboratory  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary)morphinanInformation InInformationCenterResearchCASLNanoporous MaterialsComplianceOrganizationConference Center

17

Savannah River Technology Center monthly report  

SciTech Connect (OSTI)

This document contains many small reports from personnel at the technology center under the umbrella topics of reactors, tritium, separations, environment, waste management, and general engineering. Progress and accomplishments are given.

Not Available

1992-10-01T23:59:59.000Z

18

Savannah River Technology Center Quarterly Report - July, Aug., and Sept., 1997  

SciTech Connect (OSTI)

This monthly report summarizes programs and accomplishments of the Savannah River Technology Center in support of activities at the Savannah River Site.

Ferrell, J.M.

1998-10-16T23:59:59.000Z

19

RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-RELEASES  

E-Print Network [OSTI]

1 RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-line Planning Mode, the Reservoir Release Forecast Model (RRFM) is being used to test alternatives operating River Forecast Center. The RRFM will make possible the risk-based operation of the Folsom Project

Bowles, David S.

20

Savannah River BioEnergy Integration Center Savannah River BioEnergy Integration Center  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassiveSubmitted for USMaterialstheterahertz sourcesSatellite storiesExpansion

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Call center demand forecasting : improving sales calls prediction accuracy through the combination of statistical methods and judgmental forecast  

E-Print Network [OSTI]

Call centers are important for developing and maintaining healthy relationships with customers. At Dell, call centers are also at the core of the company's renowned direct model. For sales call centers in particular, the ...

Boulin, Juan Manuel

2010-01-01T23:59:59.000Z

22

9A.2 FORECAST CHALLENGES AT THE NWS STORM PREDICTION CENTER RELATING TO THE FREQUENCY OF FAVORABLE SEVERE STORM ENVIRONMENTS  

E-Print Network [OSTI]

to the performance of convective watches issued by the NOAA Storm Prediction Center (SPC layer bulk wind shear, are linked to SPC convective watches and verification metrics forecast performance. 2. SPC CONVECTIVE WATCHES A convective watch is issued by the SPC

23

Savannah River Technology Center. Quarterly report, July 1, 1996--September 30, 1996  

SciTech Connect (OSTI)

This report provides information and progress from the Savannah River Technology Center. Topics include tritium activities, separations, environmental, and waste management activities.

Ferrell, J.M.

1997-07-01T23:59:59.000Z

24

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy  

SciTech Connect (OSTI)

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.

Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

2011-10-01T23:59:59.000Z

25

Savannah River Technology Center (SRTC) Designated as a National Laboratory  

Broader source: Energy.gov [DOE]

In 2004, the Secretary of Energy designated SRTC as a national laboratory based on its contributions and important role it has played in both energy and defense programs of the United States. The lab was also renamed the Savannah River National Laboratory (SRNL).

26

Savannah River Technology Center monthly report, September 1992  

SciTech Connect (OSTI)

This is a monthly progress report from the Savannah River Laboratory for the month of September, 1992. It has sections dealing with work in the broad areas of reactor safety, tritium processes and absorption, separations programs and wastes, environmental concerns and responses, waste management practices, and general concerns.

Ferrell, J.M. [comp.

1992-09-01T23:59:59.000Z

27

Deep River Center, Connecticut: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublic Utilities Address:011-DNA Jump to:52c8ff988c1 No38e4011f618b NoIllinois: Energy ResourcesDedini S ARiver

28

A High-Fidelity Temperature Distribution Forecasting System for Data Centers Jinzhu Chen1  

E-Print Network [OSTI]

are essential for preventing overheating- induced server shutdowns and improving a data center's energy are complex Cyber-Physical Systems (CPS) whose thermal characteristics are inherently affected by both

29

Forecast Correlation Coefficient Matrix of Stock Returns in Portfolio Analysis  

E-Print Network [OSTI]

Unadjusted Forecasts . . . . . . . . . . . . . . . .Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . .Unadjusted Forecasts . . . . . . . . . . . . . . . . . . .

Zhao, Feng

2013-01-01T23:59:59.000Z

30

Forecasted Opportunities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation ProposedUsing ZirconiaPolicyFeasibilityFieldMinds" |beamtheFor yourForForecasted

31

Forecast Technical Document Forecast Types  

E-Print Network [OSTI]

Forecast Technical Document Forecast Types A document describing how different forecast types are implemented in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Forecast Types Background Different `types' of forecast are possible for a specified area

32

Comment on "A reappraisal of the Kalman filtering technique, as applied in river flow forecasting" by Ashan, M.,  

E-Print Network [OSTI]

forecasting" by Ashan, M., O'Connor, K.M., 1994. Journal of Hydrology 161, 197­226 Jozsef Szilagyi402-472-9667; fax: þ402-472-4608. E-mail address: jszilagyil@unl.edu (J. Szilagyi). #12;where

Szilagyi, Jozsef

33

Improving week two forecasts with multi-model re-forecast ensembles  

E-Print Network [OSTI]

Improving week two forecasts with multi-model re-forecast ensembles Jeffrey S. Whitaker and Xue Wei NOAA-CIRES Climate Diagnostics Center, Boulder, CO Fr´ed´eric Vitart Seasonal Forecasting Group, ECMWF dataset of ensemble 're-forecasts' from a single model can significantly improve the skill

Whitaker, Jeffrey S.

34

P2.3 DEVELOPMENT OF A COMPREHENSIVE SEVERE WEATHER FORECAST VERIFICATION SYSTEM AT THE STORM PREDICTION CENTER  

E-Print Network [OSTI]

: Andrew R. Dean, CIMMS, Univ. of Oklahoma, National Weather Center, Suite 2300, Norman, OK 73072-7268; e PREDICTION CENTER Andrew R. Dean*1,2 , Russell S. Schneider 2 , and Joseph T. Schaefer 2 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 2 NOAA/NWS Storm

35

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator Aug 2013 Center for Economic Analysis and Forecasting (CEAF), California  

E-Print Network [OSTI]

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator Aug 2013 © Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

36

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator November 2013 Center for Economic Analysis and Forecasting (CEAF), Calif  

E-Print Network [OSTI]

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator November 2013 © Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

37

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator May 2014 Center for Economic Analysis and Forecasting (CEAF), California  

E-Print Network [OSTI]

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator May 2014 © Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

38

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator February 2014 Center for Economic Analysis and Forecasting (CEAF), Calif  

E-Print Network [OSTI]

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator February 2014 © Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

39

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator August 2014 Center for Economic Analysis and Forecasting (CEAF), Califor  

E-Print Network [OSTI]

Southern California Leading EconomicSouthern California Leading EconomicSouthern California Leading EconomicSouthern California Leading Economic IndicatorIndicatorIndicatorIndicator August 2014 © Center for Economic Analysis and Forecasting (CEAF), California State University Fullerton Adrian R. Fleissig, Ph

de Lijser, Peter

40

Solar Forecasting  

Broader source: Energy.gov [DOE]

On December 7, 2012,DOE announced $8 million to fund two solar projects that are helping utilities and grid operators better forecast when, where, and how much solar power will be produced at U.S....

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Forecast Technical Document Restocking in the Forecast  

E-Print Network [OSTI]

Forecast Technical Document Restocking in the Forecast A document describing how restocking of felled areas is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the forecast Background During the period of a production forecast it is assumed that, as forest sub

42

Streamflow Forecasting Based on Statistical Applications and Measurements Made with Rain Gage and Weather Radar  

E-Print Network [OSTI]

Techniques for streamflow forecasting are developed and tested for the Little Washita River in Oklahoma. The basic input for streamflow forecasts is rainfall. the rainfall amounts may be obtained from several sources; however, this study...

Hudlow, M.D.

43

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Power Basics (TheEtelligence (SmartHome Kyoung's pictureFlintFlowerForecast

44

USE OF AN EQUILIBRIUM MODEL TO FORECAST DISSOLUTION EFFECTIVENESS, SAFETY IMPACTS, AND DOWNSTREAM PROCESSABILITY FROM OXALIC ACID AIDED SLUDGE REMOVAL IN SAVANNAH RIVER SITE HIGH LEVEL WASTE TANKS 1-15  

SciTech Connect (OSTI)

This thesis details a graduate research effort written to fulfill the Magister of Technologiae in Chemical Engineering requirements at the University of South Africa. The research evaluates the ability of equilibrium based software to forecast dissolution, evaluate safety impacts, and determine downstream processability changes associated with using oxalic acid solutions to dissolve sludge heels in Savannah River Site High Level Waste (HLW) Tanks 1-15. First, a dissolution model is constructed and validated. Coupled with a model, a material balance determines the fate of hypothetical worst-case sludge in the treatment and neutralization tanks during each chemical adjustment. Although sludge is dissolved, after neutralization more is created within HLW. An energy balance determines overpressurization and overheating to be unlikely. Corrosion induced hydrogen may overwhelm the purge ventilation. Limiting the heel volume treated/acid added and processing the solids through vitrification is preferred and should not significantly increase the number of glass canisters.

KETUSKY, EDWARD

2005-10-31T23:59:59.000Z

45

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira Farka, Ph.D. -- Co-Director, Center for Economic Analysis and Forecasting -- Associate Professor

de Lijser, Peter

46

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS FORECAST IMPROVEMENTS  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS BRISBANE FORECAST IMPROVEMENTS The Bureau of Meteorology is progressively upgrading its forecast system to provide more detailed forecasts across Australia and Sunshine Coast. FURTHER INFORMATION : www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 Links

Greenslade, Diana

47

Environmental Assessment for the construction and operation of the Three Rivers Solid Waste Authority regional waste management center at the Savannah River Site  

SciTech Connect (OSTI)

This Environmental Assessment (EA) has been prepared by the US Department of Energy (DOE) to assess the potential environmental impacts associated with the construction and operation of a landfill and technology center for regionally-generated municipal solid waste at the Savannah River Site (SRS) near Aiken, South Carolina. The facility would serve the municipal solid waste disposal needs for SRS and at least nine of the surrounding counties who currently comprise the Three Rivers Solid Waste Authority (TRSWA). Additional counties could become included in the proposed action at some future date. Current Federal and state requirements do not afford individual counties and municipalities within the region encompassing SRS the ability to efficiently or economically operate modern waste management facilities. In addition, consolidation of regional municipal solid waste at one location would have the benefit of reducing the duplicity of environmental consequences associated with the construction and operation of county-level facilities. The option to seek a combined disposal and technology development facility based on a regionally-cooperative effort was selected as a viable alternative to the existing individual SRS or county disposal activities. This document was prepared in compliance with the National Environmental Policy Act (NEPA) of 1969, as amended, the requirements of the Council on Environmental Quality Regulations for Implementing NEPA (40 CFR Part 1021). NEPA requires the assessment of environmental consequences of Federal actions that may affect the quality of the human environment. Based on the potential for impacts described for impacts described herein, DOE will either publish a Finding of No Significant Impact or prepare an environmental impact statement (EIS).

NONE

1995-12-01T23:59:59.000Z

48

INEOS-New Planet: Indian River Bioenergy Center | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomentheATLANTA,Fermi NationalBusiness Plan Competition |

49

Solar forecasting review  

E-Print Network [OSTI]

and forecasting of solar radiation data: a review,forecasting of solar- radiation data, Solar Energy, vol.sequences of global solar radiation data for isolated sites:

Inman, Richard Headen

2012-01-01T23:59:59.000Z

50

California's Russian River: A Conservation Partnership  

E-Print Network [OSTI]

. Improve weather and river flow forecasting to maximize water captured for reservoirs and fisheries support forecast-based reservoir operations and allow for improved water management. It may also provide's Russian River Habitat Blueprint #12;Restore floodplain habitat through reclamation of abandoned gravel

51

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS DISTRICT FORECASTS  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS DISTRICT FORECASTS IMPROVEMENTS FOR QUEENSLAND across Australia From October 2013, new and improved district forecasts will be introduced in Queensland Protection times FURTHER INFORMATION : www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 PTO> Wind

Greenslade, Diana

52

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1  

E-Print Network [OSTI]

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1 Adrian E. Raftery, Fadoua forecasting often exhibit a spread-skill relationship, but they tend to be underdispersive. This paper of PDFs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal

Washington at Seattle, University of

53

Forecasting sudden changes in environmental pollution patterns  

E-Print Network [OSTI]

Forecasting sudden changes in environmental pollution patterns María J. Olascoagaa,1 and George of Mexico in 2010. We present a methodology to predict major short-term changes in en- vironmental River's mouth in the Gulf of Mexico. The resulting fire could not be extinguished and the drilling rig

Olascoaga, Maria Josefina

54

CENTER  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6Energy, science, andAnalysis1 Solely Supported by CEESScience

55

Using Wikipedia to forecast diseases  

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

Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of...

56

Savannah River Site Eastern Transportation Hub: A Concept For a DOE Eastern Packaging, Staging and Maintenance Center - 13143  

SciTech Connect (OSTI)

The Department of Energy (DOE) is working to de-inventory sites and consolidate hazardous materials for processing and disposal. The DOE administers a wide range of certified shipping packages for the transport of hazardous materials to include Special Nuclear Material (SNM), radioactive materials, sealed sources and radioactive wastes. A critical element to successful and safe transportation of these materials is the availability of certified shipping packages. There are over seven thousand certified packagings (i.e., Type B/Type AF) utilized within the DOE for current missions. The synergistic effects of consolidated maintenance, refurbishment, testing, certification, and costing of these services would allow for efficient management of the packagings inventory and to support anticipated future in-commerce shipping needs. The Savannah River Site (SRS) receives and ships radioactive materials (including SNM) and waste on a regular basis for critical missions such as consolidated storage, stabilization, purification, or disposition using H-Canyon and HB-Line. The Savannah River National Laboratory (SRNL) has the technical capability and equipment for all aspects of packaging management. SRS has the only active material processing facility in the DOE complex and is one of the sites of choice for nuclear material consolidation. SRS is a logical location to perform maintenance and periodic testing of the DOE fleet of certified packagings. This initiative envisions a DOE Eastern Packaging Staging and Maintenance Center (PSMC) at the SRS and a western hub at the Nevada National Security Site (NNSS), an active DOE Regional Disposal Site. The PSMC's would be the first place DOE would go to meet their radioactive packaging needs and the primary locations projects would go to disposition excess packaging for beneficial reuse. These two hubs would provide the centralized management of a packaging fleet rather than the current approach to design, procure, maintain and dispose of packagings on a project-by-project basis. This initiative provides significant savings in packaging costs and acceleration of project schedules. In addition to certified packaging, the PSMC would be well suited for select designs of 7A Type A packaging and Industrial Packaging. (authors)

England, Jeffery L. [Savannah River National Laboratory, Aiken, South Carolina (United States)] [Savannah River National Laboratory, Aiken, South Carolina (United States); Adams, Karen; Maxted, Maxcine; Ruff Jr, Clarence [U.S. Department of Energy, Savannah River Site, Aiken, SC (United States)] [U.S. Department of Energy, Savannah River Site, Aiken, SC (United States); Albenesius, Andrew; Bowers, Mark D.; Fountain, Geoffrey; Hughes, Michael [Savannah River Nuclear Solutions, Aiken, SC (United States)] [Savannah River Nuclear Solutions, Aiken, SC (United States); Gordon, Sydney [National Security Technologies, LLC, Las Vegas, NV (United States)] [National Security Technologies, LLC, Las Vegas, NV (United States); O'Connor, Stephen [U.S. Department of Energy, HQ DOE, EM-33, Germantown MD (United States)] [U.S. Department of Energy, HQ DOE, EM-33, Germantown MD (United States)

2013-07-01T23:59:59.000Z

57

RACORO Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared at 278, 298, and 323 K.Office of Science7a.RACETRACK AT ANL S.

58

Forecasted Opportunities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-SeriesFlickr Flickr Editor's note: Since theNational SupplementalFor

59

Forecast Technical Document Volume Increment  

E-Print Network [OSTI]

Forecast Technical Document Volume Increment Forecasts A document describing how volume increment is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Volume increment forecasts Background A volume increment forecast is a fundamental output of the forecast

60

NREL: Transmission Grid Integration - Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and ResourcesOtherForecasting NREL researchers use solar and

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Error growth in poor ECMWF forecasts over the contiguous United States  

E-Print Network [OSTI]

Successive improvements to the European Center for Medium-range Weather Forecasting model have resulted in improved forecast performance over the Contiguous United States (CONUS). While the overall performance of the model in this region was found...

Modlin, Norman Ray

1993-01-01T23:59:59.000Z

62

ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL  

E-Print Network [OSTI]

5.5 ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL USING RETROSPECTIVE FORECASTS, Colorado 1. INTRODUCTION Improving weather forecasts is a primary goal of the U.S. National Oceanic predictions has been to improve the accuracy of the numerical forecast models. Much effort has been expended

Hamill, Tom

63

Solar forecasting review  

E-Print Network [OSTI]

2.1.2 European Solar Radiation Atlas (ESRA)2.4 Evaluation of Solar Forecasting . . . . . . . . .2.4.1 Solar Variability . . . . . . . . . . . . .

Inman, Richard Headen

2012-01-01T23:59:59.000Z

64

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network [OSTI]

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

65

Technology Forecasting Scenario Development  

E-Print Network [OSTI]

Technology Forecasting and Scenario Development Newsletter No. 2 October 1998 Systems Analysis was initiated on the establishment of a new research programme entitled Technology Forecasting and Scenario and commercial applica- tion of new technology. An international Scientific Advisory Panel has been set up

66

APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY  

E-Print Network [OSTI]

1 APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY Rick Katz.isse.ucar.edu/HP_rick/dmuu.pdf #12;2 QUOTES ON USE OF PROBABILITY FORECASTS · Lao Tzu (Chinese Philosopher) "He who knows does and Value of Probability Forecasts (4) Cost-Loss Decision-Making Model (5) Simulation Example (6) Economic

Katz, Richard

67

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network [OSTI]

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

68

HYDROGEN TECHNOLOGY RESEARCH AT THE SAVANNAH RIVER NATIONAL LABORATORY, CENTER FOR HYDROGEN RESEARCH, AND THE HYDROGEN TECHNOLOGY RESEARCH LABORATORY  

SciTech Connect (OSTI)

The Savannah River National Laboratory (SRNL) is a U.S. Department of Energy research and development laboratory located at the Savannah River Site (SRS) near Aiken, South Carolina. SRNL has over 50 years of experience in developing and applying hydrogen technology, both through its national defense activities as well as through its recent activities with the DOE Hydrogen Programs. The hydrogen technical staff at SRNL comprises over 90 scientists, engineers and technologists, and it is believed to be the largest such staff in the U.S. SRNL has ongoing R&D initiatives in a variety of hydrogen storage areas, including metal hydrides, complex hydrides, chemical hydrides and carbon nanotubes. SRNL has over 25 years of experience in metal hydrides and solid-state hydrogen storage research, development and demonstration. As part of its defense mission at SRS, SRNL developed, designed, demonstrated and provides ongoing technical support for the largest hydrogen processing facility in the world based on the integrated use of metal hydrides for hydrogen storage, separation and compression. The SRNL has been active in teaming with academic and industrial partners to advance hydrogen technology. A primary focus of SRNL's R&D has been hydrogen storage using metal and complex hydrides. SRNL and its Hydrogen Technology Laboratory have been very successful in leveraging their defense infrastructure, capabilities and investments to help solve this country's energy problems. Many of SRNL's programs support dual-use applications. SRNL has participated in projects to convert public transit and utility vehicles for operation on hydrogen fuel. Two major projects include the H2Fuel Bus and an Industrial Fuel Cell Vehicle (IFCV) also known as the GATOR{trademark}. Both of these projects were funded by DOE and cost shared by industry. These are discussed further in Section 3.0, Demonstration Projects. In addition to metal hydrides technology, the SRNL Hydrogen group has done extensive R&D in other hydrogen technologies, including membrane filters for H2 separation, doped carbon nanotubes, storage vessel design and optimization, chemical hydrides, hydrogen compressors and hydrogen production using nuclear energy. Several of these are discussed further in Section 2, SRNL Hydrogen Research and Development.

Danko, E

2007-02-26T23:59:59.000Z

69

Probabilistic manpower forecasting  

E-Print Network [OSTI]

PROBABILISTIC MANPOWER FORECASTING A Thesis JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas ASSAM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May, 1966 Major Subject...: Computer Science and Statistics PROBABILISTIC MANPOWER FORECASTING A Thesis By JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas A@M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May...

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

70

Multivariate Forecast Evaluation And Rationality Testing  

E-Print Network [OSTI]

10621088. MULTIVARIATE FORECASTS Chaudhuri, P. (1996): OnKingdom. MULTIVARIATE FORECASTS Kirchgssner, G. , and U. K.2005): Estimation and Testing of Forecast Rationality under

Komunjer, Ivana; OWYANG, MICHAEL

2007-01-01T23:59:59.000Z

71

3, 21452173, 2006 Probabilistic forecast  

E-Print Network [OSTI]

HESSD 3, 2145­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page for probabilistic forecasts of continuous hydrological variables F. Laio and S. Tamea DITIC ­ Department­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page Abstract Introduction

Paris-Sud XI, Université de

72

4, 189212, 2007 Forecast and  

E-Print Network [OSTI]

OSD 4, 189­212, 2007 Forecast and analysis assessment through skill scores M. Tonani et al. Title Science Forecast and analysis assessment through skill scores M. Tonani 1 , N. Pinardi 2 , C. Fratianni 1 Forecast and analysis assessment through skill scores M. Tonani et al. Title Page Abstract Introduction

Paris-Sud XI, Université de

73

Forecast Technical Document Technical Glossary  

E-Print Network [OSTI]

Forecast Technical Document Technical Glossary A document defining some of the terms used in the 2011 Production Forecast technical documentation. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the Forecast documentation. In some cases, the terms and the descriptions are "industry standard", in others

74

Forecast Technical Document Tree Species  

E-Print Network [OSTI]

Forecast Technical Document Tree Species A document listing the tree species included in the 2011 Production Forecast Tom Jenkins Justin Gilbert Ewan Mackie Robert Matthews #12;PF2011 ­ List of tree species The following is the list of species used within the Forecast System. Species are ordered alphabetically

75

TRAVEL DEMAND AND RELIABLE FORECASTS  

E-Print Network [OSTI]

TRAVEL DEMAND AND RELIABLE FORECASTS FOR TRANSIT MARK FILIPI, AICP PTP 23rd Annual Transportation transportation projects § Develop and maintain Regional Travel Demand Model § Develop forecast socio in cooperative review during all phases of travel demand forecasting 4 #12;Cooperative Review Should Include

Minnesota, University of

76

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

in the consensus forecast produced in 2006, primarily from the decreased demand as a result of the current nationalConsensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks

Mohaghegh, Shahab

77

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

78

Demand Forecasting of New Products  

E-Print Network [OSTI]

Demand Forecasting of New Products Using Attribute Analysis Marina Kang A thesis submitted Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock upon currently employed new-SKU demand forecasting methods which involve the processing of large

Sun, Yu

79

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

EMGT 835 FIELD PROJECT: Improving Inventory Control Using Forecasting By Juan Mario Balandran jmbg@hotmail.com Master of Science The University of Kansas Fall Semester, 2005 An EMGT Field Project report submitted...............................................................................................................................................10 Current Inventory Forecast Process ...........................................................................................10 Development of Alternative Forecast Process...

Balandran, Juan

2005-12-16T23:59:59.000Z

80

Wind Forecasting Improvement Project | Department of Energy  

Office of Environmental Management (EM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomen Owned SmallOf TheViolations | Department of EnergyisWilliamForecasting

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Renewable Forecast Min-Max2020.xls  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared at 278, 298,NIST31 ORV 15051SoilWind Energy Wind RenewableForecast

82

Forecast calls for better models | EMSL  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-SeriesFlickr Flickr Editor's note: Since theNational SupplementalFor theForecast

83

NREL: Resource Assessment and Forecasting - Facilities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and forecasting

84

NREL: Resource Assessment and Forecasting - Research Staff  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and forecastingResearch

85

Using Bayesian Model Averaging to Calibrate Forecast Ensembles ADRIAN E. RAFTERY, TILMANN GNEITING, FADOUA BALABDAOUI, AND MICHAEL POLAKOWSKI  

E-Print Network [OSTI]

Using Bayesian Model Averaging to Calibrate Forecast Ensembles ADRIAN E. RAFTERY, TILMANN GNEITING for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive centered on the individual bias-corrected forecasts, where the weights are equal to posterior probabilities

Raftery, Adrian

86

Fuel Price Forecasts INTRODUCTION  

E-Print Network [OSTI]

Fuel Price Forecasts INTRODUCTION Fuel prices affect electricity planning in two primary ways and water heating, and other end-uses as well. Fuel prices also influence electricity supply and price because oil, coal, and natural gas are potential fuels for electricity generation. Natural gas

87

Solar forecasting review  

E-Print Network [OSTI]

Quantifying PV power output variability, Solar Energy, vol.each solar sen at node i, P(t) the total power output of theSolar Forecasting Historically, traditional power generation technologies such as fossil and nu- clear power which were designed to run in stable output

Inman, Richard Headen

2012-01-01T23:59:59.000Z

88

NOAA Technical Memorandum ERL PMEL-114 Offshore forecasting of Hawaiian tsunamis  

E-Print Network [OSTI]

Disaster Center (PDC). The activity included analytical and nu- merical sensitivity studies of tsunami wave the Pacific Disaster Center (PDC) with tsunami forecasting capabilities. PDC is a joint Department of Defense

89

Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting  

E-Print Network [OSTI]

Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Ed.. Editor: Jan

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

90

Savannah River | Department of Energy  

Energy Savers [EERE]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartment of Energy fromCommentsRevolving STATEMENTSavannah River Site Savannah RiverSite

91

Forecasting oilfield economic performance  

SciTech Connect (OSTI)

This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended.

Bradley, M.E. (Univ. of Chicago, IL (United States)); Wood, A.R.O. (BP Exploration, Anchorage, AK (United States))

1994-11-01T23:59:59.000Z

92

River Thames River Thames  

E-Print Network [OSTI]

West Kent House Penge East Lower Sydenham Forest Hill Honor Oak Park Crofton Park Nunhead New CrossC BD A River Thames River Thames Waterloo & City Southwark Northwood Northwood Hills North Harrow Harrow- on-the-Hill Northwick Park Harrow & Wealdstone Headstone Lane Pinner Kenton Stanmore Canons Park

Delmotte, Nausicaa

93

Forecast Technical Document Growing Stock Volume  

E-Print Network [OSTI]

Forecast Technical Document Growing Stock Volume Forecasts A document describing how growing stock (`standing') volume is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Growing stock volume forecasts Background A forecast of standing volume (or

94

NOAA Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps  

E-Print Network [OSTI]

Forecast System Southwest Florida Forecast Region Maps 0 20 4010 Miles #12;Bay-S Pinellas Bay-UPR Bay Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12;Bay Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12

95

Price forecasting for notebook computers.  

E-Print Network [OSTI]

??This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a (more)

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

96

UWIG Forecasting Workshop -- Albany (Presentation)  

SciTech Connect (OSTI)

This presentation describes the importance of good forecasting for variable generation, the different approaches used by industry, and the importance of validated high-quality data.

Lew, D.

2011-04-01T23:59:59.000Z

97

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL

98

Conservation The Northwest ForecastThe Northwest Forecast  

E-Print Network [OSTI]

& Resources Creating Mr. Toad's Wild Ride for the PNW's Energy Efficiency InCreating Mr. Toad's Wild RideNorthwest Power and Conservation Council The Northwest ForecastThe Northwest Forecast Energy EfficiencyEnergy Efficiency Dominates ResourceDominates Resource DevelopmentDevelopment Tom EckmanTom Eckman

99

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network [OSTI]

· NATIONAL AND GLOBAL FORECASTS · WEST VIRGINIA PROFILES AND FORECASTS · ENERGY · HEALTHCARE Research West Virginia University College of Business and Economics P.O. Box 6527, Morgantown, WV 26506 EXPERT OPINION PROVIDED BY Keith Burdette Cabinet Secretary West Virginia Department of Commerce

Mohaghegh, Shahab

100

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network [OSTI]

1 CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST QUALITY: EVIDENCE FROM FRENCH IPOS Anis attributes, ownership retained, auditor quality, and underwriter reputation and management earnings forecast quality measured by management earnings forecast accuracy and bias. Using 117 French IPOs, we find

Paris-Sud XI, Université de

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2.............................................................................................. 10 #12;Introduction and Background This document describes staff's updated 2007 peak demand forecasts

102

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product estimates. Margaret Sheridan provided the residential forecast. Mitch Tian prepared the peak demand

103

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined provided estimates for demand response program impacts and contributed to the residential forecast. Mitch

104

2009 CAPS Spring Forecast Program Plan  

E-Print Network [OSTI]

package. · Two 18 UTC update forecasts on demand basis, with the same domain and configuration, running2009 CAPS Spring Forecast Experiment Program Plan April 20, 2009 #12;2 Table of Content 1. Overview .......................................................................................................4 3. Forecast System Configuration

Droegemeier, Kelvin K.

105

he long-term economic forecast calls for the continuation of the  

E-Print Network [OSTI]

T he long-term economic forecast calls for the continuation of the economic recovery in 2014 predicts a steady economic recovery for Southern Nevada from 2014 onward. The Las Vegas economy-Term Economic Forecast Figure 1: Total Employment (1990-2050) Source: Center for Business and Economic Research

Hemmers, Oliver

106

Sixth Northwest Conservation and Electric Power Plan Appendix D: Wholesale Electricity Price Forecast  

E-Print Network [OSTI]

Forecast Introduction.................................................................................................................................... 6 Demand................................................................... 16 The Base Case Forecast

107

Electricity price forecasting in a grid environment.  

E-Print Network [OSTI]

??Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate (more)

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

108

Regional-seasonal weather forecasting  

SciTech Connect (OSTI)

In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

1980-08-01T23:59:59.000Z

109

Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast uncertainty  

E-Print Network [OSTI]

Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast structures Stochastic trajectory Stochastic FTLE field Ensemble forecasting Uncertainty analysis a b s t r of the forecast FTLE fields is analyzed using ensemble forecasting. Unavoidable errors of the forecast velocity

Ross, Shane

110

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

Analysis Integrating agricultural pest biocontrol into forecasts of energy biomass production T), University of Lome, 114 Rue Agbalepedogan, BP: 20679, Lome, Togo e Center for Agricultural & Energy Policy model of potential biomass supply that incorporates the effect of biological control on crop choice

Gratton, Claudio

111

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

1 PROBLEMS OF FORECAST1 Dmitry KUCHARAVY dmitry.kucharavy@insa-strasbourg.fr Roland DE GUIO roland for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods of the forecast errors. Second, using a contradiction analysis, a set of problems related to technology forecast

Paris-Sud XI, Université de

112

Using reforecasts for probabilistic forecast calibration  

E-Print Network [OSTI]

1 Using reforecasts for probabilistic forecast calibration Tom Hamill NOAA Earth System Research that is currently operational. #12;3 Why compute reforecasts? · For many forecast problems, such as long-lead forecasts or high-precipitation events, a few past forecasts may be insufficient for calibrating

Hamill, Tom

113

Forecast Combination With Outlier Protection Gang Chenga,  

E-Print Network [OSTI]

Forecast Combination With Outlier Protection Gang Chenga, , Yuhong Yanga,1 a313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455 Abstract Numerous forecast combination schemes with distinct on combining forecasts with minimizing the occurrence of forecast outliers in mind. An unnoticed phenomenon

Yuhong, Yang

114

Forecast Technical Document Felling and Removals  

E-Print Network [OSTI]

Forecast Technical Document Felling and Removals Forecasts A document describing how volume fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Felling and removals forecasts Background A fellings and removals

115

Assessing Forecast Accuracy Measures Department of Economics  

E-Print Network [OSTI]

Assessing Forecast Accuracy Measures Zhuo Chen Department of Economics Heady Hall 260 Iowa State forecast accuracy measures. In the theoretical direction, for comparing two forecasters, only when the errors are stochastically ordered, the ranking of the forecasts is basically independent of the form

116

Load Forecast For use in Resource Adequacy  

E-Print Network [OSTI]

-term Electricity Demand Forecasting System 1) Obtain Daily Regional Temperatures 6) Estimate Daily WeatherLoad Forecast 2019 For use in Resource Adequacy Massoud Jourabchi #12;In today's presentation d l­ Load forecast methodology ­ Drivers of the forecast f i­ Treatment of conservation

117

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

118

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network [OSTI]

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

119

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network [OSTI]

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work Sheridan provided the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid

120

Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model  

E-Print Network [OSTI]

1 1 Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model 2007 Federal prices are being inputted into the model 2 Sales forecast Select yearsSales forecast Select years --Draft 0.53% Irrigation 2.76% Annual Growth Rates Preliminary Electricity ForecastAnnual Growth Rates

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Can earnings forecasts be improved by taking into account the forecast bias?  

E-Print Network [OSTI]

Can earnings forecasts be improved by taking into account the forecast bias? François DOSSOU allow the calculation of earnings adjusted forecasts, for horizons from 1 to 24 months. We explain variables. From the forecast evaluation statistics viewpoints, the adjusted forecasts make it possible quasi

Paris-Sud XI, Université de

122

Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts  

E-Print Network [OSTI]

Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts April 14, 2009 Massoud,000 MW #12;6 Demand Forecasts Price Effect (prior to conservation) - 5,000 10,000 15,000 20,000 25,000 30 Jourabchi #12;2 Changes since the Last Draft ForecastChanges since the Last Draft Forecast Improved

123

Efficient market model: within-sample fit versus out-of-sample forecasts  

E-Print Network [OSTI]

has been the center of considerable attention in the applied econometric literature. The criterion Predictive Least Squares (PLS) based on actual postsample forecasting performance is proposed to identify a time series model. The criterion is applied...

Cheng, Chi

1993-01-01T23:59:59.000Z

124

Stochastic Models Applied to Operation of Reservoirs in the Upper Colorado River Basin in Texas  

E-Print Network [OSTI]

river basin. The model is entitled "Monthly Operational Hydrometeorological Simulator (MOHS)." Use of the 30-day meteorological forecast categories of light, moderate, or heavy precipitation and below normal, near normal, or above normal temperature...

Clark, R. A.; O'Connor, G. E.; Curry, G. L.; Helm, J. C.

125

Price forecasting for notebook computers  

E-Print Network [OSTI]

This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a series of time periods, and the rates of change in the influence...

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

126

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN with primary contributions in the area of decision support for reservoir planning and management Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project

127

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN: California Energy Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL RESEARCH Martha

128

Value of Wind Power Forecasting  

SciTech Connect (OSTI)

This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts.

Lew, D.; Milligan, M.; Jordan, G.; Piwko, R.

2011-04-01T23:59:59.000Z

129

CALIFORNIA ENERGY COMMISSION0 Annual Update to the Forecasted  

E-Print Network [OSTI]

Values in TWh forthe Year2022 Formula Mid Demand Forecast Renewable Net High Demand Forecast Renewable Net Low Demand Forecast Renewable Net #12;CALIFORNIA ENERGY COMMISSION5 Demand Forecast · Retail Sales Forecast from California Energy Demand 2012 2022(CED 2011), Adopted Forecast* ­ Form 1.1c · Demand Forecast

130

Weather forecasting : the next generation : the potential use and implementation of ensemble forecasting  

E-Print Network [OSTI]

This thesis discusses ensemble forecasting, a promising new weather forecasting technique, from various viewpoints relating not only to its meteorological aspects but also to its user and policy aspects. Ensemble forecasting ...

Goto, Susumu

2007-01-01T23:59:59.000Z

131

Wind Forecast Improvement Project Southern Study Area Final Report...  

Office of Environmental Management (EM)

Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern...

132

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007 INTEGRATED Table of Contents General Instructions for Demand Forecast Submittals.............................................................................. 4 Protocols for Submitted Demand Forecasts

133

Applying Bayesian Forecasting to Predict New Customers' Heating Oil Demand.  

E-Print Network [OSTI]

??This thesis presents a new forecasting technique that estimates energy demand by applying a Bayesian approach to forecasting. We introduce our Bayesian Heating Oil Forecaster (more)

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

134

Wind Power Forecasting Data  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear SecurityTensile Strain Switched FerromagnetismWaste and MaterialsWenjun DengWISPWind Industry Soars to New1Wind

135

Wind Power Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered energy consumption byAbout PrintableBlenderWhatFellows - PastFarmWind

136

Savannah River Site | Department of Energy  

Energy Savers [EERE]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartment of Energy fromCommentsRevolving STATEMENTSavannah River Site Savannah River Site

137

Savannah River Site | Department of Energy  

Energy Savers [EERE]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartment of Energy fromCommentsRevolving STATEMENTSavannah River Site Savannah River

138

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01T23:59:59.000Z

139

Electric Load Forecasting  

E-Print Network [OSTI]

-commitment, coordination, and interchange evaluation. In addition, the liberalization of electric energy markets worldwide has led to the development of energy exchanges where consumers, 1066-033X/07/$25.002007IEEE OCTOBER/GODDARD SPACE FLIGHT CENTER SCIENTIFIC VISUALIZATION STUDIO Authorized licensed use limited to: Katholieke

140

Management forecast credibility and underreaction to news  

E-Print Network [OSTI]

In this paper, we first document evidence of underreaction to management forecast news. We then hypothesize that the credibility of the forecast influences the magnitude of this underreaction. Relying on evidence that more ...

Ng, Jeffrey

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Management Forecast Quality and Capital Investment Decisions  

E-Print Network [OSTI]

Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable ...

Goodman, Theodore H.

142

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

FORECASTING THE ROLE OF RENEWABLES IN HAWAII Jayant SathayeFORECASTING THE ROLF OF RENEWABLES IN HAWAII J Sa and Henrythe Conservation Role of Renewables November 18, 1980 Page 2

Sathaye, Jayant

2013-01-01T23:59:59.000Z

143

5, 183218, 2008 A rainfall forecast  

E-Print Network [OSTI]

HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N. Q. Hung et al An artificial neural network model for rainfall forecasting in Bangkok, Thailand N. Q. Hung, M. S. Babel, S Geosciences Union. 183 #12;HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N

Paris-Sud XI, Université de

144

Ensemble Forecast of Analyses With Uncertainty Estimation  

E-Print Network [OSTI]

Ensemble Forecast of Analyses With Uncertainty Estimation Vivien Mallet1,2, Gilles Stoltz3 2012 Mallet, Stoltz, Zhuk, Nakonechniy Ensemble Forecast of Analyses November 2012 1 / 14 hal-00947755,version1-21Feb2014 #12;Objective To produce the best forecast of a model state using a data assimilation

Boyer, Edmond

145

(1) Ensemble forecast calibration & (2) using reforecasts  

E-Print Network [OSTI]

1 (1) Ensemble forecast calibration & (2) using reforecasts Tom Hamill NOAA Earth System Research · Calibration: ; the statistical adjustment of the (ensemble) forecast ­ Rationale 1: Infer large-sample probabilities from small ensemble. ­ Rationale 2: Remove bias, increase forecast reliability while preserving

Hamill, Tom

146

Load forecast and treatment of conservation  

E-Print Network [OSTI]

conservation is implicitly incorporated in the short-term demand forecast? #12;3 Incorporating conservationLoad forecast and treatment of conservation July 28th 2010 Resource Adequacy Technical Committee in the short-term model Our short-term model is an econometric model which can not explicitly forecast

147

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity forecast is the combined product of the hard work and expertise of numerous staff members in the Demand prepared the peak demand forecast. Ravinderpal Vaid provided the projections of commercial floor space

148

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

149

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy for demand response program impacts and contributed to the residential forecast. Mitch Tian prepared

150

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work provided estimates for demand response program impacts and contributed to the residential forecast. Mitch

151

Consensus Coal Production And Price Forecast For  

E-Print Network [OSTI]

Consensus Coal Production And Price Forecast For West Virginia: 2011 Update Prepared for the West December 2011 Copyright 2011 WVU Research Corporation #12;#12;W.Va. Consensus Coal Forecast Update 2011 i Table of Contents Executive Summary 1 Recent Developments 3 Consensus Coal Production And Price Forecast

Mohaghegh, Shahab

152

Electric Grid - Forecasting system licensed | ornl.gov  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation Proposed NewcatalystNeutronEnvironmentZIRKLEEFFECTS OFElaineElectric Grid - Forecasting system

153

NREL: Resource Assessment and Forecasting - Data and Resources  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and forecasting research

154

A first large-scale flood inundation forecasting model  

SciTech Connect (OSTI)

At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.

Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

2013-11-04T23:59:59.000Z

155

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 Gorin Principal Authors Lynn Marshall Project Manager Kae C. Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency and Demand Analysis Division Scott W. Matthews Acting

156

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

energy system. Observed refrigeration load and local ambient temperature from a Danish su- permarket renewable energy, is increasing, therefore a flexible energy system is needed. In the present ThesisLoad Forecasting of Supermarket Refrigeration Lisa Buth Rasmussen Kongens Lyngby 2013 M.Sc.-2013

157

Savannah River Site: Plutonium Preparation Project (PuPP) at Savannah River  

Energy Savers [EERE]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartment of Energy fromCommentsRevolving STATEMENTSavannah River Site Savannah RiverSite |

158

Forecasting Distributions with Experts Advice  

E-Print Network [OSTI]

) is the probability forecast based on an arbitrary vector wE in the unit simplex, experts forecasts ?E , and model {p?} . Remark 2 In most cases, we can choose c = 1/?, implying in the result below that c? = 1. Example 3 The prediction function is a mixture... 0 = 1, and #IT (k) = tk+1 ? tk. Define ek ? E. Theorem 12 Under Conditions 1 and 7, R1,...,t (pW ) ? c? K? k=0 Rt(k),...,t(k+1)?1 ( p?(e(k)) ) + c ln (#E) ?c K? k=1 ln ut(k) (ek, ek?1)? c K? k=0 t(k+1)?2? s=t(k) ln (us+1 (ek, ek)) . 9 Remark 13...

Sancetta, Alessio

2006-03-14T23:59:59.000Z

159

Forecasting wind speed financial return  

E-Print Network [OSTI]

The prediction of wind speed is very important when dealing with the production of energy through wind turbines. In this paper, we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model that has been shown to be able to reproduce accurately the statistical behavior of wind speed. The model is used to forecast, one step ahead, wind speed. In order to check the validity of the model we show, as indicator of goodness, the root mean square error and mean absolute error between real data and predicted ones. We also compare our forecasting results with those of a persistence model. At last, we show an application of the model to predict financial indicators like the Internal Rate of Return, Duration and Convexity.

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

160

A Walking Tour Health Center  

E-Print Network [OSTI]

and caring. #12;3. River of Ponds Tapestry Enter the Health Center, look to the left, and see the massiveHealing Pathways A Walking Tour of the UConn Health Center Public Art Collection Made possible by a generous gift from the Health Center Auxiliary #12;#12;Welcome! Everywhere you walk, whether outdoors

Oliver, Douglas L.

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Geothermal wells: a forecast of drilling activity  

SciTech Connect (OSTI)

Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.

Brown, G.L.; Mansure, A.J.; Miewald, J.N.

1981-07-01T23:59:59.000Z

162

Online Forecast Combination for Dependent Heterogeneous Data  

E-Print Network [OSTI]

the single individual forecasts. Several studies have shown that combining forecasts can be a useful hedge against structural breaks, and forecast combinations are often more stable than single forecasts (e.g. Hendry and Clements, 2004, Stock and Watson, 2004... in expectations. Hence, we have the following. Corollary 4 Suppose maxt?T kl (Yt, hwt,Xti)kr ? A taking expectation on the left hand side, adding 2A ? T and setting ? = 0 in mT (?), i.e. TX t=1 E [lt (wt)? lt (ut...

Sancetta, Alessio

163

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

164

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

165

Solid low-level waste forecasting guide  

SciTech Connect (OSTI)

Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford`s experience within the last six years. Hanford`s forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford`s annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford`s forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data.

Templeton, K.J.; Dirks, L.L.

1995-03-01T23:59:59.000Z

166

The Value of Wind Power Forecasting  

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

Wind Power Forecasting Preprint Debra Lew and Michael Milligan National Renewable Energy Laboratory Gary Jordan and Richard Piwko GE Energy Presented at the 91 st American...

167

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations  

E-Print Network [OSTI]

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations Audun Botterud://www.dis.anl.gov/projects/windpowerforecasting.html IAWind 2010 Ames, IA, April 6, 2010 #12;Outline Background Using wind power forecasts in market operations ­ Current status in U.S. markets ­ Handling uncertainties in system operations ­ Wind power

Kemner, Ken

168

U-M Construction Forecast December 15, 2011 U-M Construction Forecast  

E-Print Network [OSTI]

U-M Construction Forecast December 15, 2011 U-M Construction Forecast Spring Fall 2012 As of December 15, 2011 Prepared by AEC Preliminary & Advisory #12;U-M Construction Forecast December 15, 2011 Overview Campus by campus Snapshot in time Not all projects Construction coordination efforts

Kamat, Vineet R.

169

NOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future)  

E-Print Network [OSTI]

conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through are generated every 6 hours and you can step backward in hourly increments to view conditions over the previousNOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future) and Nowcasts

170

Pennsylvania Scenic Rivers Program  

Broader source: Energy.gov [DOE]

Rivers included in the Scenic Rivers System will be classified, designated and administered as Wild, Scenic, Pastoral, Recreational and Modified Recreational Rivers (Sections 4; (a) (1) of the...

171

Optimal combined wind power forecasts using exogeneous variables  

E-Print Network [OSTI]

Optimal combined wind power forecasts using exogeneous variables Fannar ¨Orn Thordarson Kongens of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

172

Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation  

E-Print Network [OSTI]

Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation Vivien Mallet1. [1] Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: The weights

Mallet, Vivien

173

Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging  

E-Print Network [OSTI]

is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty canProbabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging J. Mc discretization than is seen in other weather quantities. The prevailing paradigm in weather forecasting

Washington at Seattle, University of

174

Coordinating production quantities and demand forecasts through penalty schemes  

E-Print Network [OSTI]

Coordinating production quantities and demand forecasts through penalty schemes MURUVVET CELIKBAS1 departments which enable organizations to match demand forecasts with production quantities. This research problem where demand is uncertain and the marketing de- partment provides a forecast to manufacturing

Swaminathan, Jayashankar M.

175

CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Demand Forecast report is the product of the efforts of many current and former California Energy-2 Demand Forecast Disaggregation......................................................1-4 Statewide

176

HIERARCHY OF PRODUCTION DECISIONS Forecasts of future demand  

E-Print Network [OSTI]

HIERARCHY OF PRODUCTION DECISIONS Forecasts of future demand Aggregate plan Master production Planning and Forecast Bias · Forecast error seldom is normally distributed · There are few finite planning

Brock, David

177

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc in demand forecasting for new communication services. Acknowledgments: The writing of this paper commenced employers or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica- tions

Parsons, Simon

178

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

has developed longterm forecasts of transportation energy demand as well as projected ranges of transportation fuel and crude oil import requirements. The transportation energy demand forecasts makeCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY

179

Infrastructure Assurance Center  

E-Print Network [OSTI]

and impact-analysis tool developed by Argonne National Laboratory. This powerful tool allows for rapid, first in which flooding of the Savannah River along the South Carolina/Georgia border caused a 25% reduction Assurance Center Our nation relies on natural gas to meet about 22% of its energy needs. Within the next 10

Kemner, Ken

180

Arkansas Water Resources Center  

E-Print Network [OSTI]

:L\\faps 1.1Siteswhere fish (largemouth bass)tissueHg concentrationwere detetned(from 194 i riversArkansas Water Resources Center SPATIAL ANALYSIS OF THE CAUSE OF MERCURY CONTAMINATION OF FISH have beenconcernsabout mercury (Hg) contaminationin fish in Arkansas sincethe discovFryof the problem

Soerens, Thomas

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

P9.137 The SPC Storm-Scale Ensemble of Opportunity: Overview and Results from the 2012 Hazardous Weather Testbed Spring Forecasting Experiment  

E-Print Network [OSTI]

P9.137 The SPC Storm-Scale Ensemble of Opportunity: Overview and Results from the 2012 Hazardous) available to forecasters at the Storm Prediction Center (SPC) has been increasing over the past few years to examine and scrutinize the data in creating a forecast has not changed. Thus, the concept of the SPC Storm

182

The Wind Forecast Improvement Project (WFIP): A Public/Private...  

Energy Savers [EERE]

The Wind Forecast Improvement Project (WFIP): A PublicPrivate Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The...

183

Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo  

E-Print Network [OSTI]

Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo Oldenburg University have been presented more than twenty years ago (Jensenius, 1981), when daily solar radiation forecasts

Heinemann, Detlev

184

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

185

Inverse Modelling to Forecast Enclosure Fire Dynamics  

E-Print Network [OSTI]

. This thesis proposes and studies a method to use measurements of the real event in order to steer and accelerate fire simulations. This technology aims at providing forecasts of the fire development with a positive lead time, i.e. the forecast of future events...

Jahn, Wolfram

186

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near-surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

187

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near­surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

188

Nonparametric models for electricity load forecasting  

E-Print Network [OSTI]

Electricity consumption is constantly evolving due to changes in people habits, technological innovations1 Nonparametric models for electricity load forecasting JANUARY 23, 2015 Yannig Goude, Vincent at University Paris-Sud 11 Orsay. His research interests are electricity load forecasting, more generally time

Genève, Université de

189

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

deficits. After solid 3% growth this year, real GDP growth will recede a bit for the next two years. New household spending. Real GDP will firm above 3% in 2015. · The pace of growth in China has continuedUHERO FORECAST PROJECT DECEMBER 5, 2014 Asia-Pacific Forecast: Press Version: Embargoed Until 2

190

-Assessment of current water conditions -Precipitation Forecast  

E-Print Network [OSTI]

#12;-Assessment of current water conditions - Precipitation Forecast - Recommendations for Drought of the mountains, so early demand for irrigation water should be minimal as we officially move into spring. Western, it is forecast to bring wet snow to the eastern slope of the Rockies, with less accumulations west of the divide

191

A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS  

E-Print Network [OSTI]

APPROACH FOR EVALUATING ECONOMIC FORECASTS Tara M. Sinclair , H.O. Stekler, and Warren Carnow Department of Economics The George Washington University Monroe Hall #340 2115 G Street NW Washington, DC 20052 JEL Codes, Mahalanobis Distance Abstract This paper presents a new approach to evaluating multiple economic forecasts

Vertes, Akos

192

2013 Midyear Economic Forecast Sponsorship Opportunity  

E-Print Network [OSTI]

2013 Midyear Economic Forecast Sponsorship Opportunity Thursday, April 18, 2013, ­ Hyatt Regency Irvine 11:30 a.m. ­ 1:30 p.m. Dr. Anil Puri presents his annual Midyear Economic Forecast addressing and Economics at California State University, Fullerton, the largest accredited business school in California

de Lijser, Peter

193

1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.  

SciTech Connect (OSTI)

This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

194

Savannah River Technology Center monthly report  

SciTech Connect (OSTI)

This report for the month of June 1995 presents information on the typical topics of tritium processing, environmental studies, waste management issues, and miscellaneous projects. The document consists many small reports by individuals or small groups.

NONE

1995-06-01T23:59:59.000Z

195

Earthquake Forecast via Neutrino Tomography  

E-Print Network [OSTI]

We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. Antineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations in the matter effect on the antineutrino oscillation would provide a tomography of the vicinity of the region. In this preliminary work, we adopt a simplified model for the geometrical profile and matter density in a fault zone. We calculate the survival probability of electron antineutrinos for cases without and with an anomalous accumulation of electrons which can be considered as a clear signal of the coming earthquake, at the geological region with a fault zone, and find that the variation may reach as much as 3% for $\\bar \

Bin Wang; Ya-Zheng Chen; Xue-Qian Li

2011-03-29T23:59:59.000Z

196

MSSM Forecast for the LHC  

E-Print Network [OSTI]

We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of $M_Z$ is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on $e^+e^-$ data) is considered, the preferred region (for $\\mu>0$) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-$\\mu$ possibilities.

Maria Eugenia Cabrera; Alberto Casas; Roberto Ruiz de Austri

2010-12-10T23:59:59.000Z

197

Short Term Hourly Load Forecasting Using Abductive Networks R. E. Abdel-Aal  

E-Print Network [OSTI]

Short Term Hourly Load Forecasting Using Abductive Networks R. E. Abdel-Aal Center for Applied for this purpose. This paper proposes using the alternative technique of abductive networks, which offers with statistical and empirical models. Using hourly temperature and load data for five years, 24 dedicated models

Abdel-Aal, Radwan E.

198

Agency. Any Federal agency or organization participating in the tropical cyclone forecasting and warning service.  

E-Print Network [OSTI]

cyclone forecasting and warning service. Airport Radar Service Area (ARSA). Regulatory airspace/IFR-standard IFR separation; IFR/VFR-traffic advisories and conflict resolution; and VFR/VFR-traffic advisories and of the center of a tropical or subtropical cyclone obtained by means other than reconnaissance aircraft

199

Forecasting Water Quality & Biodiversity  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:YearRound-UpHeat PumpRecordFederal7.pdfFlash_2010_-24.pdfOverview FlowControlIndian1 DOEForecasting

200

Pea River Electric Coop | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories onFocusOski Energy LLCPascoag Utility DistrictPea River Electric

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Green River Biodiesel Incorporated | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to: navigation,Ohio: EnergyGrasslands RenewableGreatwood,GreenFalls,Group LtdGreen River

202

Raft River Geothermal Facility | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColoradosource HistoryRaft River Sector Geothermal energy

203

Powder River Energy Corporation | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy ResourcesLoadingPenobscot County, Maine:Plug Power IncPowder River Energy Corporation Place: Sundance,

204

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

This report, which updates WHC-EP-0567, 1992 Solid Waste Reference Forecast Summary, (WHC 1992) forecasts the volumes of solid wastes to be generated or received at the US Department of Energy Hanford Site during the 30-year period from FY 1993 through FY 2022. The data used in this document were collected from Westinghouse Hanford Company forecasts as well as from surveys of waste generators at other US Department of Energy sites who are now shipping or plan to ship solid wastes to the Hanford Site for disposal. These wastes include low-level and low-level mixed waste, transuranic and transuranic mixed waste, and nonradioactive hazardous waste.

Valero, O.J.; Blackburn, C.L. [Westinghouse Hanford Co., Richland, WA (United States); Kaae, P.S.; Armacost, L.L.; Garrett, S.M.K. [Pacific Northwest Lab., Richland, WA (United States)

1993-08-01T23:59:59.000Z

205

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

PSO (FU 2101) Ensemble-forecasts for wind power Analysis of the Results of an On-line Wind Power Ensemble- forecasts for wind power (FU2101) a demo-application producing quantile forecasts of wind power correct) quantile forecasts of the wind power production are generated by the application. However

206

A New Measure of Earnings Forecast Uncertainty Xuguang Sheng  

E-Print Network [OSTI]

A New Measure of Earnings Forecast Uncertainty Xuguang Sheng American University Washington, D of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations

Kim, Kiho

207

AN ANALYSIS OF FORECAST BASED REORDER POINT POLICIES : THE BENEFIT  

E-Print Network [OSTI]

AN ANALYSIS OF FORECAST BASED REORDER POINT POLICIES : THE BENEFIT OF USING FORECASTS Mohamed Zied Ch^atenay-Malabry Cedex, France Abstract: In this paper, we analyze forecast based inventory control policies for a non-stationary demand. We assume that forecasts and the associated uncertainties are given

Paris-Sud XI, Université de

208

The Complexity of Forecast Testing Lance Fortnow # Rakesh V. Vohra +  

E-Print Network [OSTI]

The Complexity of Forecast Testing Lance Fortnow # Rakesh V. Vohra + Abstract Consider a weather forecaster predicting a probability of rain for the next day. We consider tests that given a finite sequence of forecast predictions and outcomes will either pass or fail the forecaster. Sandroni shows that any test

Fortnow, Lance

209

Does increasing model stratospheric resolution improve extended range forecast skill?  

E-Print Network [OSTI]

Does increasing model stratospheric resolution improve extended range forecast skill? Greg Roff,1 forecast skill at high Southern latitudes is explored. Ensemble forecasts are made for two model configurations that differ only in vertical resolution above 100 hPa. An ensemble of twelve 30day forecasts

210

Distribution of Wind Power Forecasting Errors from Operational Systems (Presentation)  

SciTech Connect (OSTI)

This presentation offers new data and statistical analysis of wind power forecasting errors in operational systems.

Hodge, B. M.; Ela, E.; Milligan, M.

2011-10-01T23:59:59.000Z

211

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-Print Network [OSTI]

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural demand time series based only on data for six years to forecast the demand for the seventh year. Both networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system

Abdel-Aal, Radwan E.

212

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022  

E-Print Network [OSTI]

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA or adequacy of the information in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

213

Strategic safety stocks in supply chains with evolving forecasts  

E-Print Network [OSTI]

we have an evolving demand forecast. Under assumptions about the forecasts, the demand process their supply chain operations based on a forecast of future demand over some planning horizon. Furthermore stock inventory in a supply chain that is subject to a dynamic, evolving demand forecast. In particular

Graves, Stephen C.

214

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST Energy Demand 2008-2018 forecast supports the analysis and recommendations of the 2007 Integrated Energy Commission demand forecast models. Both the staff draft energy consumption and peak forecasts are slightly

215

Metrics for Evaluating the Accuracy of Solar Power Forecasting (Presentation)  

SciTech Connect (OSTI)

This presentation proposes a suite of metrics for evaluating the performance of solar power forecasting.

Zhang, J.; Hodge, B.; Florita, A.; Lu, S.; Hamann, H.; Banunarayanan, V.

2013-10-01T23:59:59.000Z

216

SolarAnywhere forecast (Perez & Hoff) This chapter describes, and presents an evaluation of, the forecast models imbedded in the  

E-Print Network [OSTI]

SolarAnywhere forecast (Perez & Hoff) ABSTRACT This chapter describes, and presents an evaluation of, the forecast models imbedded in the SolarAnywhere platform. The models include satellite derived cloud motion based forecasts for the short to medium horizon (1 5 hours) and forecasts derived from NOAA

Perez, Richard R.

217

Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting  

E-Print Network [OSTI]

J.B. , 2004: Probabilistic wind power forecasts using localforecast intervals for wind power output using NWP-predictedsources such as wind and solar power. Integration of this

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

218

Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting  

E-Print Network [OSTI]

United States California Solar Initiative Coastally Trappedparticipants in the California Solar Initiative (CSI)on location. In California, solar irradiance forecasts near

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

219

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

Zhu, Xinxin

2013-07-22T23:59:59.000Z

220

Appendix A: Fuel Price Forecast Introduction..................................................................................................................................... 1  

E-Print Network [OSTI]

Appendix A: Fuel Price Forecast Introduction ................................................................................................................... 17 INTRODUCTION Since the millennium, the trend for fuel prices has been one of uncertainty prices, which have traditionally been relatively stable, increased by about 50 percent in 2008. Fuel

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 Mignon Marks Principal Author Mignon Marks Project Manager David Ashuckian Manager ELECTRICITY ANALYSIS OFFICE Sylvia Bender Acting Deputy Director ELECTRICITY SUPPLY DIVISION B.B. Blevins Executive Director

222

Essays in International Macroeconomics and Forecasting  

E-Print Network [OSTI]

This dissertation contains three essays in international macroeconomics and financial time series forecasting. In the first essay, I show, numerically, that a two-country New-Keynesian Sticky Prices model, driven by monetary and productivity shocks...

Bejarano Rojas, Jesus Antonio

2012-10-19T23:59:59.000Z

223

Sandia National Laboratories: solar forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1developmentturbineredox-activeNational Solar Thermal Test Facility

224

Dynamic Algorithm for Space Weather Forecasting System  

E-Print Network [OSTI]

for the designation as UNDERGRADUATE RESEARCH SCHOLAR April 2010 Major: Nuclear Engineering DYNAMIC ALGORITHM FOR SPACE WEATHER FORECASTING SYSTEM A Junior Scholars Thesis by LUKE DUNCAN FISCHER Submitted to the Office of Undergraduate... 2010 Major: Nuclear Engineering iii ABSTRACT Dynamic Algorithm for Space Weather Forecasting System. (April 2010) Luke Duncan Fischer Department of Nuclear Engineering Texas A&M University Research Advisor: Dr. Stephen Guetersloh...

Fischer, Luke D.

2011-08-08T23:59:59.000Z

225

Nambe Pueblo Water Budget and Forecasting model.  

SciTech Connect (OSTI)

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

Brainard, James Robert

2009-10-01T23:59:59.000Z

226

Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast Summary............................................................................................................ 2 Sixth Power Plan Demand Forecast................................................................................................ 4 Demand Forecast Range

227

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand................................................................................................................................. 1 Demand Forecast Methodology.................................................................................................. 3 New Demand Forecasting Model for the Sixth Plan

228

Three Rivers Builders The Three Rivers House Project Summary  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarlyEnergyDepartment of EnergyProgram (Alabama)TechnologyPhoenix,Further ThirdDOEThree Rivers

229

alara center experience: Topics by E-print Network  

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

were dissolved oxygen, turbidity, nitrate-N, phosphateArkansas Water Resources Center Kings River Quality Assurance Project Final Report Marc Nelson, Ph Soerens, Thomas 193...

230

Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint  

SciTech Connect (OSTI)

The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

Zhang, J.; Florita, A.; Hodge, B. M.; Freedman, J.

2014-05-01T23:59:59.000Z

231

Using Wikipedia to forecast disease  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear SecurityTensile Strain Switched Ferromagnetism inS-4500II Field Emission SEMUsedUserUsingelectronEnergyThe BatchUsing

232

Using Wikipedia to forecast diseases  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered energy consumption by sectorlongUpdates byUser GuideHadoopUsing Wikipedia to

233

Stellar Astrophysics Requirements NERSC Forecast  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administrationcontroller systemsBiSiteNeutron Scattering4 ByWatchingStateAbout Us

234

Red River Compact (Texas)  

Broader source: Energy.gov [DOE]

The Red River Compact Commission administers the Red River Compact to ensure that Texas receives its equitable share of quality water from the Red River and its tributaries as apportioned by the...

235

Thirty-year solid waste generation forecast for facilities at SRS  

SciTech Connect (OSTI)

The information supplied by this 30-year solid waste forecast has been compiled as a source document to the Waste Management Environmental Impact Statement (WMEIS). The WMEIS will help to select a sitewide strategic approach to managing present and future Savannah River Site (SRS) waste generated from ongoing operations, environmental restoration (ER) activities, transition from nuclear production to other missions, and decontamination and decommissioning (D&D) programs. The EIS will support project-level decisions on the operation of specific treatment, storage, and disposal facilities within the near term (10 years or less). In addition, the EIS will provide a baseline for analysis of future waste management activities and a basis for the evaluation of the specific waste management alternatives. This 30-year solid waste forecast will be used as the initial basis for the EIS decision-making process. The Site generates and manages many types and categories of waste. With a few exceptions, waste types are divided into two broad groups-high-level waste and solid waste. High-level waste consists primarily of liquid radioactive waste, which is addressed in a separate forecast and is not discussed further in this document. The waste types discussed in this solid waste forecast are sanitary waste, hazardous waste, low-level mixed waste, low-level radioactive waste, and transuranic waste. As activities at SRS change from primarily production to primarily decontamination and decommissioning and environmental restoration, the volume of each waste s being managed will change significantly. This report acknowledges the changes in Site Missions when developing the 30-year solid waste forecast.

Not Available

1994-07-01T23:59:59.000Z

236

Savannah River Tank Waste Residuals  

Office of Environmental Management (EM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of Energy Power.pdf11-161-LNG |September2-SCORECARD-01-24-13 Page 1 of 1SandraSavannah River Savannah

237

Ecotoxicology | Savannah River Ecology Laboratory  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary)morphinanInformation Desert Southwest Region service area. TheEPSCI Home It is the mission ofEconomicSavannah River

238

Platte River Cooperative Agreement  

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

Platte River Cooperative Agreement Skip Navigation Links Transmission Functions Infrastructure projects Interconnection OASIS OATT Platte River Cooperative Agreement PEIS, NE, WY,...

239

Maine Rivers Policy (Maine)  

Broader source: Energy.gov [DOE]

The Maine Rivers Policy accompanies the Maine Waterway Development and Conservation Act and provides additional protection for some river and stream segments, which are designated as outstanding...

240

River Basin Commissions (Indiana)  

Broader source: Energy.gov [DOE]

This legislation establishes river basin commissions, for the Kankakee, Maumee, St. Joseph, and Upper Wabash Rivers. The commissions facilitate and foster cooperative planning and coordinated...

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Wabash River Heritage Corridor (Indiana)  

Broader source: Energy.gov [DOE]

The Wabash River Heritage Corridor, consisting of the Wabash River, the Little River, and the portage between the Little River and the Maumee River, is considered a protected area, where...

242

Autoregressive Time Series Forecasting of Computational Demand  

E-Print Network [OSTI]

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential smoothing models perform better for two and three-step-ahead forecasts. A Monte Carlo bootstrap test is proposed to evaluate the continuous prediction performance of different models with arbitrary confidence and statistical significance levels. Although the prediction results differ between the Tycoon and PlanetLab networks, we observe very similar overall statistical properties, such as volatility dynamics.

Sandholm, Thomas

2007-01-01T23:59:59.000Z

243

Do Investors Forecast Fat Firms? Evidence from the Gold Mining Industry  

E-Print Network [OSTI]

Economists Gold Price Forecasts, Australian Journal ofDo Investors Forecast Fat Firms? Evidence from the Gold

Borenstein, Severin; Farrell, Joseph

2006-01-01T23:59:59.000Z

244

Potential to Improve Forecasting Accuracy: Advances in Supply Chain Management  

E-Print Network [OSTI]

Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic ...

Datta, Shoumen

2008-07-31T23:59:59.000Z

245

Market perceptions of efficiency and news in analyst forecast errors  

E-Print Network [OSTI]

Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms...

Chevis, Gia Marie

2004-11-15T23:59:59.000Z

246

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

This study examines the relationship between a firm??s degree of multinationality and its managers?? earnings forecasts. Firms with a high degree of multinationality are subject to greater uncertainty regarding earnings forecasts due...

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

247

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts.

Botterud, A.; Wang, J.; Miranda, V.; Bessa, R. J.; Decision and Information Sciences; INESC Porto

2010-04-01T23:59:59.000Z

248

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. (author)

Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro; Bessa, Ricardo J.

2010-04-15T23:59:59.000Z

249

Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)  

SciTech Connect (OSTI)

This presentation presents some statistical analysis of wind power forecast errors and error distributions, with examples using ERCOT data.

Hodge, B. M.; Milligan, M.

2011-07-01T23:59:59.000Z

250

0 20 4010 Miles NOAA Harmful Algal Bloom Operational Forecast System  

E-Print Network [OSTI]

0 20 4010 Miles NOAA Harmful Algal Bloom Operational Forecast System Texas Forecast Region Maps to Sargent BCH NOAA Harmful Algal Bloom Operational Forecast System Texas Forecast Region Maps 0 5 102 Bloom Operational Forecast System Texas Forecast Region Maps 0 5 102.5 Miles West Bay #12;Aransas Bay

251

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

PSO (FU 2101) Ensemble-forecasts for wind power Wind Power Ensemble Forecasting Using Wind Speed the problems of (i) transforming the meteorological ensembles to wind power ensembles and, (ii) correcting) data. However, quite often the actual wind power production is outside the range of ensemble forecast

252

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

requirements. The transportation energy demand forecasts make assumptions about fuel price forecastsCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY ENERGY COMMISSION Gordon Schremp, Jim Page, and Malachi Weng-Gutierrez Principal Authors Jim Page Project

253

Forecast Combinations of Computational Intelligence and Linear Models for the  

E-Print Network [OSTI]

Forecast Combinations of Computational Intelligence and Linear Models for the NN5 Time Series Forecasting competition Robert R. Andrawis Dept Computer Engineering Cairo University, Giza, Egypt robertrezk@eg.ibm.com November 6, 2010 Abstract In this work we introduce a forecasting model with which we participated

Atiya, Amir

254

GET your forecast at the click of a button.  

E-Print Network [OSTI]

GET your forecast at the click of a button. EXPLORE your local weather in detail. PLAN your days favourite locations; · Pan and zoom to any area in Australia; · Combine the latest weather and forecast current temperatures across Australia. MetEyeTM computer screen image displaying the weather forecast

Greenslade, Diana

255

Compatibility of Stand Basal Area Predictions Based on Forecast Combination  

E-Print Network [OSTI]

Compatibility of Stand Basal Area Predictions Based on Forecast Combination Xiongqing Zhang Carr.) in Beijing, forecast combination was used to adjust predicted stand basal areas from these three types of models. The forecast combination method combines information and disperses errors from

Cao, Quang V.

256

MOUNTAIN WEATHER PREDICTION: PHENOMENOLOGICAL CHALLENGES AND FORECAST METHODOLOGY  

E-Print Network [OSTI]

MOUNTAIN WEATHER PREDICTION: PHENOMENOLOGICAL CHALLENGES AND FORECAST METHODOLOGY Michael P. Meyers of the American Meteorological Society Mountain Weather and Forecasting Monograph Draft from Friday, May 21, 2010 of weather analysis and forecasting in complex terrain with special emphasis placed on the role of humans

Steenburgh, Jim

257

WP1: Targeted and informative forecast system design  

E-Print Network [OSTI]

WP1: Targeted and informative forecast system design Emma Suckling, Leonard A. Smith and David Stainforth EQUIP Meeting ­ August 2011 Edinburgh #12;Targeted and informative forecast system design Develop models to support decision making (1.4) #12;Targeted and informative forecast system design KEY QUESTIONS

Stevenson, Paul

258

Earnings forecast bias -a statistical analysis Franois Dossou  

E-Print Network [OSTI]

Earnings forecast bias - a statistical analysis François Dossou Sandrine Lardic** Karine Michalon' earnings forecasts is an important aspect of research for different reasons: Many empirical studies employ analysts' consensus forecasts as a proxy for the market's expectations of future earnings in order

Paris-Sud XI, Université de

259

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta*  

E-Print Network [OSTI]

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta* Department of Economics Bayesian and classical methods to forecast employment for eight sectors of the US economy. In addition-sample period and January 1990 to March 2009 as the out-of- sample horizon, we compare the forecast performance

Ahmad, Sajjad

260

Power load forecasting Organization: Huizhou Electric Power, P. R. China  

E-Print Network [OSTI]

Power load forecasting Organization: Huizhou Electric Power, P. R. China Presenter: Zhifeng Hao can be divided into load forecasting and electrical consumption predicting according to forecasting in generators macroeconomic control, power exchange plan and so on. And the prediction is from one day to seven

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

· advantage: no NWP data necessary ­ very actual shortest term forecasts possible · wind power inputAccuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method

Heinemann, Detlev

262

Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1  

E-Print Network [OSTI]

1 Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1 1 Great Lakes forecasts in operational hydrology builds a sample of possibilities for the future, of climate series from-parametric method can be extended into a new weighted parametric hydrological forecasting technique to allow

263

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

Forecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona; Numerical weather prediction 1. Introduction Wave forecasting is now an integral part of operational weather methods for generating such forecasts from numerical model output from the European Centre for Medium

Stevenson, Paul

264

AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.  

E-Print Network [OSTI]

AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

Povinelli, Richard J.

265

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff members in the Demand, and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption

266

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff in the Demand Analysis. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data

267

Draft for Public Comment Appendix A. Demand Forecast  

E-Print Network [OSTI]

Draft for Public Comment A-1 Appendix A. Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required component of the Council's Northwest Regional Conservation had a tradition of acknowledging the uncertainty of any forecast of electricity demand and developing

268

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc to redress this situation by presenting a discussion of the issues involved in demand forecasting for new or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica­ tions Services. 1 #12

McBurney, Peter

269

Using Belief Functions to Forecast Demand for Mobile Satellite Services  

E-Print Network [OSTI]

Using Belief Functions to Forecast Demand for Mobile Satellite Services Peter McBurney and Simon.j.mcburney,s.d.parsonsg@elec.qmw.ac.uk Abstract. This paper outlines an application of belief functions to forecasting the demand for a new service in a new category, based on new technology. Forecasting demand for a new product or service

McBurney, Peter

270

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

Boyer, Edmond

271

FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH  

E-Print Network [OSTI]

FORECASTING SOLAR RADIATION -- PRELIMINARY EVALUATION OF AN APPROACH BASED UPON THE NATIONAL, and undertake a preliminary evaluation of, a simple solar radiation forecast model using sky cover predictions forecasts is 0.05o in latitude and longitude. Solar Radiation model: The model presented in this paper

Perez, Richard R.

272

U.S. Crude Oil Production Forecast-Analysis of Crude Types  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) " ,"ClickPipelines AboutDecemberSteamYearTexas--State OffshoreProduction Forecast- Analysis of Crude Types

273

Forecasting the Market Penetration of Energy Conservation Technologies: The Decision Criteria for Choosing a Forecasting Model  

E-Print Network [OSTI]

capital requirements and research and development programs in the alum inum industry. : CONCLUSIONS Forecasting the use of conservation techndlo gies with a market penetration model provides la more accountable method of projecting aggrega...

Lang, K.

1982-01-01T23:59:59.000Z

274

Issues in midterm analysis and forecasting, 1996  

SciTech Connect (OSTI)

This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation.

NONE

1996-08-01T23:59:59.000Z

275

Forecasting Turbulent Modes with Nonparametric Diffusion Models  

E-Print Network [OSTI]

This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.

Tyrus Berry; John Harlim

2015-01-27T23:59:59.000Z

276

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

' · Empirical models quantify our ability to predict without knowing the laws of physics · Climatology skill' model? 2. Dynamic climatology (DC) is a more appropriate benchmark for near- term (initialised) climate forecasts · A conditional climatology, initialised at launch and built from the historical archive

Stevenson, Paul

277

Issues in midterm analysis and forecasting 1998  

SciTech Connect (OSTI)

Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

NONE

1998-07-01T23:59:59.000Z

278

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS MARINE SERVICE  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS MARINE SERVICE IMPROVEMENTS FOR QUEENSLAND across Australia. FURTHER INFORMATION: www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 From © Copyright Commonwealth of Australia 2013, Bureau of Meteorology Queensland Australia Coastal Waters Zones

Greenslade, Diana

279

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

280

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surface. In this paper, a case is presented for which the operational Numerical Weather Prediction Model (NWP) HIRLAM

Stoffelen, Ad

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

Prediction versus Projection: How weather forecasting and climate models differ. Aaron B. Wilson Context: Global http://data.giss.nasa.gov/ #12;Numerical Weather Prediction Collect Observations alters associated weather patterns. Models used to predict weather depend on the current observed state

Howat, Ian M.

282

Facebook IPO updated valuation and user forecasting  

E-Print Network [OSTI]

Facebook IPO updated valuation and user forecasting Based on: Amendment No. 6 to Form S-1 (May 9. Peter Cauwels and Didier Sornette, Quis pendit ipsa pretia: facebook valuation and diagnostic Extreme Growth JPMPaper Cauwels and Sornette 840 1110 1820 S1- filing- May 9 2012 1006 1105 1371 Facebook

283

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

#12;FORECAST OF VACANCIES Until end of 2016 (Issue No. 22) #12;Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff the team of professionals. Second half 2015 VACANCY GRADE REQUIREMENTS / ROLE EXPECTED DATE OF VACANCY

284

Segmenting Time Series for Weather Forecasting  

E-Print Network [OSTI]

) models is summarised as weather forecast texts. In the domain of gas turbines, sensor data from an operational gas turbine is summarised for the maintenance engineers. More details on SUMTIME have been to develop a generic model for summarisation of time series data. Initially, we have applied standard

Sripada, Yaji

285

Online short-term solar power forecasting  

SciTech Connect (OSTI)

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)

Bacher, Peder; Madsen, Henrik [Informatics and Mathematical Modelling, Richard Pedersens Plads, Technical University of Denmark, Building 321, DK-2800 Lyngby (Denmark); Nielsen, Henrik Aalborg [ENFOR A/S, Lyngsoe Alle 3, DK-2970 Hoersholm (Denmark)

2009-10-15T23:59:59.000Z

286

Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price forecast of the Fifth Northwest Power  

E-Print Network [OSTI]

Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price as traded on the wholesale, short-term (spot) market at the Mid-Columbia trading hub. This price represents noted. BASE CASE FORECAST The base case wholesale electricity price forecast uses the Council's medium

287

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

288

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

289

Lynches River Elec Coop, Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories on climateJunoMedanos Energy Center6334247°,LuminusLynches River

290

Salt River Project | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar PowerstoriesNrelPartnerType JumpJersey)Carbon DevelopmentCorp JumpRiverProject

291

Kings River Conservation Dist | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429 Throttled (botOpen6Kentwood,George County is aKings River Conservation Dist

292

Black River Electric Coop | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartmentAUDIT REPORTOpenWendeGuo Feng Bio JumpVenturesCoral Capital JumpBlack River

293

Missouri River Energy Services | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision hasInformation Earth's HeatMexico: Energy ResourcesMinnesota/Incentives <MinotCSVMissouri River

294

Maple River Energy LLC | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429Lacey,(MonasterLowellis a town inRiver Energy LLC Jump to: navigation, search

295

Mary's River Geothermal Project | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429Lacey,(MonasterLowellis a town inRiver93.Information Martinez4°

296

Wing River Wind Farm | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublic Utilities Address: 160 East 300 South Place:ReferenceEdit JumpWill County,Windspire EnergyFlatsRiver Wind

297

Elk River Wind Farm | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision has beenFfe2fb55-352f-473b-a2dd-50ae8b27f0a6 No revisionWind,Soils and RocksElement PowerElk River Wind

298

American River Ventures | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublic Utilities Address: 160 East 300Algoil JumpAltergy SystemsAmerican Energy Systems IncMunicipalAmerican River

299

Three Rivers Electric Coop | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty LtdSteen,Ltd Jump JumpAl., 1978) |Thrall, Texas: EnergyThree Rivers Electric

300

Illinois River Energy LLC | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to: navigation,Ohio:GreerHiCalifornia:ISI SolarIdanha, Oregon:IkeIllinois River Energy LLC

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Test application of a semi-objective approach to wind forecasting for wind energy applications  

SciTech Connect (OSTI)

The test application of the semi-objective (S-O) wind forecasting technique at three locations is described. The forecasting sites are described as well as site-specific forecasting procedures. Verification of the S-O wind forecasts is presented, and the observed verification results are interpreted. Comparisons are made between S-O wind forecasting accuracy and that of two previous forecasting efforts that used subjective wind forecasts and model output statistics. (LEW)

Wegley, H.L.; Formica, W.J.

1983-07-01T23:59:59.000Z

302

Canadian River Compact (Texas)  

Broader source: Energy.gov [DOE]

The Canadian River Commission administers the Canadian River Compact which includes the states of New Mexico, Oklahoma, and Texas. Signed in 1950 by the member states, the Compact was subsequently...

303

Pecos River Compact (Texas)  

Broader source: Energy.gov [DOE]

This legislation authorizes the state's entrance into the Pecos River Compact, a joint agreement between the states of New Mexico and Texas. The compact is administered by the Pecos River Compact...

304

LANL JOWOG 31 2012 Forecast  

SciTech Connect (OSTI)

Joint Working Group (JOWOG) 31, Nuclear Weapons Engineering, has a particularly broad scope of activities within its charter which emphasizes systems engineering. JOWOG 31 brings together experts from AWE and the national laboratories to address engineering issues associated with warhead design and certification. Some of the key areas of interaction, as addressed by the HOCWOGs are: (1) Engineering Analysis, (2) Hydrodynamic Testing, (3) Environmental Testing, and (4) Model Based Integrated Toolkit (MBIT). Gas Transfer Systems and Condition Monitoring interaction has been moved back to JOWOG 31. The regularly scheduled JOWOG 31 activities are the General Sessions, Executive Sessions, Focused Exchanges and HOCWOGs. General Sessions are scheduled every 12-18 months and are supported by the four design laboratories (AWE, LANL, LLNL, and SNL). Beneficial in educating the next generation of weapons engineers and establishing contacts between AWE and the US laboratory personnel. General Sessions are based on a blend of presentations and workshops centered on various themed subjects directly related to Stockpile Stewardship. HOCWOG meetings are more narrowly focused than the General Sessions. They feature presentations by experts in the field with a greater emphasis on round table discussions. Typically about 20 people attend. Focused exchanges are generally the result of interactions within JOWOG general sessions or HOCWOG meetings. They generally span a very specific topic of current interest within the US and UK.

Vidlak, Anton J. II [Los Alamos National Laboratory

2012-08-08T23:59:59.000Z

305

Black River Electric Coop, Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartmentAUDIT REPORTOpenWendeGuo Feng Bio JumpVenturesCoral Capital JumpBlack RiverRiver

306

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30T23:59:59.000Z

307

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the method presented here can be used to forecast solar wind velocities and magnetic polarity from a single coronal hole image, along with a single magnetic full-disk image. The coronal hole parameters used in this study are estimated with Kitt Peak Vacuum Telescope He I 1083 nm spectrograms and photospheric magnetograms. Solar wind and coronal hole data for the period between May 1992 and September 2003 are investigated. The new model is found to be accurate to within 10% of observed solar wind measurements for its best one-month periods, and it has a linear correlation coefficient of ~0.38 for the full 11 years studied. Using a single estimated coronal hole map, the model can forecast the Earth directed solar wind velocity up to 8.5 days in advance. In addition, this method can be used with any source of coronal hole area and location data.

S. Robbins; C. J. Henney; J. W. Harvey

2007-01-09T23:59:59.000Z

308

A survey on wind power ramp forecasting.  

SciTech Connect (OSTI)

The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-02-23T23:59:59.000Z

309

The Fish Passage Center Annual Report of Accomplishments  

E-Print Network [OSTI]

The Fish Passage Center Annual Report of Accomplishments 2012 Salmon River Smolt Monitoring Program Trap Submitted To The Fish Passage Center Oversight Board December 30, 2012 #12;G:\\STAFF\\DOCUMENT\\2012 Documents\\2012 Files\\156-12.doc Profile The Fish Passage Center (Center) was first established in 1984

310

Draft Fourth Northwest Conservation and Electric Power Plan, Appendix D ECONOMIC AND DEMAND FORECASTS  

E-Print Network [OSTI]

AND DEMAND FORECASTS INTRODUCTION AND SUMMARY Role of the Demand Forecast A demand forecast of at least 20 years is one of the explicit requirements of the Northwest Power Act. A demand forecast is, of course analysis. Because the future is inherently uncertain, the Council forecasts a range of future demand levels

311

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

Zhang, J.; Hodge, B. M.; Florita, A.; Lu, S.; Hamann, H. F.; Banunarayanan, V.

2013-10-01T23:59:59.000Z

312

Powder River Energy Corporation Smart Grid Project | Open Energy  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories onFocusOskiPhilips ColorLoading map...ClimatePowder River

313

Little River County, Arkansas: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429Lacey,(Monaster And Coolbaugh, 2007)isLithologically ControlledFerry,River

314

Great River, New York: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to: navigation,Ohio: EnergyGrasslands Renewable Energy LLCGray,BoilingRiver, New York:

315

Raft River III Geothermal Project | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColoradosource HistoryRaft River Sector Geothermal energyProject

316

Red River Hot Springs Geothermal Area | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColoradosourceRausWyoming: Energy Resources6072302°,Red River

317

Powder River, Wyoming: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy ResourcesLoadingPenobscot County, Maine:Plug Power IncPowder River Energy Corporation Place:

318

Council's Columbia River Fish and Wildlife Program  

E-Print Network [OSTI]

Walleye Smallmouth bass Northern pike Others 5 Native and Non-native Fish Predators #12; At dams#12;#12;#12;#12;#12;#12;#12;Council's Columbia River Fish and Wildlife Program Summary of Predation Event Center #12;Council's 2009 Fish and Wildlife Program Piscivorous Predator Control Implement

319

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

??Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter (more)

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

320

Western Area Power Administration Starting Forecast Month: Sierra...  

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

CVP Generation Project Use First Preference Purchases and Exchanges Base Resource February 2014 Twelve-Month Forecast of CVP Generation and Base Resource February 2014 January...

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Western Area Power Administration Starting Forecast Month: Sierra...  

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

Use First Preference Purchases and Exchanges Base Resource April 2014 Twelve-Month Forecast of CVP Generation and Base Resource April 2014 March 2015 Exceedence Level: 90% (Dry)...

322

Western Area Power Administration Starting Forecast Month: Sierra...  

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

Preference Month CVP Generation Project Use First Preference Purchases and Exchanges Base Resource May 2014 Twelve-Month Forecast of CVP Generation and Base Resource May 2014 April...

323

Western Area Power Administration Starting Forecast Month: Sierra...  

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

based on Green Book ("Above Normal") values. Base Resource March 2014 Twelve-Month Forecast of CVP Generation and Base Resource March 2014 February 2015 Exceedence Level: 90%...

324

analytical energy forecasting: Topics by E-print Network  

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

COMMISSION Tom Gorin Lynn Marshall Principal Author Tom Gorin Project 11 Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Computer Technologies and...

325

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

SciTech Connect (OSTI)

Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.

2012-09-01T23:59:59.000Z

326

Today's Forecast: Improved Wind Predictions | Department of Energy  

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

forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind...

327

Forecasting the underlying potential governing climatic time series  

E-Print Network [OSTI]

We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for `potential analysis' of climatic tipping points which altogether serves anticipating, detecting and forecasting climate transitions and bifurcations using several independent techniques of time series analysis.

Livina, V N; Mudelsee, M; Lenton, T M

2012-01-01T23:59:59.000Z

328

Econometric model and futures markets commodity price forecasting  

E-Print Network [OSTI]

Versus CCll1rnercial Econometric M:ldels." Uni- versity ofWorking Paper No. 72 ECONOMETRIC ! 'econometric forecasts with the futures

Just, Richard E.; Rausser, Gordon C.

1979-01-01T23:59:59.000Z

329

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network [OSTI]

Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery , Michael Trick Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213. nihat

Gordon, Geoffrey J.

330

State-of-the art of freight forecast modeling: lessons learned and the road ahead  

E-Print Network [OSTI]

of-the art of freight forecast modeling: lessons learned andof goods as well as to forecast the expected future truckused for the short-term forecasts of freight volumes on

Chow, Joseph Y.; Yang, Choon Heon; Regan, Amelia C.

2010-01-01T23:59:59.000Z

331

Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices  

E-Print Network [OSTI]

revisions to the EIAs natural gas price forecasts in AEOsolely on the AEO 2005 natural gas price forecasts willComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

332

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network [OSTI]

to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

333

Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller  

E-Print Network [OSTI]

Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller Abstract We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing. Introduction Electricity demand forecasting is an important aspect of the control and scheduling of power

Teschner, Matthias

334

Reducing the demand forecast error due to the bullwhip effect in the computer processor industry  

E-Print Network [OSTI]

Intel's current demand-forecasting processes rely on customers' demand forecasts. Customers do not revise demand forecasts as demand decreases until the last minute. Intel's current demand models provide little guidance ...

Smith, Emily (Emily C.)

2010-01-01T23:59:59.000Z

335

Evaluation of forecasting techniques for short-term demand of air transportation  

E-Print Network [OSTI]

Forecasting is arguably the most critical component of airline management. Essentially, airlines forecast demand to plan the supply of services to respond to that demand. Forecasts of short-term demand facilitate tactical ...

Wickham, Richard Robert

1995-01-01T23:59:59.000Z

336

Craig Thomas Discovery & Visitor Center  

High Performance Buildings Database

Moose, WY Grand Teton National Park's rugged landscape and stunning array of wildlife attract nearly three million visitors every year, making it one of our most popular national parks. A new Grand Teton National Park visitor center near the park's headquarters north of Jackson, Wyoming, replaces an outdated building, educates an increased number of visitors, and inspires further exploration of this extraordinary landscape. The project site is located along the Snake River, between a riparian forest and a sagebrush meadow.

337

Solid Waste Integrated Forecast Technical (SWIFT) Report FY2001 to FY2046 Volume 1  

SciTech Connect (OSTI)

This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons to previous forecasts and other national data sources. This report does not include: waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); waste that has been received by the WM Project to date (i.e., inventory waste); mixed low-level waste that will be processed and disposed by the River Protection Program; and liquid waste (current or future generation). Although this report currently does not include liquid wastes, they may be added as information becomes available.

BARCOT, R.A.

2000-08-31T23:59:59.000Z

338

Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty  

E-Print Network [OSTI]

114 Solar Irradiance And Power Output Variabilityand L. Bangyin. Online 24-h solar power forecasting based onNielsen. Online short-term solar power forecasting. Solar

Marquez, Ricardo

2012-01-01T23:59:59.000Z

339

Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast Role of the Economic Forecast ................................................................................................. 2 Economic Drivers of Residential Demand

340

Improving baseline forecasts in a 500-industry dynamic CGE model of the USA.  

E-Print Network [OSTI]

??MONASH-style CGE models have been used to generate baseline forecasts illustrating how an economy is likely to evolve through time. One application of such forecasts (more)

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing quasi-deterministic components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

2014-10-27T23:59:59.000Z

342

Distribution Based Data Filtering for Financial Time Series Forecasting  

E-Print Network [OSTI]

recent past. In this paper, we address the challenge of forecasting the behavior of time series using@unimelb.edu.au Abstract. Changes in the distribution of financial time series, particularly stock market prices, can of stock prices, which aims to forecast the future values of the price of a stock, in order to obtain

Bailey, James

343

On the reliability of seasonal forecasts Antje Weisheimer  

E-Print Network [OSTI]

On the reliability of seasonal forecasts Antje Weisheimer Weisheimer to achieving a "5"? à Use reliability of non-climatological forecastsDon: · if (X) C(X) à climatological (reliable) informaDon · if (X) C(X) à

Stevenson, Paul

344

SATELLITE BASED SHORT-TERM FORECASTING OF SOLAR IRRADANCE  

E-Print Network [OSTI]

SATELLITE BASED SHORT-TERM FORECASTING OF SOLAR IRRADANCE - COMPARISON OF METHODS AND ERROR Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources method was used to derive motion vector fields from two consecutive images. The future image

Heinemann, Detlev

345

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network [OSTI]

of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables existing forecast-based models. I. INTRODUCTION A key goal of smart grid efforts is to substantially-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than

Shenoy, Prashant

346

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

- namic reserve quantification [8], for the optimal oper- ation of combined wind-hydro power plants [5, 1Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power

Boyer, Edmond

347

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil resources resulting in water stress. Effective water management ­ a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting water

Keller, Arturo A.

348

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network [OSTI]

Revised 1997 Retail Electricity Price Forecast March 1998 Principal Author: Ben Arikawa Electricity 1997 FORE08.DOC Page 1 CALIFORNIA ENERGY COMMISSION ELECTRICITY ANALYSIS OFFICE REVISED 1997 RETAIL ELECTRICITY PRICE FORECAST Introduction The Electricity Analysis Office of the California Energy Commission

349

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

-- Photovoltaic systems, Batteries, Forecasting I. INTRODUCTION This paper presents first results of a study Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size

Paris-Sud XI, Université de

350

Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging  

E-Print Network [OSTI]

distribution; Numerical weather prediction; Skewed distribution; Truncated data; Wind energy. 1. INTRODUCTION- native. Purely statistical methods have been applied to short-range forecasts for wind speed only a fewProbabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging J. Mc

Raftery, Adrian

351

Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1  

E-Print Network [OSTI]

for crop yield forecasting and risk analysis. Using the Census Agriculture Region (CAR) as the unit Climate Decision Support and Adaptation, Agriculture and Agri-Food Canada, 1011, Innovation Blvd, Saskatoon, SK S7V 1B7, Canada The Canadian Crop Yield Forecaster (CCYF) is a statistical modelling tool

Miami, University of

352

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An ensemble-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the ensemble's forecast period

Hansens, Jim

353

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

of the forecasting algorithm for the different conditions. 1. INTRODUCTION According to the U.S. Department of Energy could take advantage of times when electricity cost is lower, to chill a cold water storage tankForecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines

Hoff, William A.

354

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

Various factors influence the development of the voluntary 'green' power market--the market in which consumers purchase or produce power from non-polluting, renewable energy sources. These factors include climate policies, renewable portfolio standards (RPS), renewable energy prices, consumers' interest in purchasing green power, and utilities' interest in promoting existing programs and in offering new green options. This report presents estimates of voluntary market demand for green power through 2015 that were made using historical data and three scenarios: low-growth, high-growth, and negative-policy impacts. The resulting forecast projects the total voluntary demand for renewable energy in 2015 to range from 63 million MWh annually in the low case scenario to 157 million MWh annually in the high case scenario, representing an approximately 2.5-fold difference. The negative-policy impacts scenario reflects a market size of 24 million MWh. Several key uncertainties affect the results of this forecast, including uncertainties related to growth assumptions, the impacts that policy may have on the market, the price and competitiveness of renewable generation, and the level of interest that utilities have in offering and promoting green power products.

Bird, L.; Holt, E.; Sumner, J.; Kreycik, C.

2010-05-01T23:59:59.000Z

355

LED Lighting Forecast | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:YearRound-UpHeatMulti-Dimensionalthe10 DOEWashington, DCKickoffLDV HVAC ModelLED

356

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating SolarElectric Coop,SaveWhiskey Flats GeothermalElectric Coopelectricityemissionsdata +

357

Columbia River Treaty  

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

an understanding of the implications for post-2024 Treaty planning and Columbia River operations. The joint effort by the Entities to conduct initial post-2024 modeling and...

358

Saving a Dwindling River  

E-Print Network [OSTI]

information on this research is available by downloading TWRI Technical Report 291, ?Reconnaissance Survey of Salt Sources and Loading into the Pecos River,? at http://twri.tamu.edu/reports.php. The research team has also compared flow and salinity data from... Water Act, Section 319 from the U.S. Environmental Protection Agency. ?The river?s importance?historically, biologically, hydrologically and economically?to the future of the entire Pecos River Basin and the Rio Grande is huge,? said Will Hatler, project...

Wythe, Kathy

2007-01-01T23:59:59.000Z

359

Water and Environmental Research Center Annual Technical Report  

E-Print Network [OSTI]

availability, river discharge, permafrost-groundwater interactions, remote water/wastewater treatment options of the Center. Research Program Introduction Research Program Introduction 1 #12;Integrating Remote Sensing and Local Knowledge to Monitor Seasonal River Ice Dynamics Basic Information Title: Integrating Remote

360

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

SciTech Connect (OSTI)

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.

2012-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Help Center  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation ProposedUsingFun with Bigfront.jpgcommunity200cellHeat Transfer inHeikoAccessibility TextLos

362

operations center  

National Nuclear Security Administration (NNSA)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarlyEnergyDepartmentNationalRestart of the Review of theOFFICE OF8/%2A ennike | National7/%2A

363

Explosives Center  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series toESnet4:Epitaxial Thin FilmEquipment SSRLExploring the Possibilities

364

Schlumberger soundings in the Upper Raft River and Raft River...  

Open Energy Info (EERE)

Schlumberger soundings in the Upper Raft River and Raft River Valleys, Idaho and Utah Abstract In 1975, the U.S. Geological Survey made seventy Schlumberger resistivity...

365

Sabine River Compact (Multiple States)  

Broader source: Energy.gov [DOE]

The Sabine River Compact Commission administers the Sabine River Compact to ensure that Texas receives its equitable share of quality water from the Sabine River and its tributaries as apportioned...

366

Verification of hourly forecasts of wind turbine power output  

SciTech Connect (OSTI)

A verification of hourly average wind speed forecasts in terms of hourly average power output of a MOD-2 was performed for four sites. Site-specific probabilistic transformation models were developed to transform the forecast and observed hourly average speeds to the percent probability of exceedance of an hourly average power output. (This transformation model also appears to have value in predicting annual energy production for use in wind energy feasibility studies.) The transformed forecasts were verified in a deterministic sense (i.e., as continuous values) and in a probabilistic sense (based upon the probability of power output falling in a specified category). Since the smoothing effects of time averaging are very pronounced, the 90% probability of exceedance was built into the transformation models. Semiobjective and objective (model output statistics) forecasts were made compared for the four sites. The verification results indicate that the correct category can be forecast an average of 75% of the time over a 24-hour period. Accuracy generally decreases with projection time out to approx. 18 hours and then may increase due to the fairly regular diurnal wind patterns that occur at many sites. The ability to forecast the correct power output category increases with increasing power output because occurrences of high hourly average power output (near rated) are relatively rare and are generally not forecast. The semiobjective forecasts proved superior to model output statistics in forecasting high values of power output and in the shorter time frames (1 to 6 hours). However, model output statistics were slightly more accurate at other power output levels and times. Noticeable differences were observed between deterministic and probabilistic (categorical) forecast verification results.

Wegley, H.L.

1984-08-01T23:59:59.000Z

367

NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts  

E-Print Network [OSTI]

· NatioNal aNd Global Forecasts · West VirGiNia ProFiles aNd Forecasts · eNerGy · Healt Global Insight, paid for by the West Virginia Department of Revenue. 2013 WEST VIRGINIA ECONOMIC OUTLOOKWest Virginia Economic Outlook 2013 is published by: Bureau of Business & Economic Research West

Mohaghegh, Shahab

368

Publications | Savannah River National Environmental Park  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassive Solar HomePromising Science for PlutoniumAboutAboutPublicationsSavannah River

369

Green River, Utah, Disposal Site Fact Sheet  

Office of Legacy Management (LM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarlyEnergyDepartment ofDepartment ofof EnergyYou$0.C. 20545*. . : '* FEB1f\l pGreen River, Utah,

370

First Savannah River Shipment Arrives At WIPP  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation ProposedUsing ZirconiaPolicyFeasibilityField OfficeFirmFirst Proof ofFirstFirst Savannah River

371

River Edge Redevelopment Zone (Illinois)  

Broader source: Energy.gov [DOE]

The purpose of the River Edge Redevelopment Program is to revive and redevelop environmentally challenged properties adjacent to rivers in Illinois.

372

Lane County Child-Care Centers Serving Children Under Three  

E-Print Network [OSTI]

S River Rd Cottage Grove OR 97424 5419423391 x112 1 Jasper Mountain Child Care Center 37875 Jasper Lowell Rd Jasper OR 97438 5417471235 1 Kindercare - Chambers 1056 1410 Chambers St Eugene OR 97402

Oregon, University of

373

Marion Center, Massachusetts: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429Lacey,(MonasterLowellis a town inRiver

374

A Cosmology Forecast Toolkit -- CosmoLib  

E-Print Network [OSTI]

The package CosmoLib is a combination of a cosmological Boltzmann code and a simulation toolkit to forecast the constraints on cosmological parameters from future observations. In this paper we describe the released linear-order part of the package. We discuss the stability and performance of the Boltzmann code. This is written in Newtonian gauge and including dark energy perturbations. In CosmoLib the integrator that computes the CMB angular power spectrum is optimized for a $\\ell$-by-$\\ell$ brute-force integration, which is useful for studying inflationary models predicting sharp features in the primordial power spectrum of metric fluctuations. The numerical code and its documentation are available at http://www.cita.utoronto.ca/~zqhuang/CosmoLib.

Zhiqi Huang

2012-06-11T23:59:59.000Z

375

Development and testing of improved statistical wind power forecasting methods.  

SciTech Connect (OSTI)

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-12-06T23:59:59.000Z

376

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of demand.

Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

2005-07-01T23:59:59.000Z

377

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revisionEnvReviewNonInvasiveExplorationUT-g GrantAtlas (PACA Region - France) Jump to:Energy

378

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision hasInformation Earth'sOklahoma/Geothermal < Oklahomast, 2012Coastfred <div

379

Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty  

E-Print Network [OSTI]

Solar irradiance data . . . . . . . . . . . . .Accuracy . . . . . . . . . . . . . . . . . Solar Resourcev Uncertainty In Solar Resource: Forecasting

Marquez, Ricardo

2012-01-01T23:59:59.000Z

380

Documentation of the Hourly Time Series NCEP Climate Forecast System Reanalysis  

E-Print Network [OSTI]

- 1 - Documentation of the Hourly Time Series from the NCEP Climate Forecast System Reanalysis: ------------------------------------------------------------------------------------------------------------ Initial condition 1 Jan 1979, 0Z Record 1: f00: forecast at first time step of 3 mins Record 2: f01: forecast (either averaged over 0 to 1 hour, or instantaneous at 1 hour) Record 3: f02: forecast (either

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models  

E-Print Network [OSTI]

Solving the problem of inadequate scoring rules for assessing probabilistic football forecast forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies

Fenton, Norman

382

Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System  

E-Print Network [OSTI]

Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observa- tions and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature

383

Distributed Forcing of Forecast and Assimilation Error Systems BRIAN F. FARRELL  

E-Print Network [OSTI]

Distributed Forcing of Forecast and Assimilation Error Systems BRIAN F. FARRELL Division forecast system gov- erning forecast error growth and the tangent linear observer system governing deterministic and stochastic forcings of the forecast and observer systems over a chosen time interval

Farrell, Brian F.

384

Ozone ensemble forecast with machine learning Vivien Mallet,1,2  

E-Print Network [OSTI]

Ozone ensemble forecast with machine learning algorithms Vivien Mallet,1,2 Gilles Stoltz,3 forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past

Paris-Sud XI, Université de

385

MLWFA: A Multilingual Weather Forecast Text Generation Tianfang YAO Dongmo ZHANG Qian WANG  

E-Print Network [OSTI]

MLWFA: A Multilingual Weather Forecast Text Generation System1 Tianfang YAO Dongmo ZHANG Qian WANG generation; Weather forecast generation system Abstract In this demonstration, we present a system for multilingual text generation in the weather forecast domain. Multilingual Weather Forecast Assistant (MLWFA

Wu, Dekai

386

November 14, 2000 A Quarterly Forecast of U.S. Trade  

E-Print Network [OSTI]

November 14, 2000 A Quarterly Forecast of U.S. Trade in Services and the Current Account, 2000 of Forecast*** We forecast that the services trade surplus, which declined from 1997 to 1998 and edged upward. That is, from a level of $80.6 billion in 1999, we forecast that the services trade surplus will be $80

Shyy, Wei

387

USING BOX-JENKINS MODELS TO FORECAST FISHERY DYNAMICS: IDENTIFICATION, ESTIMATION, AND CHECKING  

E-Print Network [OSTI]

~ is illustrated by developing a model that makes monthly forecasts of skipjack tuna, Katsuwonus pelamis, catches

388

Wind Energy Technology Trends: Comparing and Contrasting Recent Cost and Performance Forecasts (Poster)  

SciTech Connect (OSTI)

Poster depicts wind energy technology trends, comparing and contrasting recent cost and performance forecasts.

Lantz, E.; Hand, M.

2010-05-01T23:59:59.000Z

389

ASSESSING THE QUALITY AND ECONOMIC VALUE OF WEATHER AND CLIMATE FORECASTS  

E-Print Network [OSTI]

INFORMATION SYSTEM Forecast -- Conditional probability distribution for event Z = z indicates forecast tornado #12;(1.2) FRAMEWORK Joint Distribution of Observations & Forecasts Observed Weather = Forecast probability p (e.g., induced by Z) Reliability Diagram Observed weather: = 1 (Adverse weather occurs) = 0

Katz, Richard

390

A comparison of univariate methods for forecasting electricity demand up to a day ahead  

E-Print Network [OSTI]

A comparison of univariate methods for forecasting electricity demand up to a day ahead James W methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short of Forecasters. Published by Elsevier B.V. All rights reserved. Keywords: Electricity demand forecasting

McSharry, Patrick E.

391

Resource Adequacy Load Forecast A Report to the Resource Adequacy Advisory Committee  

E-Print Network [OSTI]

one hour peak demand and monthly energy assuming normal weather. The Council forecast includes loadsResource Adequacy Load Forecast A Report to the Resource Adequacy Advisory Committee Tomás of the assessment is the load forecast. The Council staff has recently developed a load forecast to be used

392

A collaborative demand forecasting process with event-based fuzzy judgements Naoufel Cheikhrouhou a,  

E-Print Network [OSTI]

A collaborative demand forecasting process with event-based fuzzy judgements Naoufel Cheikhrouhou a July 2011 Keywords: Collaborative forecasting Demand planning Judgement Time series Fuzzy logic a b s t r a c t Mathematical forecasting approaches can lead to reliable demand forecast in some

393

Demand forecast accuracy and performance of inventory policies under multi-level rolling  

E-Print Network [OSTI]

Demand forecast accuracy and performance of inventory policies under multi-level rolling schedule is to study the behaviour of lot-sizing rules in a multi- level context when forecast demand is subject Interchange to ameliorate demand forecast. Although the presence or absence of forecast errors matters more

Paris-Sud XI, Université de

394

Water Resources Center Annual Technical Report  

E-Print Network [OSTI]

and wildlife habitat functions, and supplemental water supply for state-of- the-art subirrigated cropWater Resources Center Annual Technical Report FY 1999 Introduction Research Program Basic Project: The Maumee River valley is characterized by flat topography with soils that are predominantly heavy clay

395

Loup River Public Power Dist | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories on climateJunoMedanos Energy Center LLC Jump to:River Public Power

396

Saddle River, New Jersey: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, searchVirginiaRooseveltVI Solar Power PlantCenterDistrictSaddle River is a

397

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

Forecasting volatility has held the attention of academics and practitioners all over the world. The objective for this master's thesis is to predict the volatility in stock market by using generalized autoregressive conditional heteroscedasticity(GARCH...

Yang, Xiaorong

2008-01-01T23:59:59.000Z

398

Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap  

E-Print Network [OSTI]

Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap Lorenzo Pascual, Juan generated by GARCH processes. The main advantage over other bootstrap methods previously proposed for GARCH by having conditional heteroscedasticity. Generalized Autoregressive Conditionally Heteroscedastic (GARCH

Romo, Juan

399

Wind Power Forecasting: State-of-the-Art 2009  

E-Print Network [OSTI]

Wind Power Forecasting: State-of-the-Art 2009 ANL/DIS-10-1 Decision and Information Sciences about Argonne and its pioneering science and technology programs, see www.anl.gov. #12;Wind Power

Kemner, Ken

400

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

This report presents information from the Energy Information Administration (EIA). Articles are included on international energy forecasting data, data on the use of home appliances, gasoline prices, household energy use, and EIA information products and dissemination avenues.

NONE

1995-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Optimally controlling hybrid electric vehicles using path forecasting  

E-Print Network [OSTI]

Hybrid Electric Vehicles (HEVs) with path-forecasting belong to the class of fuel efficient vehicles, which use external sensory information and powertrains with multiple operating modes in order to increase fuel economy. ...

Katsargyri, Georgia-Evangelina

2008-01-01T23:59:59.000Z

402

Variable Selection and Inference for Multi-period Forecasting Problems  

E-Print Network [OSTI]

Variable Selection and Inference for Multi-period Forecasting Problems? M. Hashem Pesaran Cambridge University and USC Andreas Pick De Nederlandsche Bank and Cambridge University, CIMF Allan Timmermann UC San Diego and CREATES January 26, 2009...

Pesaran, M Hashem; Pick, Andreas; Timmermann, Allan

403

Optimally Controlling Hybrid Electric Vehicles using Path Forecasting  

E-Print Network [OSTI]

The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted ...

Kolmanovsky, Ilya V.

404

Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1  

E-Print Network [OSTI]

Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1 David Levinson 2 February, the assumed networks to the actual in-place networks and other travel behavior assumptions that went

Levinson, David M.

405

africa conditional forecasts: Topics by E-print Network  

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

forecasts had the potential to improve resource management but instead played only a marginal role in real-world decision making. 1 A widespread perception that the quality of the...

406

An econometric analysis and forecasting of Seoul office market  

E-Print Network [OSTI]

This study examines and forecasts the Seoul office market, which is going to face a big supply in the next few years. After reviewing several previous studies on the Dynamic model and the Seoul Office market, this thesis ...

Kim, Kyungmin

2011-01-01T23:59:59.000Z

407

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

A methodology was developed for forecasting carbon dioxide (CO2) flooding performance quickly and reliably. The feasibility of carbon dioxide flooding in the Dollarhide Clearfork "AB" Unit was evaluated using the methodology. This technique is very...

Marroquin Cabrera, Juan Carlos

1998-01-01T23:59:59.000Z

408

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

................................................................................................. 20 3.1.2 Integrate's Flow of ControlWeather Research and Forecasting Model 2.2 Documentation: A Step-by-step guide of a Model Run .......................................................................................................................... 19 3.1 The Integrate Subroutine

Sadjadi, S. Masoud

409

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

410

Grid-scale Fluctuations and Forecast Error in Wind Power  

E-Print Network [OSTI]

The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

Bel, G; Toots, M; Bandi, M M

2015-01-01T23:59:59.000Z

411

Grid-scale Fluctuations and Forecast Error in Wind Power  

E-Print Network [OSTI]

The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

G. Bel; C. P. Connaughton; M. Toots; M. M. Bandi

2015-03-29T23:59:59.000Z

412

Dispersion in analysts' forecasts: does it make a difference?  

E-Print Network [OSTI]

Financial analysts are an important group of information intermediaries in the capital markets. Their reports, including both earnings forecasts and stock recommendations, are widely transmitted and have a significant impact on stock prices (Womack...

Adut, Davit

2004-09-30T23:59:59.000Z

413

OCTOBER-NOVEMBER FORECAST FOR 2014 CARIBBEAN BASIN HURRICANE ACTIVITY  

E-Print Network [OSTI]

and hurricanes, but instead predicts both hurricane days and Accumulated Cyclone Energy (ACE). Typically, while) tropical cyclone (TC) activity. We have decided to issue this forecast, because Klotzbach (2011) has

Collett Jr., Jeffrey L.

414

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information ...

Choi, Han-Lim

2009-01-01T23:59:59.000Z

415

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting Seasonal Prediction · POAMA · Issues for future Outline #12;Pacific Adaptation Strategy Assistance Program Major source Adaptation Strategy Assistance Program El Nino Mean State · Easterlies westward surface current upwelling

Lim, Eun-pa

416

The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss  

E-Print Network [OSTI]

Agency: 1982-2005a, Annual Energy Outlook, EIA, Washington,Agency: 2004, Annual Energy Outlook Forecast Evaluation,Agency: 2005b, Annual Energy Outlook, EIA, Washington, D.C.

Auffhammer, Maximilian

2005-01-01T23:59:59.000Z

417

Forecasting and strategic inventory placement for gas turbine aftermarket spares  

E-Print Network [OSTI]

This thesis addresses the problem of forecasting demand for Life Limited Parts (LLPs) in the gas turbine engine aftermarket industry. It is based on work performed at Pratt & Whitney, a major producer of turbine engines. ...

Simmons, Joshua T. (Joshua Thomas)

2007-01-01T23:59:59.000Z

418

Short-Termed Integrated Forecasting System: 1993 Model documentation report  

SciTech Connect (OSTI)

The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

Not Available

1993-05-01T23:59:59.000Z

419

Mesoscale predictability and background error convariance estimation through ensemble forecasting  

E-Print Network [OSTI]

Over the past decade, ensemble forecasting has emerged as a powerful tool for numerical weather prediction. Not only does it produce the best estimate of the state of the atmosphere, it also could quantify the uncertainties associated with the best...

Ham, Joy L

2002-01-01T23:59:59.000Z

420

ARM - CARES - Tracer Forecast for CARES  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered‰PNGExperience4AJ01)

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

NREL: Resource Assessment and Forecasting - Capabilities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions and

422

A model for short term electric load forecasting  

E-Print Network [OSTI]

A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE, III Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE May 1975 Major... Subject: Electrical Engineering A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE& III Approved as to style and content by: (Chairman of Committee) (Head Depart t) (Member) ;(Me r (Member) (Member) May 1975 ABSTRACT...

Tigue, John Robert

1975-01-01T23:59:59.000Z

423

Subhourly wind forecasting techniques for wind turbine operations  

SciTech Connect (OSTI)

Three models for making automated forecasts of subhourly wind and wind power fluctuations were examined to determine the models' appropriateness, accuracy, and reliability in wind forecasting for wind turbine operation. Such automated forecasts appear to have value not only in wind turbine control and operating strategies, but also in improving individual wind turbine control and operating strategies, but also in improving individual wind turbine operating strategies (such as determining when to attempt startup). A simple persistence model, an autoregressive model, and a generalized equivalent Markhov (GEM) model were developed and tested using spring season data from the WKY television tower located near Oklahoma City, Oklahoma. The three models represent a pure measurement approach, a pure statistical method and a statistical-dynamical model, respectively. Forecasting models of wind speed means and measures of deviations about the mean were developed and tested for all three forecasting techniques for the 45-meter level and for the 10-, 30- and 60-minute time intervals. The results of this exploratory study indicate that a persistence-based approach, using onsite measurements, will probably be superior in the 10-minute time frame. The GEM model appears to have the most potential in 30-minute and longer time frames, particularly when forecasting wind speed fluctuations. However, several improvements to the GEM model are suggested. In comparison to the other models, the autoregressive model performed poorly at all time frames; but, it is recommended that this model be upgraded to an autoregressive moving average (ARMA or ARIMA) model. The primary constraint in adapting the forecasting models to the production of wind turbine cluster power output forecasts is the lack of either actual data, or suitable models, for simulating wind turbine cluster performance.

Wegley, H.L.; Kosorok, M.R.; Formica, W.J.

1984-08-01T23:59:59.000Z

424

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

it can generate as much or more energy that it needs ? Building activities need N kWhrs per day (solar panels, heating, etc) ? Harvested from solar panels & passive solar. Amount depends on weather ? NWP models forecast DSWRF @ surface (MJ/m2... contract work Saturday Would you plan work for Saturday? Not much detail for Saturday and Sunday With more info could be easier to decide go, no go From deterministic to probabilistic ? Forecast presented as a single scenario ? One scenario presented...

Poulin, L.

2013-01-01T23:59:59.000Z

425

Streamflow forecasting for large-scale hydrologic systems  

E-Print Network [OSTI]

STREAMFLOW FORECASTING FOR LARGE-SCALE HYDROLOGIC SYSTEMS A Thesis by HAITHAM MUNIR AWWAD Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1991 Major Subject: Civil Engineering STREAMFLOW FORECASTING FOR LARGE-SCALE HYDROLOGIC SYSTEMS A Thesis by HAITHAM MUNIR AWWAD Approved as to style and content by: uan B. Valdes (Chair of Committee) alph A. Wurbs (Member) Marshall J. Mc...

Awwad, Haitham Munir

1991-01-01T23:59:59.000Z

426

Use of Shields stress to reconstruct and forecast changes in river metabolism  

E-Print Network [OSTI]

to movement of the bed (Uehlinger, Buhrer & Reichert, 1996; Uehlinger & Naegeli, 1998; Biggs, Smith & Duncan, 1999; Biggs, 2000). Movement of the bed can be expected to cause abrupt changes in metabolism compilation ? 2007 Blackwell Publishing Ltd 1587 #12;displaces organisms (Peterson et al., 1994; Biggs, 1995

Lewis Jr., William M.

427

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.

Lobell, D B; Cahill, K N; Field, C B

2005-09-26T23:59:59.000Z

428

Comparison of Bottom-Up and Top-Down Forecasts: Vision Industry Energy Forecasts with ITEMS and NEMS  

E-Print Network [OSTI]

Comparisons are made of energy forecasts using results from the Industrial module of the National Energy Modeling System (NEMS) and an industrial economic-engineering model called the Industrial Technology and Energy Modeling System (ITEMS), a model...

Roop, J. M.; Dahowski, R. T

429

A Statistical Solar Flare Forecast Method  

E-Print Network [OSTI]

A Bayesian approach to solar flare prediction has been developed, which uses only the event statistics of flares already observed. The method is simple, objective, and makes few ad hoc assumptions. It is argued that this approach should be used to provide a baseline prediction for certain space weather purposes, upon which other methods, incorporating additional information, can improve. A practical implementation of the method for whole-Sun prediction of Geostationary Observational Environment Satellite (GOES) events is described in detail, and is demonstrated for 4 November 2003, the day of the largest recorded GOES flare. A test of the method is described based on the historical record of GOES events (1975-2003), and a detailed comparison is made with US National Oceanic and Atmospheric Administration (NOAA) predictions for 1987-2003. Although the NOAA forecasts incorporate a variety of other information, the present method out-performs the NOAA method in predicting mean numbers of event days, for both M-X and X events. Skill scores and other measures show that the present method is slightly less accurate at predicting M-X events than the NOAA method, but substantially more accurate at predicting X events, which are important contributors to space weather.

M. S. Wheatland

2005-05-14T23:59:59.000Z

430

Probabilistic wind power forecasting -European Wind Energy Conference -Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting  

E-Print Network [OSTI]

Probabilistic wind power forecasting - European Wind Energy Conference - Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting based on kernel density estimators J´er´emie Juban jeremie.juban@ensmp.fr; georges.kariniotakis@ensmp.fr Abstract Short-term wind power forecasting tools

Paris-Sud XI, Université de

431

Inclusion of In-Situ Velocity Measurements intotheUCSD Time-Dependent Tomography toConstrain and Better-Forecast Remote-Sensing Observations  

E-Print Network [OSTI]

to Constrain and Better-Forecast Remote-Sensing Observationsa decade to reconstruct and forecast coronal mass ejectionset al. , 2009b). In this forecast, IPS results are compared

Jackson, B. V.; Hick, P. P.; Bisi, M. M.; Clover, J. M.; Buffington, A.

2010-01-01T23:59:59.000Z

432

NREL: Resource Assessment and Forecasting Home Page  

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

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433

Rolling 12 Month Forecast November-2008.xls  

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

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434

EIA lowers forecast for summer gasoline prices  

Gasoline and Diesel Fuel Update (EIA)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) " ,"Click worksheet9,1,50022,3,,,,6,1,,781 2,328 2,683Diesel pricesArkansas Electricity ProfileSeptember

435

Sandia National Laboratories: forecast PV market trends  

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

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436

Sandia National Laboratories: forecasting plant health outcomes  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1development Sandia,evaluating wind-turbine/radar impactsfluid-structureforecasting

437

UPF Forecast | Y-12 National Security Complex  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear SecurityTensile Strain Switched Ferromagnetism in Layeredof EnergyLeaseEnergyUNCLASSIFIED 2UPDATED: Apps

438

Sandia National Laboratories: accurate electricity supply forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1development Sandia, NREL Release Wave EnergyLinksZ Newsaccelerating

439

NREL: Resource Assessment and Forecasting - Webmaster  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and

440

EA-273 Rainy River Energy Corporation | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Year in Review: TopEnergy DOEDealingVehicle1 Closing American Electric Power80A Rainy River

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

EA-273-A Rainy River Energy Corporation | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Year in Review: TopEnergy DOEDealingVehicle1 Closing American Electric Power80A Rainy River-A

442

Pearl River Valley El Pwr Assn | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories onFocusOski Energy LLCPascoag Utility DistrictPea RiverAwarenessPwr

443

Powder River Energy Corporation (Montana) | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Powerstories onFocusOskiPhilips ColorLoading map...ClimatePowder River Energy

444

Salmon River Electric Coop Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar PowerstoriesNrelPartnerType JumpJersey)Carbon DevelopmentCorp JumpRiver Electric

445

Salt River Electric Coop Corp | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar PowerstoriesNrelPartnerType JumpJersey)Carbon DevelopmentCorp JumpRiver

446

Flambeau River Biofuels Demonstration-Scale Biorefinery | Department of  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:YearRound-UpHeat PumpRecordFederal Registry CommentsOverviewEnergy Flambeau River Biofuels

447

Kings River Conservation District (KRCD) Solar Farm Solar Power Plant |  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429 Throttled (botOpen6Kentwood,George County is aKings River Conservation

448

MHK Projects/Cohansey River Tidal Energy | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision hasInformation Earth's Heat JumpIncMAK Technologies Jump to:BW2 Tidal <CETOCohansey River Tidal

449

MHK Technologies/Sabella River Generator | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision hasInformation Earth's Heat JumpIncMAKGalway Bay IEOWC < MHKPulse-Stream 120Sabella River

450

Upper Saddle River, New Jersey: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov PtyInformation UC 19-6-401 etWisconsin:california1070404°,Saddle River, New

451

Broad River Electric Coop, Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublic Utilities Address: 160Benin: EnergyBoston Area SolarConnecticut:659243° LoadingBroad River Electric Coop,

452

Self Potential Measurements At Raft River Geothermal Area (1983) | Open  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revisionEnvReviewNonInvasiveExplorationUT-g Grant ofRichardton AbbeyARaft River, Idaho | OpenSelawik

453

Snake River Plain Geothermal Region | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revisionEnvReviewNonInvasiveExplorationUT-g Grant ofRichardtonManagement,SmartestEnergy Ltd JumpSmokySnake River

454

Cuttings Analysis At Raft River Geothermal Area (1976) | Open Energy  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentratingRenewable Solutions LLC JumpCrow Lake Wind JumpCuttings Analysis At New River Area

455

Savannah River National Laboratory (SRNL) | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, searchVirginiaRooseveltVISanton GmbH JumpSatconSaulsbury,Savannah River

456

Singing River Elec Pwr Assn (Mississippi) | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty Ltd Jump to: navigation,Pvt LtdShrubSimpsonville, SouthSingaporeSinging River

457

South River Elec Member Corp | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty Ltd JumpGTZHolland, Illinois: EnergyInformation Park TownshipRiver Elec

458

South River, New Jersey: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty Ltd JumpGTZHolland, Illinois: EnergyInformation Park TownshipRiver

459

Mary's River SW Geothermal Project | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia: Energy Resources Jump to:46 - 429Lacey,(MonasterLowellis a town inRiver93.Information Martinez4°SW

460

Raft River Rural Elec Coop Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revisionEnvReviewNonInvasiveExplorationUT-g Grant of Access Permit5-ID-aREC SolarRadium Hot SpringsOpenRaft River

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Rogue River, Oregon: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, searchVirginia BlueRiverwoods,RockRipple, Indiana: EnergyRogersRogue River,

462

Fox River, Alaska: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublicIDAPowerPlantSitingConstruction.pdfNotify98.pdf JumpFlixMapFileFostoria,Chapel, Pennsylvania: EnergyRiver,

463

Cuivre River Electric Coop Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Power Basics (The following text isRica NRELPower Comm Jump to:Cuivre River

464

Escambia River Elec Coop, Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Power Basics (The followingDirectLowDiscussion-4,611,201.00fiscalRiver Elec

465

Fall River Rural Elec Coop Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Power Basics (TheEtelligence (Smart GridHome Kyoung's pictureFall River Rural

466

Field Mapping At Raft River Geothermal Area (1977) | Open Energy  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision has beenFfe2fb55-352f-473b-a2dd-50ae8b27f0a6 NoSanEnergy Information 4)OpenInformation Raft River

467

Sun River Electric Coop, Inc | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty LtdSteen, Minnesota:36052°, -97.6114217° LoadingEnergy Group LLCSun River

468

Three Rivers, Massachusetts: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:Seadov Pty LtdSteen,Ltd Jump JumpAl., 1978) |Thrall, Texas: EnergyThree Rivers

469

Survey of Variable Generation Forecasting in the West: August 2011 - June 2012  

SciTech Connect (OSTI)

This report surveyed Western Interconnection Balancing Authorities regarding their implementation of variable generation forecasting, the lessons learned to date, and recommendations they would offer to other Balancing Authorities who are considering variable generation forecasting. Our survey found that variable generation forecasting is at an early implementation stage in the West. Eight of the eleven Balancing Authorities interviewed began forecasting in 2008 or later. It also appears that less than one-half of the Balancing Authorities in the West are currently utilizing variable generation forecasting, suggesting that more Balancing Authorities in the West will engage in variable generation forecasting should more variable generation capacity be added.

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z

470

Price forecasting for U.S. cattle feeders: which technique to apply?  

E-Print Network [OSTI]

0. 04 0. 10 0. 08 0. 06 0. 06 0. 06 sAdvantaged forecast as it was compiled a calendar annual forecast with six months of actual data. All forecasts assume a January Benchmark. 27 Table 4 is the one-quarter ahead forecast comparison which... 12. 30 MAPE 0. 05 0. 05 0. 04 0. 04 0. 04 "All forecasts assume a July benchmark. 28 Table 5 is the two-quarter ahead forecast comparison which is for the second half of the calendar year (i. e. , July - December). The Futures Market...

Hicks, Geoff Cody

2012-06-07T23:59:59.000Z

471

Savannah River Technology Center monthly report, March 1993  

SciTech Connect (OSTI)

This report outlines progress and accomplishments in the following categories: Reactor; Tritium; Separations; Environmental; Waste Management; and General. Reactor topics are a summary of the Applied Physics Group`s work for the K-15.1 cycle; FLOWTRAN-TF code test certification, documentation, and reactor support; analysis of Mark 16B material from the L-Reactor Dissassembly Basin; the Reactor Tank inspection program T-Weld project; and Consistency Matrix support. Tritium topics are Leak Test Systems for the Container Management Facility; Replacement Tritium Facility technical issues tracking; transmission electron microscopy specimen preparation; the tritium aging effect on LaNi4.95A10.05 compressor material; and the life storage program. Separations topics are the F-Canyon Safety Analysis Report Addendum; an H-Canyon dissolver model; non-reactor assistance to the Nuclear Regulatory Commission; and onsite packaging criteria draft documentation. Environmental topics are Pond C studies with an underwater HPGe detector; the PREDICT run for the 1992 Annual Environmental Report; an integrated demonstration for cleanup of organic soils and groundwater at non-arid sites; and radio frequency soil heating -- fiberoptic temperature measurement probes. Waste Management topics are adding sodium titanate to in-tank precipitation simulants; lab-scale stripper column defoaming tests; the development of an extended sludge processing batch one model; and a canister temperature study. General topics are video image capture system -- drum inspection; old solvent tank characterization; plutonium storage containers; a glove box docking port; the development of SRS engineering and scientific software catalog; Nuclear Incident Monitor and criticality analysis for separations equipment development; and Nuclear Regulatory Commission assistance. Publications and other items of interest are cited at the end of this report.

Ferrell, J.M.

1993-03-01T23:59:59.000Z

472

Savannah River Technology Center monthly report, July 1995  

SciTech Connect (OSTI)

Progress is reported in the context of: tritium, separations, environmental, waste management, and general affairs. Emphasized topics include: metal hydrides, valves, sampling, water contamination, Par pond, F and H canyon tanks, tritium transport models, landfill stabilization, pumps, waste storage, and chemical analyzers.

Ferrell, J.M.

1995-07-01T23:59:59.000Z

473

Savannah River Technology Center monthly report: June 1994  

SciTech Connect (OSTI)

Short summaries are given for 53 projects concerned with tritium, separations, environmental, waste management and general topics. Included in the general topics are the following: High-level waste integrated flowsheet model; Scoping thermal analysis of failed equipment storage vault thermal model; Decision support system for in-tank precipitation benzene stripper experiment; Development of a liquid level bubbler for Defense Waste Processing Facility canyon vessels; Thermal testing of 9973 and 9975 series packagings; and Technical review of CD-1 cask for first time use.

Not Available

1994-06-01T23:59:59.000Z

474

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

1992-02-01T23:59:59.000Z

475

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01T23:59:59.000Z

476

Indiana Water Resources Research Center Annual Technical Report  

E-Print Network [OSTI]

on the state's Water Shortage Task Force. Most importantly, we have convened the first "Wabash River ResearchIndiana Water Resources Research Center Annual Technical Report FY 2008 Indiana Water Resources Overview: This report covers the activities of the Indiana Water Resources Research Center (IWRRC

477

July 6, 2012, Spring Operations Review Forum  

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

The Dalles of 450 kcfs. * The NW River Forecast Center's June 28, January-July Water Supply Forecast at The Dalles is 128.4 million acre-feet. * Oversupply Management Protocol...

478

Microsoft PowerPoint - SpringOpsCallHandout16May13 [Compatibility...  

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

hourly flow of 350 kcfs at JDA. The NW River Forecast Center's May 14th Official Water Supply Forecast for January-July at The Dalles is 98.0 million acre-feet. Increase from 92.4...

479

Regional tree growth and inferred summer climate in the Winnipeg River basin, Canada, since AD 1783  

E-Print Network [OSTI]

changes in summer climate within the Winnipeg River basin, Canada, since AD 1783. The basin drains parts the center of their range usually exhibit a more complex relationship with climate (Hughes, 2002Regional tree growth and inferred summer climate in the Winnipeg River basin, Canada, since AD 1783

Evans, Michael N.

480

Rivanna River Basin Commission (Virginia)  

Broader source: Energy.gov [DOE]

The Rivanna River Basin Commission is an independent local entity tasked with providing guidance for the stewardship and enhancement of the water quality and natural resources of the Rivanna River...

Note: This page contains sample records for the topic "river forecast center" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Yellowstone River Compact (North Dakota)  

Broader source: Energy.gov [DOE]

The Yellowstone River Compact, agreed to by the States of Montana, North Dakota, and Wyoming, provides for an equitable division and apportionment of the waters of the Yellowstone River, as well as...

482

P. Julien S. Ikeda River Engineering and  

E-Print Network [OSTI]

1 P. Julien S. Ikeda River Engineering and Stream Restoration Pierre Y. Julien Hong Kong - December 2004 River Engineering and Stream Restoration I - Stream Restoration Objectives Brief overview of River Engineering and Stream Restoration with focus on : 1. River Equilibrium; 2. River Dynamics; 3. River

Julien, Pierre Y.

483

Pecos River Ecosystem Monitoring Project  

E-Print Network [OSTI]

TR- 272 2004 Pecos River Ecosystem Monitoring Project C. Hart A. McDonald Texas Water Resources Institute Texas A&M University - 146 - 2003 Pecos River Ecosystem Monitoring Project... Charles R. Hart, Extension Range Specialist, Fort Stockton Alyson McDonald, Extension Assistant Hydrology, Fort Stockton SUMMARY The Pecos River Ecosystem Project is attempting to minimize the negative impacts of saltcedar on the river ecosystem...

McDonald, A.; Hart, C.

2004-01-01T23:59:59.000Z

484

Muddy River Restoration Project Begins  

E-Print Network [OSTI]

Muddy River Restoration Project Begins Page 5 #12;2 YANKEE ENGINEER February 2013 Yankee Voices of the Muddy River Restoration project. Inset photo: Flooding at the Muddy River. Materials provided by Mike Project Manager, on the passing of his father in law, Francis James (Jim) Murray, Jan. 9. ... to Laura

US Army Corps of Engineers

485

Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011  

SciTech Connect (OSTI)

This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

486

Forecasting 65+ travel : an integration of cohort analysis and travel demand modeling  

E-Print Network [OSTI]

Over the next 30 years, the Boomers will double the 65+ population in the United States and comprise a new generation of older Americans. This study forecasts the aging Boomers' travel. Previous efforts to forecast 65+ ...

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

487

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network [OSTI]

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

488

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network [OSTI]

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

489

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network [OSTI]

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark

2009-01-01T23:59:59.000Z

490

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network [OSTI]

late January 2008, extend its natural gas futures strip anComparison of AEO 2008 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

Bolinger, Mark

2008-01-01T23:59:59.000Z

491

Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010  

SciTech Connect (OSTI)

Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

Porter, K.; Rogers, J.

2010-04-01T23:59:59.000Z

492

Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs  

E-Print Network [OSTI]

Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. ...

Ganguly, Auroop Ratan

2002-01-01T23:59:59.000Z

493

Assembling the crystal ball : using demand signal repository to forecast demand  

E-Print Network [OSTI]

Improving forecast accuracy has positive effects on supply chain performance. Forecast accuracy can reduce inventory levels, increase customer service levels and responsiveness, or a combination of the two. However, the ...

Rashad, Ahmed (Ahmed Fathy Mustafa Rashad Abdelaal)

2013-01-01T23:59:59.000Z

494

Sixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast  

E-Print Network [OSTI]

............................................................................................................................... 12 Oil Price Forecast Range. The price of crude oil was $25 a barrel in January of 2000. In July 2008 it averaged $127, even approachingSixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast Introduction

495

E-Print Network 3.0 - air pollution forecast Sample Search Results  

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

forecast Search Powered by Explorit Topic List Advanced Search Sample search results for: air pollution forecast Page: << < 1 2 3 4 5 > >> 1 DISCOVER-AQ Outlook for Wednesay, July...

496

Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States  

E-Print Network [OSTI]

andvalidation. SolarEnergy. 73:5,307? Perez,R. ,irradianceforecastsforsolarenergyapplicationsbasedonforecastdatabase. SolarEnergy. 81:6,809?812.

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

497

Earnings Management Pressure on Audit Clients: Auditor Response to Analyst Forecast Signals  

E-Print Network [OSTI]

This study investigates whether auditors respond to earnings management pressure created by analyst forecasts. Analyst forecasts create an important earnings target for management, and professional standards direct auditors to consider how...

Newton, Nathan J.

2013-06-26T23:59:59.000Z

498

SHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS  

E-Print Network [OSTI]

SHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS Annette governing the insolation, forecasting of solar radiation makes the description of development of the cloud

Heinemann, Detlev

499

CCPP-ARM Parameterization Testbed Model Forecast Data  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

Klein, Stephen

500

Savannah River Site Robotics  

ScienceCinema (OSTI)

Meet Sandmantis and Frankie, two advanced robotic devices that are key to cleanup at Savannah River Site. Sandmantis cleans hard, residual waste off huge underground storage tanks. Frankie is equipped with unique satellite capabilities and sensing abilties that can determine what chemicals still reside in the tanks in a cost effective manner.

None

2012-06-14T23:59:59.000Z