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1

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

2

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer term time range of 30 minutes to 6 hours. As far as short term horizons are concerned, satellite data index images according to the Heliosat method, a semi-empirical methode to derive radiation from

Heinemann, Detlev

3

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

4

Combination of Long Term and Short Term Forecasts, with Application to Tourism  

E-Print Network [OSTI]

Combination of Long Term and Short Term Forecasts, with Application to Tourism Demand Forecasting that are combined. As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we con- sider 33 source countries, as well as the aggregate. The novel

Abu-Mostafa, Yaser S.

5

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

6

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

7

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks  

E-Print Network [OSTI]

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Stefan Achleitner, Tao Liu an advantage for output power prediction. Solar Energy Prediction System Our prediction model is based variability of more then 100 kW per minute. For practical usage of solar energy, predicting times of high

Cerpa, Alberto E.

8

Development of Short-term Demand Forecasting Model Application in Analysis of Resource Adequacy  

E-Print Network [OSTI]

Development of Short-term Demand Forecasting Model And its Application in Analysis of Resource will present the methodology, testing and results from short-term forecasting model developed by Northwest and applied the short-term forecasting model to Resource Adequacy analysis. These steps are presented below. 1

9

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

10

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

11

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

12

Short-term Wind Power Forecasting Using Advanced Statistical T.S. Nielsen1  

E-Print Network [OSTI]

Short-term Wind Power Forecasting Using Advanced Statistical Methods T.S. Nielsen1 , H. Madsen1 , H considered in the ANEMOS project for short-term fore- casting of wind power. The total procedure typically in for prediction of wind power or wind speed, estimating the uncertainty of the wind power forecast, and finally

Paris-Sud XI, Université de

13

ANN-based Short-Term Load Forecasting in Electricity Markets  

E-Print Network [OSTI]

ANN-based Short-Term Load Forecasting in Electricity Markets Hong Chen Claudio A. Ca~nizares Ajit1 Abstract--This paper proposes an Artificial Neu- ral Network (ANN)-based short-term load forecasting, electricity markets, spot prices, Artificial Neural Networks (ANN) I. Introduction Short

Cañizares, Claudio A.

14

Session: Short-term forecasting of wind power (BT2.5) Track: Technical  

E-Print Network [OSTI]

Session: Short-term forecasting of wind power (BT2.5) Track: Technical BEST PRACTICE IN THE USE) Armines / Ecole des Mines Short-term forecasting of wind power for about 48 hours in advance is an established technique by now. Any utility getting over a few percent wind power penetration is buying a system

15

A Hierarchical Bayesian Model for Improving Short-Term Forecasting of Hospital Demand by Including Meteorological  

E-Print Network [OSTI]

A Hierarchical Bayesian Model for Improving Short-Term Forecasting of Hospital Demand by Including Sarran4 Abstract The effect of weather on health has been widely researched, and the ability to forecast, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow

Sahu, Sujit K

16

Short-term Forecasting of Offshore Wind Farm Production Developments of the Anemos Project  

E-Print Network [OSTI]

Short-term Forecasting of Offshore Wind Farm Production ­ Developments of the Anemos Project J.a.brownsword@rl.ac.uk 6 Overspeed GmBH & Co.KG, 26129 Oldenburg, Germany Email: h.p.waldl@overspeed.de Key words: Offshore to the large dimensions of offshore wind farms, their electricity production must be known well in advance

Paris-Sud XI, Université de

17

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.

18

Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint  

SciTech Connect (OSTI)

Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

2012-09-01T23:59:59.000Z

19

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

20

Research on Short-term Load Forecasting of the Thermoelectric Boiler Based on a Dynamic RBF Neural Network  

E-Print Network [OSTI]

As thermal inertia is the key factor for the lag of thermoelectric utility regulation, it becomes very important to forecast its short-term load according to running parameters. In this paper, dynamic radial basis function (RBF) neural network...

Dai, W.; Zou, P.; Yan, C.

2006-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting  

SciTech Connect (OSTI)

The operators of electrical grids, sometimes referred to as Balancing Authorities (BA), typically make critical decisions on how to most reliably and economically balance electrical load and generation in time frames ranging from a few minutes to six hours ahead. At higher levels of wind power generation, there is an increasing need to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts on this time scale have typically been strongly dependent on short-term trends indicated by the time series of power production and meteorological data from a wind farm. Additional input information is often available from the output of Numerical Weather Prediction (NWP) models and occasionally from off-site meteorological towers in the region surrounding the wind generation facility. A widely proposed approach to improve short-term forecasts is the deployment of off-site meteorological towers at locations upstream from the wind generation facility in order to sense approaching wind perturbations. While conceptually appealing, it turns out that, in practice, it is often very difficult to derive significant benefit in forecast performance from this approach. The difficulty is rooted in the fact that the type, scale, and amplitude of the processes controlling wind variability at a site change from day to day if not from hour to hour. Thus, a location that provides some useful forecast information for one time may not be a useful predictor a few hours later. Indeed, some processes that cause significant changes in wind power production operate predominantly in the vertical direction and thus cannot be monitored by employing a network of sensors at off-site locations. Hence, it is very challenging to determine the type of sensors and deployment locations to get the most benefit for a specific short-term forecast application. Two tools recently developed in the meteorological research community have the potential to help determine the locations and parameters to measure in order to get the maximum positive impact on forecast performance for a particular site and short-term look-ahead period. Both tools rely on the use of NWP models to assess the sensitivity of a forecast for a particular location to measurements made at a prior time (i.e. the look-ahead period) at points surrounding the target location. The fundamental hypothesis is that points and variables with high sensitivity are good candidates for measurements since information at those points are likely to have the most impact on the forecast for the desired parameter, location and look-ahead period. One approach is called the adjoint method (Errico and Vukicevic, 1992; Errico, 1997) and the other newer approach is known as Ensemble Sensitivity Analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008). Both approaches have been tested on large-scale atmospheric prediction problems (e.g. forecasting pressure or precipitation over a relatively large region 24 hours ahead) but neither has been applied to mesoscale space-time scales of winds or any other variables near the surface of the earth. A number of factors suggest that ESA is better suited for short-term wind forecasting applications. One of the most significant advantages of this approach is that it is not necessary to linearize the mathematical representation of the processes in the underlying atmospheric model as required by the adjoint approach. Such a linearization may be especially problematic for the application of short-term forecasting of boundary layer winds in complex terrain since non-linear shifts in the structure of boundary layer due to atmospheric stability changes are a critical part of the wind power production forecast problem. The specific objective of work described in this paper is to test the ESA as a tool to identify measurement locations and variables that have the greatest positive impact on the accuracy of wind forecasts in the 0- to 6-hour look-ahead periods for the wind generation area of California's Tehachapi Pass during the warm (high generation) season. The paper is organized

Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

2010-02-21T23:59:59.000Z

22

Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)  

Reports and Publications (EIA)

The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

1998-01-01T23:59:59.000Z

23

Freeway Short-Term Traffic Flow Forecasting by Considering Traffic Volatility Dynamics and Missing Data Situations  

E-Print Network [OSTI]

, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model...

Zhang, Yanru

2012-10-19T23:59:59.000Z

24

Next Generation Short-Term Forecasting of Wind Power Overview of the ANEMOS Project.  

E-Print Network [OSTI]

of difficulties to the power system operation. This is due to the fluctuating nature of wind generation to the management of wind generation. Accurate and reliable forecasting systems of the wind production are widely

Boyer, Edmond

25

Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint  

SciTech Connect (OSTI)

Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

Hodge, B. M.; Lew, D.; Milligan, M.

2013-01-01T23:59:59.000Z

26

Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint  

SciTech Connect (OSTI)

The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

2012-09-01T23:59:59.000Z

27

Seismic Activity of the Earth, the Cosmological Vectorial Potential And Method of a Short-term Earthquakes Forecasting  

E-Print Network [OSTI]

To the foundation of a principally new short-term forecasting method there has been laid down a theory of surrounding us world's creation and of physical vacuum as a result of interaction of byuons - discrete objects. The definition of the byuon contains the cosmological vector-potential A_g - a novel fundamental vector constant. This theory predicts a new anisotropic interaction of nature objects with the physical vacuum. A peculiar "tap" to gain new energy (giving rise to an earthquake) are elementary particles because their masses are proportional to the modulus of some summary potential A_sum that contains potentials of all known fields. The value of A_sum cannot be larger than the modulus of A_g. In accordance with the experimental results a new force associated with A_sum ejects substance from the area of the weakened A_sum along a conical formation with the opening of 100 +- 10 and the axis directed along the vector A_sum. This vector has the following coordinates in the second equatorial coordinate system: right ascension alpha = 293 +- 10, declination delta = 36 +- 10. Nearly 100% probability of an earthquake (earthquakes of 6 points strong and more by the Richter scale) arises when in the process of the earth rotation the zenith vector of a seismically dangerous region and/or the vectorial potential of Earth's magnetic fields are in a certain way oriented relative to the vector A_g. In the work, basic models and standard mechanisms of earthquakes are briefly considered, results of processing of information on the earthquakes in the context of global spatial anisotropy caused by the existence of the vector A_g, are presented, and an analysis of them is given.

Yu. A. Baurov; Yu. A. Baurov; Yu. A. Baurov Jr.; A. A. Spitalnaya; A. A. Abramyan; V. A. Solodovnikov

2008-08-20T23:59:59.000Z

28

Short-Term Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

day Forecast -1.0 2012 2013 2014 OPEC countries North America Russia and Caspian Sea Latin America North Sea Other Non-OPEC Source: Short-Term Energy Outlook, November 2013 -1 0...

29

PostScript file created: April 17, 2005 Comparison of short-term and long-term earthquake forecast models  

E-Print Network [OSTI]

forecast models for southern California Agn`es Helmstetter1,3 , Yan Y. Kagan2 and David D. Jackson2 1, Columbia University, New York Abstract We consider the problem of forecasting earthquakes on two different time scales: years, and days. We evaluate some published forecast models on these time scales

Paris-Sud XI, Université de

30

Short-term energy outlook: Quarterly projections. Second quarter 1995  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the second quarter of 1995 through the fourth quarter of 1996. Values for the first quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the second quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service.

NONE

1995-05-02T23:59:59.000Z

31

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations the Southern Study Area  

SciTech Connect (OSTI)

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP)--Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 3 hours.

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John; Ancell, Brian; Brewster, Keith; Basu, Sukanta; Banunarayanan, Venkat; Hodge, Bri-Mathias; Flores, Isabel

2014-04-30T23:59:59.000Z

32

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework  

E-Print Network [OSTI]

. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser, forecast consumers, water managers and reservoir operators, have difficulty interpreting such products in a risk management framework A. Sankarasubramanian,1 Upmanu Lall,2 Francisco Assis Souza Filho,3

Arumugam, Sankar

33

Short-Term Energy Outlook September 2013  

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

September 2013 1 September 2013 Short-Term Energy Outlook (STEO) Highlights Monthly average crude oil prices increased for the fourth consecutive month in August 2013, as...

34

Short-term energy outlook, January 1999  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares the Short-Term Energy Outlook (energy supply, demand, and price projections) monthly. The forecast period for this issue of the Outlook extends from January 1999 through December 2000. Data values for the fourth quarter 1998, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the January 1999 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 28 figs., 19 tabs.

NONE

1999-01-01T23:59:59.000Z

35

Short-term energy outlook, July 1998  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from July 1998 through December 1999. Values for second quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the July 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. 28 figs., 19 tabs.

NONE

1998-07-01T23:59:59.000Z

36

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...

37

Short-term energy outlook, Quarterly projections. Third quarter 1993  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the third quarter of 1993 through the fourth quarter of 1994. Values for the second quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

NONE

1993-08-04T23:59:59.000Z

38

Short-Term Energy Outlook: Quarterly projections. Fourth quarter 1993  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the fourth quarter of 1993 through the fourth quarter of 1994. Values for the third quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications.

Not Available

1993-11-05T23:59:59.000Z

39

Short-term energy outlook quarterly projections: First quarter 1993  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.). The forecast period for this issue of the Outlook extends from the first quarter of 1993 through the fourth quarter of 1994. Values for the fourth quarter of 1992, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

Not Available

1993-02-03T23:59:59.000Z

40

Short-term energy outlook: Quarterly projections, Third quarter 1992  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The principal users of the Outlook are managers and energy analysts in private industry and government. The forecast period for this issue of the Outlook extends from the third quarter of 1992 through the fourth quarter of 1993. Values for the second quarter of 1992, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

Not Available

1992-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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.

42

Short-term energy outlook quarterly projections. First quarter 1994  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short- term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets.

Not Available

1994-02-07T23:59:59.000Z

43

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...

44

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)...

45

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%...

46

Short-term energy outlook: Quarterly projections, fourth quarter 1997  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for printed publication in January, April, July, and October in the Short-Term Energy Outlook. The details of these projections, as well as monthly updates on or about the 6th of each interim month, are available on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The forecast period for this issue of the Outlook extends from the fourth quarter of 1997 through the fourth quarter of 1998. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the fourth quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. 19 tabs.

NONE

1997-10-14T23:59:59.000Z

47

Short-term energy outlook annual supplement, 1993  

SciTech Connect (OSTI)

The Short-Term Energy Outlook Annual Supplement (supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

NONE

1993-08-06T23:59:59.000Z

48

Short-term energy outlook, annual supplement 1994  

SciTech Connect (OSTI)

The Short-Term Energy Outlook Annual Supplement (Supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

Not Available

1994-08-01T23:59:59.000Z

49

Short-term energy outlook, April 1999  

SciTech Connect (OSTI)

The forecast period for this issue of the Outlook extends from April 1999 through December 2000. Data values for the first quarter 1999, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the April 1999 version of the Short-Term Integrated forecasting system (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 25 figs., 19 tabs.

NONE

1999-04-01T23:59:59.000Z

50

Short-term energy outlook. Quarterly projections, first quarter 1995  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). The forecast period for this issue of the Outlook extends from the first quarter of 1995 through the fourth quarter of 1996. Values for the fourth quarter of 1994, however, are preliminary EIA estimates or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the first quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. The EIA model is available on computer tape from the National Technical Information Service.

Not Available

1995-02-01T23:59:59.000Z

51

Short-term energy outlook. Quarterly projections, first quarter 1996  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Outlook. The forecast period for this issue of the Outlook extends from the first quarter of 1996 through the fourth quarter of 1997. Values for the fourth quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the first quarter 1996 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook.

NONE

1996-02-01T23:59:59.000Z

52

Short-term energy outlook: Quarterly projections, second quarter 1997  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for publication in January, April, July, and October in the Outlook. The forecast period for this issue of the Outlook extends from the second quarter of 1997 through the fourth quarter of 1998. Values for the first quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the second quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the Short-Term Integrated Forecasting System (STIFS). 34 figs., 19 tabs.

NONE

1997-04-01T23:59:59.000Z

53

Short-term energy outlook. Quarterly projections, Third quarter 1994  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202). The feature article for this issue is Demand, Supply and Price Outlook for Reformulated Gasoline, 1995.

Not Available

1994-08-02T23:59:59.000Z

54

Prediction of wind speed profiles for short-term forecasting in the offshore environment R.J. Barthelmie and G. Giebel  

E-Print Network [OSTI]

in the forecast wind speed/power output might be anticipated using a directional rather than a constant bias for the calibration phase. A further advantage is that statistical techniques can predict power output directly rather than having to take the additional step of predicting power output from wind speed through the power

55

Short-term energy outlook. Quarterly projections, third quarter 1996  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in January, April, July, and October in the Outlook. The forecast period for this issue of the Outlook extends from the third quarter of 1996 through the fourth quarter of 1997. Values for the second quarter of 1996, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled in the third quarter 1996 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service.

NONE

1996-07-01T23:59:59.000Z

56

Short-term energy outlook. Quarterly projections, Third quarter 1995  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. The forecast period for this issue of the Outlook extends from the third quarter of 1995 through the fourth quarter of 1996. Values for the second quarter of 1995, however, are preliminary EIA estimates.

NONE

1995-08-02T23:59:59.000Z

57

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations the Northern Study Area.  

SciTech Connect (OSTI)

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times. A comprehensive analysis of wind energy forecast errors for the various model-based power forecasts was presented for a suite of wind energy ramp definitions. The results compiled over the year-long study period showed that the power forecasts based on the research models (ESRL_RAP, HRRR) more accurately predict wind energy ramp events than the current operational forecast models, both at the system aggregate level and at the local wind plant level. At the system level, the ESRL_RAP-based forecasts most accurately predict both the total number of ramp events and the occurrence of the events themselves, but the HRRR-based forecasts more accurately predict the ramp rate. At the individual site level, the HRRR-based forecasts most accurately predicted the actual ramp occurrence, the total number of ramps and the ramp rates (40-60% improvement in ramp rates over the coarser resolution forecast

Finley, Cathy [WindLogics

2014-04-30T23:59:59.000Z

58

Short-term energy outlook. Quarterly projections, 2nd quarter 1994  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. The forecast period for this issue of the Outlook extends from the second quarter of 1994 through the fourth quarter of 1995. Values for the first quarter of 1994, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available. The historical energy data, compiled into the second quarter 1994 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the STIFS. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. The EIA model is available on computer tape from the National Technical Information Service.

Not Available

1994-05-01T23:59:59.000Z

59

Short-term energy outlook. Volume 2. Methodology  

SciTech Connect (OSTI)

This volume updates models and forecasting methodologies used and presents information on new developments since November 1981. Chapter discusses the changes in forecasting methodology for motor gasoline demand, electricity sales, coking coal, and other petroleum products. Coefficient estimates, summary statistics, and data sources for many of the short-term energy models are provided. Chapter 3 evaluates previous short-term forecasts for the macroeconomic variables, total energy, petroleum supply and demand, coal consumption, natural gas, and electricity fuel shares. Chapter 4 reviews the relationship of total US energy consumption to economic activity between 1960 and 1981.

Not Available

1982-05-01T23:59:59.000Z

60

Short-term energy outlook, quarterly projections, first quarter 1998  

SciTech Connect (OSTI)

The forecast period for this issue of the Outlook extends from the first quarter of 1998 through the fourth quarter of 1999. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the first quarter 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are adjusted by EIA to reflect EIA assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 24 figs., 19 tabs.

NONE

1998-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Short-Term Energy Outlook  

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

Chart Gallery for April 2015 Short-Term Energy Outlook U.S. Energy Information Administration Independent Statistics & Analysis 0 20 40 60 80 100 120 140 160 180 200 220 Jan 2014...

62

Short-term energy outlook. Quarterly projections, second quarter 1996  

SciTech Connect (OSTI)

The Energy Information Administration prepares quarterly, short-term energy supply, demand, and price projections. The forecasts in this issue cover the second quarter of 1996 through the fourth quarter of 1997. Changes to macroeconomic measures by the Bureau of Economic Analysis have been incorporated into the STIFS model used.

NONE

1996-04-01T23:59:59.000Z

63

Short-term energy outlook, October 1998. Quarterly projections, 1998 4. quarter  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the Internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from October 1998 through December 1999. Values for third quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the October 1998 version of the Short-term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

NONE

1998-10-01T23:59:59.000Z

64

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term Energy33

65

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term

66

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term(STEO)

67

Short-Term Energy Outlook Model Documentation: Petroleum Product Prices Module  

Reports and Publications (EIA)

The petroleum products price module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. average wholesale and retail price forecasts for motor gasoline, diesel fuel, heating oil, and jet fuel.

2015-01-01T23:59:59.000Z

68

Short-term energy outlook, Annual supplement 1995  

SciTech Connect (OSTI)

This supplement is published once a year as a complement to the Short- Term Energy Outlook, Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. Chap. 2 analyzes the response of the US petroleum industry to the recent four Federal environmental rules on motor gasoline. Chap. 3 compares the EIA base or mid case energy projections for 1995 and 1996 (as published in the first quarter 1995 Outlook) with recent projections made by four other major forecasting groups. Chap. 4 evaluates the overall accuracy. Chap. 5 presents the methology used in the Short- Term Integrated Forecasting Model for oxygenate supply/demand balances. Chap. 6 reports theoretical and empirical results from a study of non-transportation energy demand by sector. The empirical analysis involves the short-run energy demand in the residential, commercial, industrial, and electrical utility sectors in US.

NONE

1995-07-25T23:59:59.000Z

69

Forecasting the monthly volume of orders for southern pine lumber - an econometric model  

E-Print Network [OSTI]

the orders estimates should be minimal, and the benefits of forecasting should exceed the costs. Included in this matter of convenience is the mathematical simplicity of the computations and their evaluation. With these essential characteristics in mind... FORECASTING THE MONTHLY VOLUME OF ORDERS FOR SOUTHERN PINE LUMBER - AH ECONOMETRIC MODEL A Thesis by BEN DOUGLAS JACKSON Submitted to the Graduate College of Texas ASM University in Partial fulfillment of the requirement for the degree...

Jackson, Ben Douglas

1973-01-01T23:59:59.000Z

70

QIP Short Term Course Application of Renewable  

E-Print Network [OSTI]

QIP Short Term Course on Application of Renewable Energy sources (December 11-17, 2013) Course mitigation and credit · PV modules/arrays · Batteries · Hybrid systems (wind, hydro etc.) · Life cycle cost:gntiwari@ces.iitd.ernet.in Application Form QIP Short-Term Course on Applications of Renewable Energy Sources (December 11-17, 2013) Name

Kumar, M. Jagadesh

71

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

72

Electricity storage for short term power system service (Smart...  

Open Energy Info (EERE)

Electricity storage for short term power system service (Smart Grid Project) Jump to: navigation, search Project Name Electricity storage for short term power system service...

73

SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS  

E-Print Network [OSTI]

SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS Hans. For the conventional power park, the power production of the wind turbines presents a fluctuating 'negative load PRODUCTION OF WIND TURBINES For the forecast of the power production of wind turbines two approaches may

Heinemann, Detlev

74

Short-term energy outlook, quarterly projections, second quarter 1998  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections. The details of these projections, as well as monthly updates, are available on the Internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The paper discusses outlook assumptions; US energy prices; world oil supply and the oil production cutback agreement of March 1998; international oil demand and supply; world oil stocks, capacity, and net trade; US oil demand and supply; US natural gas demand and supply; US coal demand and supply; US electricity demand and supply; US renewable energy demand; and US energy demand and supply sensitivities. 29 figs., 19 tabs.

NONE

1998-04-01T23:59:59.000Z

75

Short Term Energy Outlook, February 2003  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term Energy

76

Short Term Energy Outlook, January 2003  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term Energy3

77

Short Term Energy Outlook, March 2003  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1 Short-Term Energy33

78

Short-Term Energy Outlook- May 2003  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34Summer3 1 Short-Term

79

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...

80

Short-term energy outlook quarterly projections. Third quarter 1997  

SciTech Connect (OSTI)

This document presents the 1997 third quarter short term energy projections. Information is presented for fossil fuels and renewable energy.

NONE

1997-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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.


81

Semester, Academic Year and Short Term SUNY Programs  

E-Print Network [OSTI]

Semester, Academic Year and Short Term SUNY Programs: Asia #12;1 Table of Contents How to Use Year 10 Japan Short-term 12 Korea Semester & Academic Year 13 Korea Short-term 17 Programs in Other Contact Information 23 How to Use this Booklet This handout contains listings of all the programs offered

Suzuki, Masatsugu

82

SUNY Programs: Semester, Academic Year and Short Term  

E-Print Network [OSTI]

SUNY Programs: Italy Semester, Academic Year and Short Term #12;1 Table of Contents How to Use This Booklet 1 A Brief Overview 2 Semester and Academic Year Programs 3 Short Term Programs 8 Contact of programs offered in Italy by SUNY campuses. These listings provide a summary about the basic

Suzuki, Masatsugu

83

SUNY Programs: Semester, Academic Year and Short Term  

E-Print Network [OSTI]

SUNY Programs: France Semester, Academic Year and Short Term #12;1 Table of Contents How to Use This Booklet 1 A Brief Overview 2 Semester and Academic Year Programs 3 Short Term Programs 6 SUNY Programs in Canada and other Francophone Locations 9 Recommended non-SUNY Program 11 Contact Information for all SUNY

Suzuki, Masatsugu

84

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

85

Short-Term Load Forecasting This paper discusses the state of the art in short-term load fore-  

E-Print Network [OSTI]

spectrum of time intervals. In therange of seconds, when load variationsare small and random, the automatic by a number of generation control functions such as hydro scheduling, unit commitment, hydro-ther- mal present, functions such as fuel, hydro, and maintenance scheduling are performed to ensure that the load

Gross, George

86

Short-Term Energy Outlook Supplement: Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34Summer 2013

87

Short-Term Operation Scheduling in Renewable-Powered Microgrids  

E-Print Network [OSTI]

of demand forecast error. Mean of wind power forecast error. Cooling time constant of a unit. Variance of demand forecast error. Variance of wind power forecast error. Step size of the subgradient method. UC. Cold start-up cost of a unit. Dual function. Forecasted demand in time interval . Emission function

Bornemann, Jens

88

Short-term CO? abatement in the European power sector  

E-Print Network [OSTI]

This paper focuses on the possibilities for short term abatement in response to a CO2 price through fuel switching in the European power sector. The model E-Simulate is used to simulate the electricity generation in Europe ...

Delarue, Erik D.

2008-01-01T23:59:59.000Z

89

Vehicle Technologies Office: Short-Term Lightweight Materials Research  

Broader source: Energy.gov [DOE]

In the short term, replacing heavy steel components with materials such as high-strength steel, aluminum, or glass fiber-reinforced polymer composites can decrease component weight by 10-60 percent.

90

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...

91

Short-term production and synoptic influences on atmospheric 7  

E-Print Network [OSTI]

Short-term production and synoptic influences on atmospheric 7 Be concentrations Ilya G. Usoskin,1; published 21 March 2009. [1] Variations of the cosmogenic radionuclide 7 Be in the global atmosphere the variations in the 7 Be concentration in the atmosphere for the period from 1 January to 28 February 2005

92

SHORT-TERM GENERATION ASSET VALUATION: A REAL OPTIONS APPROACH  

E-Print Network [OSTI]

using real options to value power plants with unit commitment constraints over a short-term period. We forward-moving Monte Carlo simulation with backward-moving dynamic programming. We assume that the power significantly overvalue a power plant. With deregulation of the electricity industry a global trend, utilities

Tseng, Chung-Li

93

SUNY Programs: Semester, Academic Year and Short Term  

E-Print Network [OSTI]

SUNY Programs: Spain Semester, Academic Year and Short Term #12;1 Table of Contents How to Use of programs offered in Spain by SUNY campuses. These listings provide a summary about the characteristics by the SUNY campuses in Spain. In addition, there are some excellent programs in Spain outside the SUNY system

Suzuki, Masatsugu

94

Management and Conservation Short-Term Impacts of Wind Energy  

E-Print Network [OSTI]

Management and Conservation Short-Term Impacts of Wind Energy Development on Greater Sage associated with wind energy development on greater sage-grouse populations. We hypothesized that greater sage-grouse nest, brood, and adult survival would decrease with increasing proximity to wind energy infrastructure

Beck, Jeffrey L.

95

ANALYSIS OF SHORT-TERM SOLAR RADIATION DATA Gayathri Vijayakumar  

E-Print Network [OSTI]

ANALYSIS OF SHORT-TERM SOLAR RADIATION DATA Gayathri Vijayakumar Sanford A. Klein William A beckman@engr.wisc.edu ABSTRACT Solar radiation data are available for many locations on an hourly basis annual performance, although solar radiation can exhibit wide variations during an hour. Variations

Wisconsin at Madison, University of

96

analyzing short-term noise: Topics by E-print Network  

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

122 Short term effects of moderate carbon prices on land use in the New Zealand emissions trading Environmental Sciences and Ecology Websites Summary: Short term effects of...

97

Measuring Short-term Air Conditioner Demand Reductions for Operations and Settlement  

E-Print Network [OSTI]

Measuring Short-term Air Conditioner Demand Reductions forMeasuring Short-term Air Conditioner Demand Reductions forpilots have shown that air conditioner (AC) electric loads

Bode, Josh

2013-01-01T23:59:59.000Z

98

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

99

Estimating long-term mean winds from short-term wind data  

SciTech Connect (OSTI)

The estimation of long-term mean winds from short-term data is especially important in the area of wind energy. It is desirable to obtain reliable estimates of the long-term wind speed from as short a period of on-site measurements as possible. This study examined seven different methods of estimating the long-term average wind speed and compared the performance of these techniques. Three linear, three weather pattern, and one eigenvector methods were compared for measurement periods ranging from 3 months to 36 months. Average errors, both relative and absolute, and the rms errors in the techniques were determined. The best technique for less than 12 months of measurement was the eigenvector method using weekly mean wind speeds. However, this method was only slightly better than the linear adjusted method. When 12 or more months of data were used, the difference in errors between techniques was found to be slight.

Barchet, W.R.; Davis, W.E.

1983-08-01T23:59:59.000Z

100

Short-term hydroelectric generation model. Model documentation report  

SciTech Connect (OSTI)

The purpose of this report is to define the objectives of the Energy Information Administration`s (EIA) Short-Term Hydroelectric Generation Model (STHGM), describe its basic approach, and to provide details on the model structure. This report is intended as a reference document for model analysts, users, and the general public. Documentation of the model is in accordance with the EIA`s legal obligation to provide adequate documentation in support of its models.

NONE

1996-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Conditional Reliability Modeling of Short-term River Basin Management  

E-Print Network [OSTI]

CONDITIONAL RELIABILITY MODELING OF SHORT-TERM RIVER BASIN MANAGEMENT ASCE Texas Section Spring Meeting 2003 By: A.Andr?s Salazar, Ph.D. Freese and Nichols, Inc. and Ralph A. Wurbs, P.E., Ph.D. Texas A&M University 2 TEXAS WATER AVAILABITY MODEL...-88Year Storage (x 1000 ac-ft) Periods without shortage = 657 out of 672 (97.8%) What is the probability of satisfying demand when reservoir falls below 100,000 ac-ft? 9 CONDITIONAL RELIABILITY Statistical analysis of small sequences. Simulation 1...

Salazar, A.; Wurbs, R. A.

2003-01-01T23:59:59.000Z

102

Natural Gas Summary from the Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S.30 2013 Macroeconomic team:6-2015 Illinois NA NA NAIn the May 2003 Short-Term

103

Short-Term Energy and Winter Fuels Outlook October 2013  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34Summer3 1 Short-Term3

104

Short-Term Test Results: Multifamily Home Energy Efficiency Retrofit  

SciTech Connect (OSTI)

Multifamily deep energy retrofits (DERs) represent great potential for energy savings, while also providing valuable insights on research-generated efficiency measures, cost-effectiveness metrics, and risk factor strategies for the multifamily housing industry. The Bay Ridge project is comprised of a base scope retrofit with a goal of achieving 30% savings (relative to pre-retrofit), and a DER scope with a goal of 50% savings (relative to pre-retrofit). The base scope has been applied to the entire complex, except for one 12-unit building which underwent the DER scope. Findings from the implementation, commissioning, and short-term testing at Bay Ridge include air infiltration reductions of greater than 60% in the DER building; a hybrid heat pump system with a Savings to Investment Ratio (SIR) > 1 (relative to a high efficiency furnace) which also provides the resident with added incentive for energy savings; and duct leakage reductions of > 60% using an aerosolized duct sealing approach. Despite being a moderate rehab instead of a gut rehab, the Bay Ridge DER is currently projected to achieve energy savings ? 50% compared to pre-retrofit, and the short-term testing supports this estimate.

Lyons, J.

2013-01-01T23:59:59.000Z

105

Crude oil prices and petroleum inventories : remedies for a broken oil price forecasting model.  

E-Print Network [OSTI]

??The empirical relationship between crude oil prices and petroleum inventories has been exploited in a number of short-term oil price forecasting models. Some of the (more)

Grimstad, Dan

2007-01-01T23:59:59.000Z

106

aneurysm repair short-term: Topics by E-print Network  

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

is socially excessive. The empirical analysis shows that the short-term debt to reserves ratio is a robust predictor of -nancial crises, and that greater short-term...

107

Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices  

E-Print Network [OSTI]

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

108

SULFURIC ACID REMOVAL PROCESS EVALUATION: SHORT-TERM RESULTS  

SciTech Connect (OSTI)

The objective of this project is to demonstrate the use of alkaline reagents injected into the furnace of coal-fired boilers as a means of controlling sulfuric acid emissions. Sulfuric acid controls are becoming of increasing interest to utilities with coal-fired units for a number of reasons. Sulfuric acid is a Toxic Release Inventory species, a precursor to acid aerosol/condensable emissions, and can cause a variety of plant operation problems such as air heater plugging and fouling, back-end corrosion, and plume opacity. These issues will likely be exacerbated with the retrofit of SCR for NOX control on some coal-fired plants, as SCR catalysts are known to further oxidize a portion of the flue gas SO{sub 2} to SO{sub 3}. The project is testing the effectiveness of furnace injection of four different calcium- and/or magnesium-based alkaline sorbents on full-scale utility boilers. These reagents have been tested during four one- to two-week tests conducted on two FirstEnergy Bruce Mansfield Plant units. One of the sorbents tested was a magnesium hydroxide slurry produced from a wet flue gas desulfurization system waste stream, from a system that employs a Thiosorbic{reg_sign} Lime scrubbing process. The other three sorbents are available commercially and include dolomite, pressure-hydrated dolomitic lime, and commercial magnesium hydroxide. The dolomite reagent was injected as a dry powder through out-of-service burners, while the other three reagents were injected as slurries through air-atomizing nozzles into the front wall of upper furnace, either across from the nose of the furnace or across from the pendant superheater tubes. After completing the four one- to two-week tests, the most promising sorbents were selected for longer-term (approximately 25-day) full-scale tests. The longer-term tests are being conducted to confirm the effectiveness of the sorbents tested over extended operation and to determine balance-of-plant impacts. This reports presents the results of the short-term tests; the long-term test results will be reported in a later document. The short-term test results showed that three of the four reagents tested, dolomite powder, commercial magnesium hydroxide slurry, and byproduct magnesium hydroxide slurry, were able to achieve 90% or greater removal of sulfuric acid compared to baseline levels. The molar ratio of alkali to flue gas sulfuric acid content (under baseline conditions) required to achieve 90% sulfuric acid removal was lowest for the byproduct magnesium hydroxide slurry. However, this result may be confounded because this was the only one of the three slurries tested with injection near the top of the furnace across from the pendant superheater platens. Injection at the higher level was demonstrated to be advantageous for this reagent over injection lower in the furnace, where the other slurries were tested.

Gary M. Blythe; Richard McMillan

2002-03-04T23:59:59.000Z

109

SULFURIC ACID REMOVAL PROCESS EVALUATION: SHORT-TERM RESULTS  

SciTech Connect (OSTI)

The objective of this project is to demonstrate the use of alkaline reagents injected into the furnace of coal-fired boilers as a means of controlling sulfuric acid emissions. Sulfuric acid controls are becoming of increasing interest to utilities with coal-fired units for a number of reasons. Sulfuric acid is a Toxic Release Inventory species, a precursor to acid aerosol/condensable emissions, and can cause a variety of plant operation problems such as air heater plugging and fouling, back-end corrosion, and plume opacity. These issues will likely be exacerbated with the retrofit of SCR for NO{sub x} control on some coal-fired plants, as SCR catalysts are known to further oxidize a portion of the flue gas SO{sub 2} to SO{sub 3}. The project is testing the effectiveness of furnace injection of four different calcium- and/or magnesium-based alkaline sorbents on full-scale utility boilers. These reagents have been tested during four one- to two-week tests conducted on two First Energy Bruce Mansfield Plant units. One of the sorbents tested was a magnesium hydroxide slurry produced from a wet flue gas desulfurization system waste stream, from a system that employs a Thiosorbic{reg_sign} Lime scrubbing process. The other three sorbents are available commercially and include dolomite, pressure-hydrated dolomitic lime, and commercial magnesium hydroxide. The dolomite reagent was injected as a dry powder through out-of-service burners, while the other three reagents were injected as slurries through air-atomizing nozzles into the front wall of upper furnace, either across from the nose of the furnace or across from the pendant superheater tubes. After completing the four one- to two-week tests, the most promising sorbents were selected for longer-term (approximately 25-day) full-scale tests. The longer-term tests are being conducted to confirm the effectiveness of the sorbents tested over extended operation and to determine balance-of-plant impacts. This reports presents the results of the short-term tests; the long-term test results will be reported in a later document. The short-term test results showed that three of the four reagents tested, dolomite powder, commercial magnesium hydroxide slurry, and byproduct magnesium hydroxide slurry, were able to achieve 90% or greater removal of sulfuric acid compared to baseline levels. The molar ratio of alkali to flue gas sulfuric acid content (under baseline conditions) required to achieve 90% sulfuric acid removal was lowest for the byproduct magnesium hydroxide slurry. However, this result may be confounded because this was the only one of the three slurries tested with injection near the top of the furnace across from the pendant superheater platens. Injection at the higher level was demonstrated to be advantageous for this reagent over injection lower in the furnace, where the other slurries were tested.

Gary M. Blythe; Richard McMillan

2002-02-04T23:59:59.000Z

110

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

111

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

112

Short-Term Generation Asset Valuation Chung-Li Tseng, Graydon Barz  

E-Print Network [OSTI]

Short-Term Generation Asset Valuation Chung-Li Tseng, Graydon Barz Department of Civil Engineering 94305, USA chungli@eng.umd.edu, gbarz@leland.stanford.edu Abstract In this paper we present a method for valuing a power plant over a short-term period using Monte Carlo sim- ulation. The power plant valuation

113

Short term thermal energy storage Institut fr Kernenergetik und Energiesysteme, University of Stuttgart, Stuttgart, FRG  

E-Print Network [OSTI]

477 Short term thermal energy storage A. Abhat Institut für Kernenergetik und Energiesysteme the problem of short term thermal energy storage for low temperature solar heating applications. The techniques of sensible and latent heat storage are discussed, with particular emphasis on the latter

Paris-Sud XI, Université de

114

Short-term effects of salinity declines on juvenile hard clams, Mercenaria mercenaria.  

E-Print Network [OSTI]

be compounded or mitigated by other factors, such as other environmental conditions or handling effects. #12Short-term effects of salinity declines on juvenile hard clams, Mercenaria mercenaria. Final report to Florida Sea Grant, for a Program Development Award Project title: Short-term effects of rapid salinity

Florida, University of

115

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.

116

Short-Term Effects of Air Pollution on Wheeze in Asthmatic Children in Fresno, California  

E-Print Network [OSTI]

of winter air pollution on respira- tory health of asthmaticChildrens Health Short-Term Effects of Air Pollution onEnvironmental Health Perspectives Effects of air pollution

2010-01-01T23:59:59.000Z

117

An Exploration of Participant Motives and Motivational Tensions in Short-Term Medical Service Trips  

E-Print Network [OSTI]

Short-term medical service trips (MSTs) are an increasingly popular, although not new, way for healthcare providers from high-income countries (HICs) to provide healthcare in low- and middle-income countries (LMICs). In ...

Sykes, Kevin James

2014-05-31T23:59:59.000Z

118

Short-term Migration, Rural Workfare Programs and Urban Labor Markets: Evidence from India  

E-Print Network [OSTI]

, a simple calibration exercise reveals that small changes in short-term migration can have large impacts of migration in developing countries (Banerjee and Duo, 2007; Badiani and Sar, 2009; Morten, 2012). In 2007

Bandyopadhyay, Antar

119

Short-term and long-term reliability studies in the deregulated power systems  

E-Print Network [OSTI]

-term reliability in deregulated power systems. Short-term reliability is for operational purposes and is mainly concerned with security. Thus the way energy is dispatched and the actions the system operator takes to remedy an insecure system state...

Li, Yishan

2006-04-12T23:59:59.000Z

120

Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance  

E-Print Network [OSTI]

Review Short-term irradiance variability: Preliminary estimation of station pair correlation, 2010; SMUD, 2010; IEA, 2010). In a recently published article, Hoff and Perez (2010a,b) advanced

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

SPE 124332 (revised) Hierarchical Long-Term and Short-Term Production Optimization  

E-Print Network [OSTI]

. In our study we used a 3-dimensional reservoir in a fluvial depositional environment with a production at maximizing short-term production. The optimal life-cycle waterflooding strategy that includes short

Van den Hof, Paul

122

Characterizing short-term stability for Boolean networks over any distribution of transfer functions  

E-Print Network [OSTI]

We present a characterization of short-term stability of random Boolean networks under \\emph{arbitrary} distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. It has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.

C. Seshadhri; Andrew M. Smith; Yevgeniy Vorobeychik; Jackson Mayo; Robert C. Armstrong

2014-09-15T23:59:59.000Z

123

DOE/EIA-0202(85/1Q) Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q) Short-Term Energy

124

DOE/EIA-0202(85/2Q) Short-Term Energy Outlook  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q) Short-Term

125

DOE/EIA-0202(85/3Q) Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q) Short-Term3Q)

126

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

127

Managing Short-Term Electricity Contracts Under Uncertainty: A Minimax Approach  

E-Print Network [OSTI]

, the price of which follows supply and demand imbalances. Electricity prices, which were tightly controlled that occurred in the Midwest during the week of June 22, 1998, when the day-ahead electricity price departedManaging Short-Term Electricity Contracts Under Uncertainty: A Minimax Approach Samer Takriti

Ahmed, Shabbir

128

Primal-Dual Interior Point Method Applied to the Short Term Hydroelectric Scheduling Including a  

E-Print Network [OSTI]

Primal-Dual Interior Point Method Applied to the Short Term Hydroelectric Scheduling Including that minimizes losses in the transmission and costs in the generation of a hydroelectric power system, formulated such perturbing parameter. Keywords-- Hydroelectric power system, Network flow, Predispatch, Primal-dual interior

Oliveira, Aurélio R. L.

129

Interference of a short-term exposure to nitrogen dioxide with allergic airways responses to allergenic  

E-Print Network [OSTI]

Interference of a short-term exposure to nitrogen dioxide with allergic airways responses, 4 (2002) 251-260" DOI : 10.1080/096293502900000113 #12;Abstract Nitrogen dioxide (NO2) is a common and may depend to concentration of pollutant. Keywords: Mouse model of asthma; nitrogen dioxide; air

Paris-Sud XI, Université de

130

Ethical Considerations for Short-term Experiences by Trainees in Global Health  

E-Print Network [OSTI]

-constrained health care set- tings, trainees from resource-replete environments may have inflated ideas aboutCOMMENTARY Ethical Considerations for Short-term Experiences by Trainees in Global Health John A. Crump, MB, ChB, DTM&H Jeremy Sugarman, MD, MPH, MA A CADEMIC GLOBAL HEALTH PROGRAMS ARE BURGEON- ing.1

Tipple, Brett

131

SHORT-TERM EFFECTS OF SOIL AMENDMENT WITH TREE LEGUME BIOMASS ON CARBON AND NITROGEN  

E-Print Network [OSTI]

SHORT-TERM EFFECTS OF SOIL AMENDMENT WITH TREE LEGUME BIOMASS ON CARBON AND NITROGEN IN PARTICLE-to-N ratio of the added plant material seems to control the eects of soil amendment with tree legume biomass to the total quantity of C and N pre- sent. Physical fractionation of SOM can help to identify more active

Lehmann, Johannes

132

Status and evaluation of hybrid electric vehicle batteries for short term applications. Final report  

SciTech Connect (OSTI)

The objective of this task is to compile information regarding batteries which could be use for electric cars or hybrid vehicles in the short term. More specifically, this study applies lead-acid batteries and nickel-cadmium battery technologies which are more developed than the advanced batteries which are presently being investigated under USABC contracts and therefore more accessible in production efficiency and economies of scale. Moreover, the development of these batteries has advanced the state-of-the-art not only in terms of performance and energy density but also in cost reduction. The survey of lead-acid battery development took the biggest part of the effort, since they are considered more apt to be used in the short-term. Companies pursuing the advancement of lead-acid batteries were not necessarily the major automobile battery manufacturers. Innovation is found more in small or new companies. Other battery systems for short-term are discussed in the last part of this report. We will review the various technologies investigated, their status and prognosis for success in the short term.

Himy, A. [Westinghouse Electric Co., Pittsburgh, PA (United States). Machinery Technology Div.

1995-07-01T23:59:59.000Z

133

A Dual Algorithm for the Short Term Power Production Planning with Network  

E-Print Network [OSTI]

, with and without energy losses. In this model the variables are phase voltage angles and active power generated de Ingenier'ia, Montevideo, Uruguay 1 #12; 1 Introduction The demand of electric power in a country in savings of order of a million dollar per year in a medium size utility. Short term planning is performed

134

Longitudinal Analysis of Short term Bronchiolitis Air Pollution Association using Semi Parametric Models  

E-Print Network [OSTI]

pollution, semi parametric models. 1.1 Introduction Time-series studies of air pollution and health was an overestimation of the eect of air pollution on health. More recently, in a issue of Epidemiology, Ramsay et al1 Longitudinal Analysis of Short term Bronchiolitis Air Pollution Association using Semi Parametric

Mesbah, Mounir

135

Short-Term Throughput Maximization for Battery Limited Energy Harvesting Nodes  

E-Print Network [OSTI]

for energy recharge. Under the assumption of an increasing concave power-rate relationship, the short completion time of a given amount of data were found for an energy harvesting node under the assumptionShort-Term Throughput Maximization for Battery Limited Energy Harvesting Nodes Kaya Tutuncuoglu

Yener, Aylin

136

Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests, Final Report  

E-Print Network [OSTI]

This is the final report for ASHRAE Research Project 1004-RP: Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests. This report presents the results of the development and application of the methodology to Case Study #2...

Reddy, T. A.; Elleson, J.; Haberl, J. S.

2000-01-01T23:59:59.000Z

137

Short term effects of moderate carbon prices on land use in the New Zealand emissions trading  

E-Print Network [OSTI]

Short term effects of moderate carbon prices on land use in the New Zealand emissions trading Zealand Emissions Trading Scheme (NZ ETS) was introduced through the Climate Change Response Act............................................................................ 14 #12;1 1 Introduction The New Zealand Emissions Trading Scheme (NZ ETS) was legislated through

Silver, Whendee

138

Long-and Short-Term Climate Influences on Southwestern Shrublands  

E-Print Network [OSTI]

-New findings raise questions about long and short-term climatic effects on Southwestern shrublands. Millennial encroachment acceler- ated during the 1950's drought, when both winters and summers went dry, and continues, NM. Gen. Tech. Rep. INT-GTR-338. Ogden, UT: u.s. Department of Agriculture, Forest Service

139

PRIMARY RESEARCH PAPER Short-term responses of decomposers to flow restoration  

E-Print Network [OSTI]

most stream restoration projects, lack pre-restoration data and clearly defined goals, making et al., 2005; Bernhardt et al., 2005). Biotic recovery in response to stream restoration can be rapidPRIMARY RESEARCH PAPER Short-term responses of decomposers to flow restoration in Fossil Creek

LeRoy, Carri J.

140

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

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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

142

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

143

Short-term measurements for the determination of envelope retrofit performance  

SciTech Connect (OSTI)

Short-term monitoring for estimating thermal parameters of a building, along with an analytical technique to (1) determine the long-term performance and (2) calculate the parameters from a building description, has many valuable applications, which include energy ratings, diagnostics, and retrofit analysis. In this paper we address issues relating to reducing uncertainties in estimating thermal parameters with emphasis on retrofit applications. In general, it is necessary to impose a known heat flow with a suitable profile to reliably estimate the parameters. This is demonstrated with test cell measurements taken before and after changes were made to the test cell. The eventual goal of this project is to develop a practical methodology to determine long-term retrofit performance from short-term tests.

Subbarao, K.; Mort, D.; Burch, J.

1985-06-01T23:59:59.000Z

144

Performance and nutrient utilization of steers fed short term reconstituted grains  

E-Print Network [OSTI]

experiment. The ration's were composed on a dry basis of 86X grain and 14X of the same protein supplement used in the feeding experiment. Eight Beefmaster crossbred steers of the same origin and weight as those used in the growth trial were assigned...PERFORMANCE AND NUTRIENT UTILIZATION OF STEERS FED SHORT TERM RECONSTITUTED GRAINS A Thesis by EDWARD JAMES SIMPSON, JR. Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree...

Simpson, Edward James

1982-01-01T23:59:59.000Z

145

Short term generation scheduling in photovoltaic-utility grid with battery storage  

SciTech Connect (OSTI)

This paper presents an efficient approach to short term resource scheduling for an integrated thermal and photovoltaic-battery generation. The proposed model incorporated battery storage for peak load shaving. Several constraints including battery capacity, minimum up/down time and ramp rates for thermal units, as well as natural photovoltaic (PV) capacity are considered in the proposed model. A case study composed of 26 thermal units and a PV-battery plant is presented to test the efficiency of the method.

Marwali, M.K.C.; Ma, H.; Shahidehpour, S.M. [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Electrical and Computer Engineering] [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Electrical and Computer Engineering; Abdul-Rahman, K.H. [Siemens Energy and Automation, Brooklyn Park, MN (United States)] [Siemens Energy and Automation, Brooklyn Park, MN (United States)

1998-08-01T23:59:59.000Z

146

Short-Term Energy Tests of a Credit Union Building in Idaho (Draft)  

SciTech Connect (OSTI)

This report describes tests and results of the energy performance of a credit union building in Idaho. The building is in the Energy Edge Program administered by the Bonneville Power Administration (BPA). BPA provided incentives to incorporate innovative features designed to conserve energy use by the building. It is of interest to determine the actual performance of these features. The objective of this project was to evaluate the applicability of the SERI short-term energy monitoring (STEM) method to nonresidential buildings.

Subbarao, K.; Balcomb, J. D.

1993-01-01T23:59:59.000Z

147

DSM savings verification through short-term pre-and-post energy monitoring at 90 facilities  

SciTech Connect (OSTI)

This paper summarizes the DSM impact results obtained from short-term energy measurements performed at sites monitored as part of the Commercial, Industrial and Agricultural (CIA) Retrofit Incentives Evaluation Program sponsored by the Pacific Gas & Electric Company. The DSM measures include those typically found in these sectors; i.e., lighting, motors, irrigation pumps and HVAC modifications. The most important findings from the site measurements are the estimated annual energy and demand savings. Although there may be large differences of projected energy savings for individual sites, when viewed in the aggregate the total energy savings for the program were found to be fairly comparable to engineering estimates. This paper describes the lessons learned from attempting in-situ impact evaluations of DSM savings under both direct and custom rebate approaches. Impact parameters of interest include savings under both direct and custom rebate approaches. Impact parameters of interest include gross first-year savings and load shape impacts. The major method discussed in this paper is short-term before/after field monitoring of affected end-uses; however, the complete impact evaluation method also includes a billing analysis component and a hybrid statistical/engineering model component which relies, in part, on the short-term end-use data.

Misuriello, H.

1994-12-31T23:59:59.000Z

148

Cloud tracking with optical flow for short-term solar forecasting Philip Wood-Bradley, Jos Zapata, John Pye  

E-Print Network [OSTI]

, photovoltaic systems, and grid regulation (Mathiesen & Kleissl, 2011; Martínez López, et al, 2002). A method apart with a size of 640 by 480 pixels, were processed to determine the time taken for clouds to reach irradiance is essential for the effective operation of many solar applications such as solar thermal systems

149

Short-term Wind Power Prediction for Offshore Wind Farms -Evaluation of Fuzzy-Neural Network Based Models  

E-Print Network [OSTI]

Short-term Wind Power Prediction for Offshore Wind Farms - Evaluation of Fuzzy-Neural Network Based of wind power capacities are likely to take place offshore. As for onshore wind parks, short-term wind of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms

Paris-Sud XI, Université de

150

ORIGINAL PAPER Short-term effect of tillage intensity on N2O and CO2 emissions  

E-Print Network [OSTI]

ORIGINAL PAPER Short-term effect of tillage intensity on N2O and CO2 emissions Pascal Boeckx negative to positive. We studied the short-term effect of tillage intensity on N2O and CO2 emissions. We site, an intermediately aerated Luvisol in Belgium, were similar. Nitrous oxide and CO2 emissions were

Paris-Sud XI, Université de

151

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

152

DOBEIA-0202(83/4Q) Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy Outlook Quarterly

153

DOE/EIA-0202(84/1Q) Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P PRELIMINARY1Q)

154

DOE/EIA-0202(84/2QH Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P PRELIMINARY1Q)2QH

155

DOE/EIA-0202(84/3Q) Short-Term Energy Outlook  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P PRELIMINARY1Q)2QH3Q)

156

DOE/EIA-0202(84/4Q) Short-Term Energy Outlook Quarterly Projections  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P

157

DOE/EIA-0202(85/4Q) Short-Term Energy Outlook OBIS Quarterly  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q)

158

DOE/EIA-0202(87/1Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q)6/1Q)7/1Q) Energy

159

DOE/EIA-0202(87/2Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q)6/1Q)7/1Q)

160

DOE/EIA-0202(87/3Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q)6/1Q)7/1Q)3Q)

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

DOE/EIA-0202(87/4Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term Energy3/P1Q)6/1Q)7/1Q)3Q)4Q)

162

DOE/EIA-0202(88/1Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term

163

DOE/EIA-0202(88/2Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term2Q) Energy Information

164

DOE/EIA-0202(88/3Q) Energy Information Administration Short-Term  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term2Q) Energy Information3Q)

165

DOE/EIA-0202|83/2Q)-1 Short-Term Energy Outlook  

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,,781Title:DOBEIA-0202(83/4Q) Short-Term2Q) Energy1Q) 1992 1

166

Short-Term Energy Outlook - U.S. Energy Information Administration (EIA)  

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 2,328Administration (EIA)propanenaturalSpecialShort-Term

167

Short-Term Energy Outlook - U.S. Energy Information Administration (EIA)  

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 2,328AdministrationRelease Schedule Release Date. The Short-Term

168

Using futures prices to filter short-term volatility and recover a latent, long-term price series for oil  

E-Print Network [OSTI]

Oil prices are very volatile. But much of this volatility seems to reflect short-term,transitory factors that may have little or no influence on the price in the long run. Many major investment decisions should be guided ...

Herce, Miguel Angel

2006-01-01T23:59:59.000Z

169

The short-term effects of two chaining treatments on populations of Tabanus abactor Philip (Diptera: Tabanidae)  

E-Print Network [OSTI]

THE SHORT-TERM EFFECTS OF TWO CHAINING TREATMENTS ON POPULATIONS OF Tabanus abactor Philip (DIPTERA: TABANIDAE) A Thesis by STEVEN PAUL HOLMES 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 1998 Major Subject: Entomology THE SHORT-TERM EFFECTS OF TWO CHAINING TREATMENTS ON POPULATIONS OF Tabaaas abacrar Philip HIIPTERAt TABANIDAE) A Thesis STEVEN PAUL HOLMES Submitted to Texas Adt...

Holmes, Steven Paul

1998-01-01T23:59:59.000Z

170

Short term effects of commercial polychlorinated biphenyl (PCB) mixtures and individual PCB congeners in female Sprague-Dawley rats  

E-Print Network [OSTI]

SHORT TERM EFFECTS OF COMMERCIAL POLYCHLORINATED BIPHENYL (PCB) MIXTURFS AND INDIVIDUAL PCB CONGENERS IN FEMALE SPRAGUE-DAWLEY RATS A Thesis by YU-CHYU CHEN Submitted to the Office of Graduate Studies of Texas A&M University in partial... fulfillment of the requirements for the degree of MASTER OF SCIENCE December 1992 Major subject: Toxicology SHORT TERM EFFECTS OF COMMERCIAL POLYCHLORINATED BIPHENYL (PCB) MIXTURES AND INDIVIDUAL PCB CONGENERS IN FEMALE SPRAGUE-DAWLEY RATS A Thesis...

Chen, Yu-Chyu

1992-01-01T23:59:59.000Z

171

EVALUATING SHORT-TERM CLIMATE VARIABILITY IN THE LATE HOLOCENE OF THE NORTHERN GREAT PLAINS  

SciTech Connect (OSTI)

This literature study investigated methods and areas to deduce climate change and climate patterns, looking for short-term cycle phenomena and the means to interpret them. Many groups are actively engaged in intensive climate-related research. Ongoing research might be (overly) simplified into three categories: (1) historic data on weather that can be used for trend analysis and modeling; (2) detailed geological, biological (subfossil), and analytical (geochemical, radiocarbon, etc.) studies covering the last 10,000 years (about since last glaciation); and (3) geological, paleontological, and analytical (geochemical, radiometric, etc.) studies over millions of years. Of importance is our ultimate ability to join these various lines of inquiry into an effective means of interpretation. At this point, the process of integration is fraught with methodological troubles and misconceptions about what each group can contribute. This project has met its goals to the extent that it provided an opportunity to study resource materials and consider options for future effort toward the goal of understanding the natural climate variation that has shaped our current civilization. A further outcome of this project is a proposed methodology based on ''climate sections'' that provides spatial and temporal correlation within a region. The method would integrate cultural and climate data to establish the climate history of a region with increasing accuracy with progressive study and scientific advancement (e. g., better integration of regional and global models). The goal of this project is to better understand natural climatic variations in the recent past (last 5000 years). The information generated by this work is intended to provide better context within which to examine global climate change. The ongoing project will help to establish a basis upon which to interpret late Holocene short-term climate variability as evidenced in various studies in the northern Great Plains, northern hemisphere, and elsewhere. Finally these data can be integrated into a history of climate change and predictive climate models. This is not a small undertaking. The goals of researchers and the methods used vary considerably. The primary task of this project was literature research to (1) evaluate existing methodologies used in geologic climate change studies and evidence for short-term cycles produced by these methodologies and (2) evaluate late Holocene climate patterns and their interpretations.

Joseph H. Hartman

1999-09-01T23:59:59.000Z

172

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

173

Short-term Variations in the Galactic Environment of the Sun  

E-Print Network [OSTI]

The galactic environment of the Sun varies over short timescales as the Sun and interstellar clouds travel through space. Small variations in the dynamics, ionization, density, and magnetic field strength of the interstellar medium (ISM) surrounding the Sun yield pronounced changes in the heliosphere. We discuss essential information required to understand short-term variations in the galactic environment of the Sun, including the distribution and radiative transfer properties of nearby ISM, and variations in the boundary conditions of the heliosphere as the Sun traverses clouds. The most predictable transitions are when the Sun emerged from the Local Bubble interior and entered the cluster of local interstellar clouds flowing past the Sun, within the past 140,000 years, and again when the Sun entered the local interstellar cloud now surrounding and inside of the solar system, sometime during the past 44,000 years.

Priscilla C. Frisch; Jonathan D. Slavin

2006-01-17T23:59:59.000Z

174

A field study evaluation of short-term refined Gaussian dispersion models  

SciTech Connect (OSTI)

A tracer study was conducted at the Duke Forest Site in Chapel Hill, North Carolina in January, 1995 to evaluate the ability of three short-term refined Gaussian dispersion models to predict the fate of volume source emissions under field study conditions. Study participants included the American Petroleum Institute (API), the US Environmental Protection Agency (EPA), the US Department of Energy (DOE), the University of North Carolina at Chapel Hill (UNC), and private consulting firms. The models evaluated were Industrial Source Complex--Short Term versions 2 and 3 (ISC2, ISC3) and the American Meteorological Society (AMS) Environmental Protection Agency (EPA) Regulatory Model Improvement Committee (AERMIC) model, AERMOD. All three models are based on the steady-state Gaussian plume dispersion equation, which predicts concentrations at downwind receptor locations when integrated over the distance between the source and receptor. Chemicals were released at known rates and measurements were taken at various points in the study field using Tedlar bag point sampling and open-path Fourier Transform infrared (OP-FTIR) monitoring. The study found that ISC and AERMOD underpredicted the measured concentrations for each dataset collected in the field study. ISC and AERMOD each underpredicted the OPFTIR dataset by a factor of approximately 1.6. ISC underpredicted the Tedlar{reg_sign} dataset by approximately 2.1, while AERMOD underpredicted by a factor of approximately 2.6. Regardless of source configuration or measurement technique used, under-prediction with respect to the measured concentration was consistently observed. This indicates that safety factors or other corrections may be necessary in predicting contaminant concentrations over the distances examined in this study, i.e., in the near field of less than 200 meters.

Piper, A.

1996-12-31T23:59:59.000Z

175

Short-Term Energy Monitoring (STEM): Application of the PSTAR method to a residence in Fredericksburg, Virginia  

SciTech Connect (OSTI)

This report describes a project to assess the thermal quality of a residential building based on short-term tests during which a small number of data channels are measured. The project is called Short- Term Energy Monitoring (STEM). Analysis of the data provides extrapolation to long-term performance. The test protocol and analysis are based on a unified method for building simulations and short-term testing called Primary and Secondary Terms Analysis and Renormalization (PSTAR). In the PSTAR method, renormalized parameters are introduced for the primary terms such that the renormalized energy balance is best satisfied in the least squares sense; hence, the name PSTAR. The mathematical formulation of PSTAR is detailed in earlier reports. This report describes the short-term tests and data analysis performed using the PSTAR method on a residential building in Fredricksburg, Virginia. The results demonstrate the ability of the PSTAR method to provide a realistically complex thermal model of a building, and determine from short-term tests the statics as well as the dynamics of a building, including solar dynamics. 10 refs., 12 figs., 2 tabs.

Subbarao, K.; Burch, J.D.; Hancock, C.E.; Lekov, A.; Balcomb, J.D.

1988-09-01T23:59:59.000Z

176

Paper presented at EWEC 2008, Brussels, Belgium (31 March-03 April) Uncertainty Estimation of Wind Power Forecasts  

E-Print Network [OSTI]

-Antipolis, France Abstract--Short-term wind power forecasting tools providing "single-valued" (spot) predictions associated to the future wind power produc- tion for performing more efficiently functions such as reserves and modelling architec- tures for probabilistic wind power forecasting. Then, a comparison is carried out

Paris-Sud XI, Université de

177

European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind Generation by a Dynamic  

E-Print Network [OSTI]

European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind. Abstract-Short-term wind power forecasting is recognized nowadays as a major requirement for a secure and economic integration of wind power in a power system. In the case of large-scale integration, end users

Paris-Sud XI, Université de

178

Daily/Hourly Hydrosystem Operation : How the Columbia River System Responds to Short-Term Needs.  

SciTech Connect (OSTI)

The System Operation Review, being conducted by the Bonneville Power Administration, the US Army Corps of Engineers, and the US Bureau of Reclamation, is analyzing current and potential future operations of the Columbia River System. One goal of the System Operations Review is to develop a new System Operation Strategy. The strategy will be designed to balance the many regionally and nationally important uses of the Columbia River system. Short-term operations address the dynamics that affect the Northwest hydro system and its multiple uses. Demands for electrical power and natural streamflows change constantly and thus are not precisely predictable. Other uses of the hydro system have constantly changing needs, too, many of which can interfere with other uses. Project operators must address various river needs, physical limitations, weather, and streamflow conditions while maintaining the stability of the electric system and keeping your lights on. It takes staffing around the clock to manage the hour-to-hour changes that occur and the challenges that face project operators all the time.

Columbia River System Operation Review (U.S.)

1994-02-01T23:59:59.000Z

179

Comparison of observed and predicted short-term tracer gas concentrations in the atmosphere  

SciTech Connect (OSTI)

The Savannah River Laboratory is in the process of conducting a series of atmospheric tracer studies. The inert gas sulfurhexafluoride is released from a height of 62 m for 15 min and concentrations in air are measured on sampling arcs up to 30 km downwind of the release point. Maximum 15 min. air concentrations from 14 of these tracer tests have been compared with the ground-level, centerline air concentration predicted with a Gaussian plume atmospheric transport model using eight different sets of atmospheric dispersion parameters. Preliminary analysis of the results from these comparisons indicates that the dispersion parameters developed at Juelich, West Germany, based on tracers released from a height of 50 m, give the best overall agreement between the predicted and observed values. The median value of the ratio of predicted to observed air concentrations for this set of parameters is 1.3, and the correlation coefficient between the log of the predictions and the log of the observations is 0.72. For the commonly used Pasquill-Gifford dispersion parameters, the values of these same statistics are 4.4 and 0.68, respectively. The Gaussian plume model is widely used to predict air concentrations resulting from short-term radionuclide release to the atmosphere. The results of comparisons such as these must be considered whenever the Gaussian model is used for such purposes. 22 references, 3 tables.

Cotter, S.J.; Miller, C.W.; Lin, W.C.T.

1985-01-01T23:59:59.000Z

180

Short-term and creep shear characteristics of a needlepunched thermally locked geosynthetic clay liner  

SciTech Connect (OSTI)

A series of constant-rate direct shear tests were conducted on a needlepunched thermally locked geosynthetic clay liner (GCL) in accordance with ASTM Test Method for Determining the Coefficient of Soil and Geosynthetic or Geosynthetic and Geosynthetic Friction by the Direct Shear Method (D 5321). The test results demonstrate that the needlepunched thermally locked reinforcing fibers provide substantial short-term shear strength to a GCL. However, there is a growing concern that the long-term shear strength to a GCL. However, there is a growing concern that the long-term shear strength of this type of GCL can be affected due to the potential of creep within the reinforcing fibers under sustained constant loads which occur in the field. An attempt was made to address this concern through an incrementally-loaded creep shear test conducted in a newly developed constant-load (creep) shear testing device. The results of the creep shear test to date show that the GCL has undergone relatively small shear displacements with incremental shear rates decreasing with time within each loading phase.

Siebken, J.R. [National Seal Co., Galesburg, IL (United States). Technical Services; Swan, R.H. Jr.; Yuan, Z. [GeoSyntec Consultants, Atlanta, GA (United States). Soil-Geosynthetic Interaction Testing Lab.

1997-11-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Comparative effects of sodium channel blockers in short term rat whole embryo culture  

SciTech Connect (OSTI)

This study was undertaken to examine the effect on the rat embryonic heart of two experimental drugs (AZA and AZB) which are known to block the sodium channel Nav1.5, the hERG potassium channel and the L-type calcium channel. The sodium channel blockers bupivacaine, lidocaine, and the L-type calcium channel blocker nifedipine were used as reference substances. The experimental model was the gestational day (GD) 13 rat embryo cultured in vitro. In this model the embryonic heart activity can be directly observed, recorded and analyzed using computer assisted image analysis as it responds to the addition of test drugs. The effect on the heart was studied for a range of concentrations and for a duration up to 3 h. The results showed that AZA and AZB caused a concentration-dependent bradycardia of the embryonic heart and at high concentrations heart block. These effects were reversible on washout. In terms of potency to cause bradycardia the compounds were ranked AZB > bupivacaine > AZA > lidocaine > nifedipine. Comparison with results from previous studies with more specific ion channel blockers suggests that the primary effect of AZA and AZB was sodium channel blockage. The study shows that the short-term rat whole embryo culture (WEC) is a suitable system to detect substances hazardous to the embryonic heart. - Highlights: Study of the effect of sodium channel blocking drugs on embryonic heart function We used a modified method rat whole embryo culture with image analysis. The drugs tested caused a concentration dependent bradycardia and heart block. The effect of drugs acting on multiple ion channels is difficult to predict. This method may be used to detect cardiotoxicity in prenatal development.

Nilsson, Mats F, E-mail: Mats.Nilsson@farmbio.uu.se [Department of Pharmaceutical Biosciences, Uppsala University (Sweden); Skld, Anna-Carin; Ericson, Ann-Christin; Annas, Anita; Villar, Rodrigo Palma [AstraZeneca R and D Sdertlje (Sweden); Cebers, Gvido [AstraZeneca R and D, iMed, 141 Portland Street, Cambridge, MA 02139 (United States); Hellmold, Heike; Gustafson, Anne-Lee [AstraZeneca R and D Sdertlje (Sweden); Webster, William S [Department of Anatomy and Histology, University of Sydney (Australia)

2013-10-15T23:59:59.000Z

182

Short-term effects of oestradiol, T3 or insulin infusions on plasma concentrations and estimated hepatic balances  

E-Print Network [OSTI]

Short-term effects of oestradiol, T3 or insulin infusions on plasma concentrations and estimated infusions on interme- diary and hepatic metabolism were studied in 4 preruminant male calves fed milk replac/kg BW), infused during the first h after feeding. Metabolite concentrations were determined

Paris-Sud XI, Université de

183

1256 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 18, NO. 4, NOVEMBER 2003 Short-Term Hydrothermal Generation Scheduling  

E-Print Network [OSTI]

long and mid-term models, have been used to optimize the amount of hydro energy to be used during1256 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 18, NO. 4, NOVEMBER 2003 Short-Term Hydrothermal are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from

Catholic University of Chile (Universidad Catlica de Chile)

184

Volume 29, Issue 2 On the short-term influence of oil price changes on stock markets in gcc  

E-Print Network [OSTI]

Volume 29, Issue 2 On the short-term influence of oil price changes on stock markets Rouen & LEO Abstract This paper examines the short-run relationships between oil prices and GCC stock to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price

Paris-Sud XI, Université de

185

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 24, NO. 1, MARCH 2009 125 Short-Term Prediction of Wind Farm Power  

E-Print Network [OSTI]

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 24, NO. 1, MARCH 2009 125 Short-Term Prediction of Wind Farm Power: A Data Mining Approach Andrew Kusiak, Member, IEEE, Haiyang Zheng, and Zhe Song, Student Member, IEEE Abstract--This paper examines time series models for predicting the power of a wind

Kusiak, Andrew

186

Where can I find free economic forecasts? Economic forecasts have become an integral part of business and individual investment decisions. Economic  

E-Print Network [OSTI]

, the Conference Board provides short term (quarterly and annual) forecasts for real GDP, real consumer spending include (among others): GDP and real GDP, price indices for GDP and consumer spending, unemployment are projections of economic activity including GDP growth. These reports can be found on-line at: http

Johnson, Eric E.

187

PSTAR: Primary and secondary terms analysis and renormalization: A unified approach to building energy simulations and short-term monitoring  

SciTech Connect (OSTI)

This report presents a unified method of hourly simulation of a building and analysis of performance data. The method is called Primary and Secondary Terms Analysis and Renormalization (PSTAR). In the PSTAR method, renormalized parameters are introduced for the primary terms such that the renormalized energy balance equation is best satisfied in the least squares sense, hence, the name PSTAR. PSTAR allows extraction of building characteristics from short-term tests on a small number of data channels. These can be used for long-term performance prediction (''ratings''), diagnostics, and control of heating, ventilating, and air conditioning systems (HVAC), comparison of design versus actual performance, etc. By combining realistic building models, simple test procedures, and analysis involving linear equations, PSTAR provides a powerful tool for analyzing building energy as well as testing and monitoring. It forms the basis for the Short-Term Energy Monitoring (STEM) project at SERI.

Subbarao, K.

1988-09-01T23:59:59.000Z

188

Paper accepted for presentation at 2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy Wind Power Forecasting using Fuzzy Neural Networks  

E-Print Network [OSTI]

, Italy Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk) as input, to predict the power production of wind park8 48 hours ahead. The prediction system integrates of the numerical weather predictions. Index Term-Wind power, short-term forecasting, numerical weather predictions

Paris-Sud XI, Universit de

189

Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint  

SciTech Connect (OSTI)

Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

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

2013-05-01T23:59:59.000Z

190

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....

191

Rolling 12 Month Forecast November-2008.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-Infrared0 Resource ProgramEnergyMaterials: Sulfur K-edge XANES and Pt

192

Skill forecasting from ensemble predictions of wind power P. Pinson,a  

E-Print Network [OSTI]

Skill forecasting from ensemble predictions of wind power P. Pinson,a , H.Aa. Nielsena , H. Madsena with the commonly provided short-term wind power point predictions. Alternative approaches for the use uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion

Paris-Sud XI, Université de

193

EWEC 2006 Scientific Track Advanced Forecast Systems for the Grid Integration of 25 GW  

E-Print Network [OSTI]

forecasts, smoothing effects Abstract The economic success of offshore wind farms in liberalised electricity of offshore wind farms, their electricity production must be known well in advance to allow an efficient Oldenburg, Germany Key words: Offshore wind power, grid integration, short-term prediction, regional

Heinemann, Detlev

194

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

195

HyperionOpexModule Budget/8MonthReview  

E-Print Network [OSTI]

HyperionOpexModule Budget/8MonthReview #12;Hyperion Opex Module Budget/8 Month Review 1 ................................................................................................................................................... 6 Step 4 Enter the Forecast and Budget .............................................................................................................................. 14 Copy 8 Month Review into next year's budget

Hitchcock, Adam P.

196

Methodology for Analyzing Energy and Demand Savings From Energy Services Performance Contract Using Short-Term Data  

E-Print Network [OSTI]

..iilJlf t '_:pUIltaD ? (e) (d) ? ? I I , , ., ? BJ ? AmmJl.thm:pIIILt1II:l ....iind?t.m'.m1R.Dl (,) (f) r ~ ~, ~I-----------'l,----------f .. AmmJl.thJII.:p1mt1ll:1 ., February 9, 2009 Energy Systems Laboratory 10 CONCLUSIONSCASE STUDIESMETHODOLOGY DEMAND SAVINGS...METHODOLOGY FOR ANALYZING ENERGY AND DEMAND SAVINGS FROM ENERGY SERVICES PERFORMANCE CONTRACT USING SHORT-TERM DATA Zi Liu, Jeff Haberl, Soolyeon Cho Energy Systems Laboratory Texas A&M University System College Station, TX 77843 Bobby...

Liu, Z.; Haberl, J. S.; Cho, S.; Lynn, B.; Cook, M.

2006-01-01T23:59:59.000Z

197

Modular High-Temperature Gas-Cooled Reactor short term thermal response to flow and reactivity transients  

SciTech Connect (OSTI)

The analyses reported here have been conducted at the Oak Ridge National Laboratory (ORNL) for the US Nuclear Regulatory Commission's (NRC's) Division of Regulatory Applications of the Office of Nuclear Regulatory Research. The short-term thermal response of the Modular High-Temperature Gas-Cooled Reactor (MHTGR) is analyzed for a range of flow and reactivity transients. These include loss of forced circulation (LOFC) without scram, moisture ingress, spurious withdrawal of a control rod group, hypothetical large and rapid positive reactivity insertion, and a rapid core cooling event. The coupled heat transfer-neutron kinetics model is also described.

Cleveland, J.C.

1988-01-01T23:59:59.000Z

198

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

199

Prediction of short-term and long-term VOC emissions from SBR bitumen-backed carpet under different temperatures  

SciTech Connect (OSTI)

This paper presents two models for volatile organic compound (VOC) emissions from carpet. One is a numerical model using the computational fluid dynamics (CFD) technique for short-term predictions, the other an analytical model for long-term predictions. The numerical model can (1) deal with carpets that are not new, (2) calculate the time-dependent VOC distributions in a test chamber or room, and (3) consider the temperature effect on VOC emissions. Based on small-scale chamber data, both models were used to examine the VOC emissions under different temperatures from polypropene styrene-butadiene rubber (SBR) bitumen-backed carpet. The short-term predictions show that the VOC emissions under different temperatures can be modeled solely by changing the carpet diffusion coefficients. A formulation of the Arrhenius relation was used to correlate the dependence of carpet diffusion coefficient with temperature. The long-term predictions show that it would take several years to bake out the VOCs, and temperature would have a major impact on the bake-out time.

Yang, S.; Chen, Q. [Massachusetts Inst. of Tech., Cambridge, MA (United States). Building Technology Program; Bluyssen, P.M. [TNO Building and Construction Research, Delft (Netherlands)

1998-12-31T23:59:59.000Z

200

Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests; Literature Review and Site Selection, Nov. 1997 (Revised Feb. 1998)  

E-Print Network [OSTI]

This is the preliminary report contains the literature review and site selection recommendations for ASHRAE Research Project RP 1004 "Determining Long-term Performance of Cool Storage Systems From Short-term Tests"....

Haberl, J. S.; Claridge, D. E.; Reddy, T. A.; Elleson, J.

1997-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

International Statistical Review (2012), 80, 1, 223 doi:10.1111/j.1751-5823.2011.00168.x Short-Term Wind Speed Forecasting  

E-Print Network [OSTI]

@stat.tamu.edu Summary The emphasis on renewable energy and concerns about the environment have led to large-scale wind mix and develop diverse sources of clean, renewable energy. Cost-effective energy that can be produced to 1990), to increase the amount of renewable energy to 20% of the energy supply, and to reduce

Genton, Marc G.

2012-01-01T23:59:59.000Z

202

> 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

203

Microstructural evolution of delta ferrite in SAVE12 steel under heat treatment and short-term creep  

SciTech Connect (OSTI)

This research focused on the formation and microstructural evolution of delta ferrite phase in SAVE12 steel. The formation of delta ferrite was due to the high content of ferrite forming alloy elements such as Cr, W, and Ta. This was interpreted through either JMatPro-4.1 computer program or Cr{sub eq} calculations. Delta ferrite was found in bamboo-like shape and contained large amount of MX phase. It was surrounded by Laves phases before creep or aging treatment. Annealing treatments were performed under temperatures from 1050 Degree-Sign C to 1100 Degree-Sign C and various time periods to study its dissolution kinetics. The result showed that most of the delta ferrite can be dissolved by annealing in single phase austenitic region. Dissolution process of delta ferrite may largely depend on dissolution kinetic factors, rather than on thermodynamic factors. Precipitation behavior during short-term (1100 h) creep was investigated at temperature of 600 Degree-Sign C under a stress of 180 MPa. The results demonstrated that delta ferrite became preferential nucleation sites for Laves phase at the early stage of creep. Laves phase on the boundary around delta ferrite showed relatively slower growth and coarsening rate than that inside delta ferrite. - Highlights: Black-Right-Pointing-Pointer Delta ferrite is systematically studied under heat treatment and short-term creep. Black-Right-Pointing-Pointer Delta ferrite contains large number of MX phase and is surrounded by Laves phases before creep or aging treatment. Black-Right-Pointing-Pointer Formation of delta ferrite is interpreted by theoretical and empirical methods. Black-Right-Pointing-Pointer Most of the delta ferrite is dissolved by annealing in single phase austenitic region. Black-Right-Pointing-Pointer Delta ferrite becomes preferential nucleation sites for Laves phase at the early stage of creep.

Li, Shengzhi, E-mail: lishengzhi@sjtu.edu.cn [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)] [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China); Eliniyaz, Zumrat; Zhang, Lanting; Sun, Feng [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)] [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China); Shen, Yinzhong [School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)] [School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China); Shan, Aidang, E-mail: adshan@sjtu.edu.cn [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)] [School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China)

2012-11-15T23:59:59.000Z

204

Effects of various uranium leaching procedures on soil: Short-term vegetation growth and physiology. Progress report, April 1994  

SciTech Connect (OSTI)

Significant volumes of soil containing elevated levels of uranium exist in the eastern United States. The contamination resulted from the development of the nuclear industry in the United States requiring a large variety of uranium products. The contaminated soil poses a collection and disposal problem of a magnitude that justifies the development of decontamination methods. Consequently, the Department of Energy (DOE) Office of Technology Development formed the Uranium Soils Integrated Demonstration (USID) program to address the problem. The fundamental goal of the USID task group has been the selective extraction/leaching or removal of uranium from soil faster, cheaper, and safer than what can be done using current conventional technologies. The objective is to selectively remove uranium from soil without seriously degrading the soil`s physicochemical characteristics and without generating waste that is difficult to manage and/or dispose of. However, procedures developed for removing uranium from contaminated soil have involved harsh chemical treatments that affect the physicochemical properties of the soil. The questions are (1) are the changes in soil properties severe enough to destroy the soil`s capacity to support and sustain vegetation growth and survival? and (2) what amendments might be made to the leached soil to return it to a reasonable vegetation production capacity? This study examines the vegetation-support capacity of soil that had been chemically leached to remove uranium. The approach is to conduct short-term germination and phytotoxicity tests for evaluating soils after they are subjected to various leaching procedures followed by longer term pot studies on successfully leached soils that show the greatest capacity to support plant growth. This report details the results from germination and short-term phytotoxicity testing of soils that underwent a variety of leaching procedures at the bench scale at ORNL and at the pilot plant at Fernald.

Edwards, N.T.

1994-08-01T23:59:59.000Z

205

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

206

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

207

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

208

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

209

> 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

210

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

211

Apolipoprotein E Genotype-Dependent Paradoxical Short-Term Effects of {sup 56}Fe Irradiation on the Brain  

SciTech Connect (OSTI)

Purpose: In humans, apolipoprotein E (apoE) is encoded by three major alleles ({epsilon}2, {epsilon}3, and {epsilon}4) and, compared to apoE3, apoE4 increases the risk of developing Alzheimer disease and cognitive impairments following various environmental challenges. Exposure to irradiation, including that of {sup 56}Fe, during space missions poses a significant risk to the central nervous system, and apoE isoform might modulate this risk. Methods and Materials: We investigated whether apoE isoform modulates hippocampus-dependent cognitive performance starting 2 weeks after {sup 56}Fe irradiation. Changes in reactive oxygen species (ROS) can affect cognition and are induced by irradiation. Therefore, after cognitive testing, we assessed hippocampal ROS levels in ex vivo brain slices, using the ROS-sensitive fluorescent probe, dihydroethidium (DHE). Brain levels of 3-nitrotyrosine (3-NT), CuZn superoxide dismutase (CuZnSOD), extracellular SOD, and apoE were assessed using Western blotting analysis. Results: In the water maze, spatial memory retention was impaired by irradiation in apoE2 and apoE4 mice but enhanced by irradiation in apoE3 mice. Irradiation reduced DHE-oxidation levels in the enclosed blade of the dentate gyrus and levels of 3-NT and CuZnSOD in apoE2 but not apoE3 or apoE4 mice. Finally, irradiation increased apoE levels in apoE3 but not apoE2 or apoE4 mice. Conclusions: The short-term effects of {sup 56}Fe irradiation on hippocampal ROS levels and hippocampus-dependent spatial memory retention are apoE isoform-dependent.

Haley, Gwendolen E. [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States) [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States); Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR (United States); Villasana, Laura; Dayger, Catherine; Davis, Matthew J. [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States)] [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States); Raber, Jacob, E-mail: raberj@ohsu.edu [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States) [Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR (United States); Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR (United States); Department of Neurology, Oregon Health and Science University, Portland, OR (United States)

2012-11-01T23:59:59.000Z

212

Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests, Progress Report, 6-99, Revised 12-99  

E-Print Network [OSTI]

This is the Spring 1999 progress report on ASHRAE Research Project RP 1004: Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests. This report presents an update concerning the work that has been accomplished since the June...

Reddy, T. A.; Elleson, J.; Haberl, J. S.

1999-01-01T23:59:59.000Z

213

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 An Analytical Framework for Short-Term Resource  

E-Print Network [OSTI]

markets, strategic behavior, capacity gaming. I. INTRODUCTION HE electric system is said to be reliable markets, is capacity. Since sellers need not offer all their capacity to serve the demand, they may engage An Analytical Framework for Short-Term Resource Adequacy in Competitive Electricity Markets Pablo A. Ruiz

214

Estimation of original gas in place from short-term shut-in pressure data for commingled tight gas reservoirs with no crossflow  

E-Print Network [OSTI]

gas production (GP) under these circumstances. This research studies different empirical methods to estimate the original gas in place (OGIP) for one-layer or commingled two-layer tight gas reservoirs without crossflow, from short-term (72-hour) shut...

Khuong, Chan Hung

2012-06-07T23:59:59.000Z

215

European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. State-of-the-Art on Methods and Software Tools for Short-Term  

E-Print Network [OSTI]

European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. State-of-the-Art on Methods and Software Tools for Short-Term Prediction of Wind Energy Production G. Giebel*, L. Landberg, Risoe National Roskilde, Denmark Abstract: The installed wind energy capacity in Europe today is 20 GW, while

Paris-Sud XI, Université de

216

A Methodology to Characterize Ideal Short-term Counting Conditions and Improve AADT Estimation Accuracy Using a Regression-based Correcting  

E-Print Network [OSTI]

-established and robust with clear guidelines to collect short-term count data, to analyze data and develop annual average a statewide system of non-motorized data. From a planning point of view, a key measure of traffic volumes continuous counts comes from the AASHTO Guidelines for Traffic Data Programs, prepared in 1992 (AASHTO, 1992

Bertini, Robert L.

217

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...

218

An assay of duck hepatitis virus induced interferon, produced in duck embryo fibro-blasts which have experienced short term treatment with DDT  

E-Print Network [OSTI]

AN ASSAY OP DUCK HEPATITIS VIRUS INDUCED INTERFERON, PRODUCED IN DUCK EMBRYO FIBROBLASTS WHICH HAVE EXPERIENCED SHORT TERM TREATMENT WITH DDT A Thesis by BURGESS BAUDER Submitted to the Graduate College of Texas A&M University in Partial... WITH DDT A Thesi. s by /'. ". " . "i BURGESS BAUDER Approved as to style and content by: (Chairm of Committee) (Head of Depar ent) (Member) (Member) (Member) August 1973 ABSTRACT An Assay of Duck Hepatitis Virus Induced Interferon, Produced...

Bauder, Richard Burgess

1973-01-01T23:59:59.000Z

219

arXiv:1007.3122v2[q-bio.NC]30Jan2013 Robust Short-Term Memory without Synaptic  

E-Print Network [OSTI]

been stored in samuel.johnson@imperial.ac.uk 1 #12;our brains previously (not very credible). Here wearXiv:1007.3122v2[q-bio.NC]30Jan2013 Robust Short-Term Memory without Synaptic Learning Samuel Johnson1,2, , J. Marro3 , and Joaqu´in J. Torres3 1 Department of Mathematics, Imperial College London, SW

Johnson, Samuel

220

PSTAR: Primary and secondary terms analysis and renormalization: A unified approach to building energy simulations and short-term monitoring: A summary  

SciTech Connect (OSTI)

This report summarizes a longer report entitled PSTAR - Primary and Secondary Terms Analysis and Renormalization. A Unified Approach to Building Energy Simulations and Short-Term Monitoring. These reports highlight short-term testing for predicting long-term performance of residential buildings. In the PSTAR method, renormalized parameters are introduced for the primary terms such that the renormalized energy balance equation is best satisfied in the least squares sense; hence, the name PSTAR. Testing and monitoring the energy performance of buildings has several important applications, among them: extrapolation to long-term performance, refinement of design tools through feedback from comparing design versus actual parameters, building-as-a-calorimeter for heating, ventilating, and air conditioning (HVAC) diagnostics, and predictive load control. By combining realistic building models, simple test procedures, and analysis involving linear equations, PSTAR provides a powerful tool for analyzing building energy as well as testing and monitoring. It forms the basis for the Short-Term Energy Monitoring (STEM) project at SERI. 3 figs., 1 tab.

Subbarao, K.

1988-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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

222

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

223

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

224

BWRSAR (Boiling Water Reactor Severe Accident Response) calculations of reactor vessel debris pours for Peach Bottom short-term station blackout  

SciTech Connect (OSTI)

This paper describes recent analyses performed by the BWR Severe Accident Technology (BWRSAT) Program at Oak Ridge National Laboratory to estimate the release of debris from the reactor vessel for the unmitigated short-term station blackout accident sequence. Calculations were performed with the BWR Severe Accident Response (BWRSAR) code and are based upon consideration of the Peach Bottom Atomic Power Station. The modeling strategies employed within BWRSAR for debris relocation within the reactor vessel are briefly discussed and the calculated events of the accident sequence, including details of the calculated debris pours, are presented. 4 refs., 13 figs., 3 tabs.

Hodge, S.A.; Ott, L.J.

1988-01-01T23:59:59.000Z

225

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

226

Winchester/Camberley Homes New Construction Test House Design, Construction, and Short-Term Testing in a Mixed-Humid Climate  

SciTech Connect (OSTI)

The NAHB Research Center partnered with production builder Winchester/Camberley Homes to build a DOE Building America New Construction Test House (NCTH). This single family, detached house, located in the mixed-humid climate zone of Silver Spring, MD, was completed in June 2011. The primary goal for this house was to improve energy efficiency by 30% over the Building America B10 benchmark by developing and implementing an optimized energy solutions package design that could be cost effectively and reliably constructed on a production basis using quality management practices. The intent of this report is to outline the features of this house, discuss the implementation of the energy efficient design, and report on short-term testing results. During the interactive design process of this project, numerous iterations of the framing, air sealing, insulation, and space conditioning systems were evaluated for energy performance, cost, and practical implementation. The final design featured numerous advanced framing techniques, high levels of insulation, and the HVAC system entirely within conditioned space. Short-term testing confirmed a very tight thermal envelope and efficient and effective heating and cooling. In addition, relevant heating, cooling, humidity, energy, and wall cavity moisture data will be collected and presented in a future long-term report.

Mallav, D.; Wiehagen, J.; Wood, A.

2012-10-01T23:59:59.000Z

227

The long-term and the short-term at a cropping municipal sewage sludge disposal facility  

SciTech Connect (OSTI)

The City of Raleigh, NC, chose land application of municipal sewage sludge as a means of reducing pollution to the Neuse River. The Neuse River Waste Water Treatment Plant (NRWWTP) is located in the Piedmont Province of North Carolina. The soils at the facility are derived largely from the Rolesville Granite. Sewage sludge is applied to over 640 acres of cropland, owned in fee or leased. In making the policy decision for use of the sludge land application method 20 or so years ago, the City had to evaluate the potential for heavy metal accumulation in the soils and plants as well as the potential for ground-water contamination from the nitrate-nitrogen. The city also had to make a policy decision about limiting the discharge of heavy metals to the sewer system. Study of data from monitoring wells demonstrate that well position is a key in determining whether or not nitrate-nitrogen contamination is detected. Data from a three-year study suggest that nitrate-nitrogen moves fairly rapidly t the water table, although significant buildup in nitrogen-nitrogen may take a number of years. Evidence exists suggesting that the time between application of sewage sludge and an increase of nitrate-nitrogen at the water table may be on the order of nine months to a year. It is apparent that in the case of municipal sewage sludge application one can anticipate some nitrate-nitrogen buildup and that the public policy on drinking water standards must recognize this fact.

Welby, C.W. (North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth and Atmospheric Sciences)

1994-03-01T23:59:59.000Z

228

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

229

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

230

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

231

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

232

EUROBRISA products documentation This page (http://eurobrisa.cptec.inpe.br/) presents 1-month lead South America precipitation  

E-Print Network [OSTI]

South America precipitation forecasts and verification products for three month seasons. For example surface temperatures as predictor variables for precipitation over South America. For example

233

EUROBRISA products documentation This page (http://eurobrisa.cptec.inpe.br/) presents 1-month lead South America precipitation  

E-Print Network [OSTI]

South America precipitation forecasts and verification products for three month seasons. For example Pacific and Atlantic sea surface temperatures as predictor variables for precipitation over South America

234

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

235

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng Associate ResearchWestern

236

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng Associate ResearchWesternAug-2014

237

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng Associate

238

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0 120.0 110.0

239

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0 120.0

240

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0 120.0Jul-2014

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0

242

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0Mar-2015 815.0

243

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0Mar-2015

244

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015 945.0Mar-2015Nov-2014

245

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015

246

Western Area Power Administration Starting Forecast Month: Sierra Nevada Region  

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 Deng AssociateFeb-2015Sep-2014 940.0 210.0

247

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1

248

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO) Highlights

249

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO) Highlights 1

250

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO) Highlights

251

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO)

252

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO)(STEO)

253

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO)(STEO)June

254

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3 1(STEO)(STEO)June

255

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San3

256

Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1 October 2014

257

Short-Term Energy Outlook  

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 2,328Administration (EIA)propanenatural

258

Short-Term Energy Outlook  

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 2,328Administration (EIA)propanenatural

259

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

260

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

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill  

SciTech Connect (OSTI)

We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

2012-08-15T23:59:59.000Z

262

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2011  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

263

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2012  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

264

Historical Monthly Energy Review  

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 AboutDecember 2005 (Thousand9,0, 1997Environment >7,992000 Short-TermSeptember 2002

265

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

266

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

267

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

268

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

269

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

270

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

271

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

272

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

273

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

274

Monthly energy review  

SciTech Connect (OSTI)

The U.S. energy market for the first quarter of 1988 is discussed. Production, energy consumption, imports, price adjustments, and forecasts for the rest of the year are given.

Not Available

1988-03-01T23:59:59.000Z

275

INFRARED OBSERVATIONS OF THE MILLISECOND PULSAR BINARY J1023+0038: EVIDENCE FOR THE SHORT-TERM NATURE OF ITS INTERACTING PHASE IN 2000-2001  

SciTech Connect (OSTI)

We report our multi-band infrared (IR) imaging of the transitional millisecond pulsar system J1023+0038, a rare pulsar binary known to have an accretion disk in 2000-2001. The observations were carried out with ground-based and space telescopes from near-IR to far-IR wavelengths. We detected the source in near-IR JH bands and Spitzer 3.6 and 4.5 {mu}m mid-IR channels. Combined with the previously reported optical spectrum of the source, the IR emission is found to arise from the companion star, with no excess emission detected in the wavelength range. Because our near-IR fluxes are nearly equal to those obtained by the 2MASS all-sky survey in 2000 February, the result indicates that the binary did not contain the accretion disk at the time, whose existence would have raised the near-IR fluxes to twice larger values. Our observations have thus established the short-term nature of the interacting phase seen in 2000-2001: the accretion disk existed for at most 2.5 yr. The binary was not detected by the WISE all-sky survey carried out in 2010 at its 12 and 22 {mu}m bands and our Herschel far-IR imaging at 70 and 160 {mu}m. Depending on the assumed properties of the dust, the resulting flux upper limits provide a constraint of <3 Multiplication-Sign 10{sup 22}-3 Multiplication-Sign 10{sup 25} g on the mass of the dust grains that possibly exist as the remnants of the previously seen accretion disk.

Wang, Xuebing; Wang, Zhongxiang [Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030 (China)] [Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030 (China); Morrell, Nidia [Las Campanas Observatory, Observatories of the Carnegie Institution of Washington, La Serena (Chile)] [Las Campanas Observatory, Observatories of the Carnegie Institution of Washington, La Serena (Chile)

2013-02-20T23:59:59.000Z

276

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

277

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

278

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

279

ORSSAB Monthly Board Meeting  

Broader source: Energy.gov [DOE]

The ORSSAB MonthlyBoard meeting is open to the public. This month, participants will be briefed on the East Tennessee Technology Park Zone 1 Soils Proposed Plan.

280

ORSSAB monthly board meeting  

Broader source: Energy.gov [DOE]

The ORSSAB monthly board meeting is open to the public. This month, participants will receive an updateon the U-233 Project.

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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

282

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

283

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

284

NOAA ARL Monthly Activity Report Bruce B. Hicks, Director  

E-Print Network [OSTI]

· NOAA ARL Monthly Activity Report June 2004 Bruce B. Hicks, Director Air Resources Laboratory Bay 3. HIGHLIGHT ­ Air Quality Forecast System - Start of 2004 Operational Season 4. Reactions Atmosphere 7. SURFRAD/ISIS 8. Errors in Radiation Instrumentation 9. ARL Umkehr Developments Adopted 10

285

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

286

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

287

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

288

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

289

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

290

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

291

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

292

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

293

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

294

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

295

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

296

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.

297

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

298

Electricity Monthly Update  

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

by almost 10%, or just over 12 million tons, to 136 million tons. This is the largest month-to-month percentage increase since at least January 2009. In absolute terms,...

299

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

300

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

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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

302

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

303

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

304

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

305

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

306

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

307

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

308

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

309

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

310

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

311

Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting  

E-Print Network [OSTI]

Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.

Msiza, Ishmael S; Nelwamondo, Fulufhelo Vincent

2007-01-01T23:59:59.000Z

312

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

313

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

314

Natural gas monthly  

SciTech Connect (OSTI)

The Natural Gas Monthly highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the Natural Gas Monthly features articles designed to assist readers in using and interpreting natural gas information.

NONE

1998-01-01T23:59:59.000Z

315

ORSSAB monthly meeting  

Broader source: Energy.gov [DOE]

This month's ORSSAB board meeting will focus on the ETTP Zone 1 soils proposed plan. The meeting is open to the public.

316

Monthly Performance Report  

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

Budget describes the budget needs for facility and infrastructure construction, maintenance and disposition. It also identifies construction EXECUTIVE OVERVIEW MSC Monthly...

317

National Energy Awareness Month  

Broader source: Energy.gov [DOE]

October is National Energy Awareness Month. It's also a chance to talk about our countrys energy security and its clean energy future.

318

Electricity Monthly Update  

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

Wholesale Markets: October 2014 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale...

319

Electricity Monthly Update  

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

See all Electricity Reports Electricity Monthly Update With Data for September 2014 | Release Date: Nov. 25, 2014 | Next Release Date: Dec. 23, 2014 Previous Issues Issue:...

320

Electricity Monthly Update  

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

See all Electricity Reports Electricity Monthly Update With Data for October 2014 | Release Date: Dec. 23, 2014 | Next Release Date: Jan. 26, 2015 Previous Issues Issue:...

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Electricity Monthly Update  

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

Wholesale Markets: September 2014 The United States has many regional wholesale electricity markets. Below we look at monthly and annual ranges of on-peak, daily wholesale...

322

National Cybersecurity Awareness Month  

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

National Cybersecurity Awareness Month (NCSAM) October 2013 Every October, the Department of Energy joins the Department of Homeland Security (DHS) and others across the country...

323

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

324

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

325

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

326

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

327

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

328

National Osteoporosis Prevention Month  

E-Print Network [OSTI]

MAY National Osteoporosis Prevention Month JUNE National Dairy Month Texas AgriLife Extension - Bone Health Power Point # P4-1 Eat Smart for Bone Health # P4-2 Osteoporosis Disease Statistics # P4-3 Osteoporosis = Porous Bones # P4-4 Risk Factors # P4-5 Risk Factors (continued) # P4-6 Steps to Prevention # P4

329

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

330

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...

331

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

332

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

333

Disability Employment Awareness Month  

Broader source: Energy.gov [DOE]

Utilizing the talents of all Americans is essential for our Nation to out-innovate, out-educate, and out-build the rest of the world. During National Disability Employment Awareness Month, we...

334

ORSSAB monthly board meeting  

Broader source: Energy.gov [DOE]

The ORSSAB monthly board meeting is open to the public. The board will receive an update on the Community Reuse Organization of East Tennessee efforts at the East Tennessee Technology Park.

335

Monthly Performance Report  

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

OVERVIEW MSC Monthly Performance Report JUL 2013 DOERL-2009-113 Rev 46 27 10.0 RELIABILITY PROJECT STATUS Activity in July was centered on continuing progress on projects...

336

Monthly Performance Report  

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

OVERVIEW MSC Monthly Performance Report APR 2013 DOERL-2009-113 Rev 43 28 10.0 RELIABILITY PROJECT STATUS Activity in April was centered on continuing progress on projects...

337

Monthly Performance Report  

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

Average FY 0.77 CY 0.67 EXECUTIVE OVERVIEW EXECUTIVE OVERVIEW MSC Monthly Performance Report JAN 2013 DOERL-2009-113 Rev 40 7 Table 3-2. Days Away From Work. Definition...

338

Monthly Performance Report  

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

Budget environmental stewardship scorecards. EXECUTIVE OVERVIEW MSC Monthly Performance Report DEC 2012 DOERL-2009-113 Rev 39 5 2.0 ANALYSIS OF FUNDS Table 2-1. Mission Support...

339

Geographic Area Month  

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

Fuels by PAD District and State (Cents per Gallon Excluding Taxes) - Continued Geographic Area Month No. 1 Distillate No. 2 Distillate a No. 4 Fuel b Sales to End Users Sales for...

340

National Women's History Month  

Broader source: Energy.gov [DOE]

During Women's History Month, we recall that the pioneering legacy of our grandmothers and great-grandmothers is revealed not only in our museums and history books, but also in the fierce...

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Black History Month  

Broader source: Energy.gov [DOE]

During National African American History Month, we pay tribute to the contributions of past generations and reaffirm our commitment to keeping the American dream alive for the next generation. In...

342

ORSSAB monthly board meeting  

Broader source: Energy.gov [DOE]

The ORSSAB monthly board meeting is open to the public. The board will hear a presentation and discuss the development of a comprehensive mercury strategy for the Oak Ridge Reservation.

343

BLACK HISTORY MONTH  

Broader source: Energy.gov [DOE]

Black History Month is an annual celebration of achievements by black Americans and a time for recognizing the central role of African Americans in U.S. history. The event grew out of Negro History Week, created by historian Carter G. Woodson and other prominent African Americans. Other countries around the world, including Canada and the United Kingdom, also devote a month to celebrating black history.

344

Incorporating daily flood control objectives into a monthly stochastic dynamic programming model for a hydroelectric complex  

SciTech Connect (OSTI)

A monthly stochastic dynamic programing model was recently developed and implemented at British Columbia (B.C.) Hydro to provide decision support for short-term energy exports and, if necessary, for flood control on the Peace River in northern British Columbia. The model established the marginal cost of supplying energy from the B.C. Hydro system, as well as a monthly operating policy for the G.M. Shrum and Peace Canyon hydroelectric plants and the Williston Lake storage reservoir. A simulation model capable of following the operating policy then determines the probability of refilling Williston Lake and possible spill rates and volumes. Reservoir inflows are input to both models in daily and monthly formats. The results indicate that flood control can be accommodated without sacrificing significant export revenue.

Druce, D.J. (British Columbia Hydro and Power Authority, Vancouver, British Columbia (Canada))

1990-01-01T23:59:59.000Z

345

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

346

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.

347

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

348

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.

349

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

350

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

351

(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

352

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

353

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

354

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

355

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

356

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

357

Monthly Energy Review  

SciTech Connect (OSTI)

This publication presents an overview of the Energy information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. Two brief ``energy plugs`` (reviews of EIA publications) are included, as well.

NONE

1996-05-28T23:59:59.000Z

358

November 2010 monthly report  

SciTech Connect (OSTI)

These viewgraphs are to be provided to NNSA to update the status of the B61 Life Extension Project work and activities. The viewgraphs cover such issues as budget, schedule, scope, and the like. They are part of the monthly reporting process.

Neff, Warren E [Los Alamos National Laboratory

2010-12-07T23:59:59.000Z

359

Energy Action Month  

Broader source: Energy.gov [DOE]

The Federal Energy Management Program (FEMP) supports Energy Action Month by offering materials that promote energy- and water-saving practices in Federal facilities. This year's outreach materials call on Federal employees to take action and empower leadership, innovation, and excellence to realize a secure energy future.

360

September 2015 Monthly Planner  

E-Print Network [OSTI]

Tuesday Wednesday Thursday Friday Saturday 1 All HR/Payroll Responsibilities On Monthly Pay Day 2 All HR/Payroll Responsibilities On 3 All HR/Payroll Responsibilities On 4 All HR/Payroll Responsibilities On 5 All HR/Payroll Responsibilities On 6 All HR/Payroll Responsibilities On BW PPE Sch Disabled @5:00pm - HRMS Specialist - Fac

Acton, Scott

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Monthly energy review  

SciTech Connect (OSTI)

This document presents an overview of the Energy Information Administration`s (EIA) recent monthly energy statistics. The statistics cover the major activities of U.S. production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors.

NONE

1997-12-01T23:59:59.000Z

362

Monthly energy review  

SciTech Connect (OSTI)

The Monthly Energy Review presents current data on production, consumption, stocks, imports, exports, and prices of the principal energy commodities in the United States. Also included are data on international production of crude oil, consumption of petroleum products, petroleum stocks, and production of electricity from nuclear-powered facilities.

Not Available

1989-08-01T23:59:59.000Z

363

Monthly energy review  

SciTech Connect (OSTI)

The Monthly Energy Review is prepared by the Energy Information Administration. Statistical data and information are provided on the topics of energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy. (VC)

Not Available

1992-04-01T23:59:59.000Z

364

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

365

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

366

Short term accommodation and Bed and Breakfasts  

E-Print Network [OSTI]

hotels in central London. *Premier Inn County Hall Belvedere Road London SE1 7PB Website: http.indianymca.org Journey's ­ Kings Cross 54 ­ 58 Caledonian Road Kings Cross London N1 9DP Tel: 020 7833 3893 http.dovercastlehostel.com Ashlee House 261 ­ 265 Grays Inn Road Kings Cross London WC1X 8QT Tel: 020 7833 9400 Fax: 020 7833 9677

Kühn, Reimer

367

Short-Term Farm Credit in Texas.  

E-Print Network [OSTI]

.. 27.1 ............................................... For automob~les.. 7.8 For other purposes. ............................................. 4.5 Production: 1 60.6 A noticeable feature of this table is the relatively high percentage of loans...

Lee, Virgil P.

1927-01-01T23:59:59.000Z

368

Comfort control for short-term occupancy  

E-Print Network [OSTI]

to a thermostat-controlled fan-coil unit i n each room. TheThe t y p i c a l fan-coil and w a l l units are i n this

Fountain, M.; Brager, G. S.; Arens, Edward A; Bauman, Fred; Benton, C.

1994-01-01T23:59:59.000Z

369

Umea University Education Short-Term Program  

E-Print Network [OSTI]

refrigerators, freezers and kitchen utensils that you can share. The housing with IHO includes: · Private room

Viglas, Anastasios

370

August 2012 Short-Term Energy Outlook  

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,,781Title: Telephone:shortOil and Natural8U.S.NA (Barrels per

371

APPLICATION FORM Short term course on  

E-Print Network [OSTI]

Address: Prof. Tapan K. Sengupta High Performance Computing Lab Department of Aerospace Venue : IIT Kanpur, Kanpur Organized by: High Performance Computing Lab, Dept. of Aerospace Engineering

Srivastava, Kumar Vaibhav

372

Short-Term Energy Outlook January 2014  

U.S. Energy Information Administration (EIA) 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 CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data9c : U.S. Regional Weather Data Either

373

Short-Term Energy Outlook May 2014  

U.S. Energy Information Administration (EIA) 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 CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data9c : U.S. Regional Weather Data EitherMay 2014 1

374

September 2012 Short-Term Energy Outlook  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San Juan Montana Thrust

375

Short Term Energy Outlook ,November 2002  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San JuanGasData

376

Short Term Energy Outlook ,October 2002  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San JuanGasDataOctober

377

Short Term Energy Outlook, December 2002  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San

378

Short-Term Energy Outlook April 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer Fuels

379

Short-Term Energy Outlook February 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer Fuels4 1

380

Short-Term Energy Outlook January 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer Fuels4

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Short-Term Energy Outlook July 2013  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer Fuels41

382

Short-Term Energy Outlook June 2013  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer Fuels411

383

Short-Term Energy Outlook March 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer

384

Short-Term Energy Outlook May 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and Summer(STEO)

385

Short-Term Energy Outlook September 2013  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1and

386

Short-Term Energy Outlook September 2014  

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) " ,"ClickPipelines About U.S. Natural GasquestionnairesquestionnairesGasA.San34 1andOutlook

387

Short-Term Energy Outlook April 2014  

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

-0.5 0.0 0.5 1.0 1.5 2013 2014 2015 OPEC countries North America Russia and Caspian Sea Latin America North Sea Other Non-OPEC World Crude Oil and Liquid Fuels Production Growth...

388

Petroleum marketing monthly  

SciTech Connect (OSTI)

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

NONE

1996-02-01T23:59:59.000Z

389

Electric power monthly  

SciTech Connect (OSTI)

The Energy Information Administration (EIA) prepares the Electric Power Monthly (EPM) for a wide audience including Congress, Federal and State agencies, the electric utility industry, and the general public. This publication provides monthly statistics for net generation, fossil fuel consumption and stocks, quantity and quality of fossil fuels, cost of fossil fuels, electricity sales, revenue, and average revenue per kilowatthour of electricity sold. Data on net generation, fuel consumption, fuel stocks, quantity and cost of fossil fuels are also displayed for the North American Electric Reliability Council (NERC) regions. The EIA publishes statistics in the EPM on net generation by energy source, consumption, stocks, quantity, quality, and cost of fossil fuels; and capability of new generating units by company and plant. The purpose of this publication is to provide energy decisionmakers with accurate and timely information that may be used in forming various perspectives on electric issues that lie ahead.

NONE

1995-08-01T23:59:59.000Z

390

Electric power monthly  

SciTech Connect (OSTI)

The Electric Power Monthly is prepared by the Survey Management Division; Office of Coal, Nuclear, Electric and Alternate Fuels, Energy Information Administration (EIA), Department of Energy. This publication provides monthly statistics at the national, Census division, and State levels for net generation, fuel consumption, fuel stocks, quantity and quality of fuel, cost of fuel, electricity sales, revenue, and average revenue per kilowatthour of electricity sold. Data on net generation, fuel consumption, fuel stocks, quantity and cost of fuel are also displayed for the North American Electric Reliability Council (NERC) regions. Additionally, statistics by company and plant are published in the EPM on capability of new plants, new generation, fuel consumption, fuel stocks, quantity and quality of fuel, and cost of fuel.

Not Available

1992-05-01T23:59:59.000Z

391

Petroleum marketing monthly  

SciTech Connect (OSTI)

Petroleum Marketing Monthly (PPM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o. b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

NONE

1996-07-01T23:59:59.000Z

392

Petroleum marketing monthly  

SciTech Connect (OSTI)

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data in the Petroleum Marketing Monthly.

NONE

1995-08-01T23:59:59.000Z

393

Monthly energy review  

SciTech Connect (OSTI)

This issue of the Monthly Energy Review contains preliminary energy summary data for 1982. A 4.3% decline in total energy consumption marked the third year in a row that domestic energy consumption fell. Decreases in the consumption of petroleum, natural gas, and coal contributed to the decline but were offset somewhat by increased use of hydroelectric and nuclear power. Because demand for energy was down, a lower level of imports was sufficient to meet US energy needs.

Not Available

1983-02-01T23:59:59.000Z

394

The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.  

SciTech Connect (OSTI)

ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational setting. It shall be demonstrated that when used properly, the real-time offsite measurements materially improve wind ramp capture and prediction statistics, when compared to traditional wind forecasting techniques and to a simple persistence model.

Martin Wilde, Principal Investigator

2012-12-31T23:59:59.000Z

395

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

396

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

397

Petroleum marketing monthly  

SciTech Connect (OSTI)

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. The Petroleum Marketing Division, Office of Oil and Gas, Energy Information Administration ensures the accuracy, quality, and confidentiality of the published data.

NONE

1995-11-01T23:59:59.000Z

398

Petroleum marketing monthly  

SciTech Connect (OSTI)

The Petroleum Marketing Monthly (PMM) is designed to give information and statistical data about a variety of crude oils and refined petroleum products. The publication provides statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Sales data for motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane are presented.

Not Available

1992-03-01T23:59:59.000Z

399

Monthly Performance Report  

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 Conchas recovery challenge fundProject8Mistakes toMolecularMonitoring‹3 Monthly

400

Monthly Performance Report  

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 Conchas recovery challenge fundProject8Mistakes toMolecularMonitoring‹3 Monthly5

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Monthly Performance Report  

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 Conchas recovery challenge fundProject8Mistakes toMolecularMonitoring‹3 Monthly54

402

Monthly Performance Report  

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 Conchas recovery challenge fundProject8Mistakes toMolecularMonitoring‹3 Monthly542

403

Monthly Performance Report  

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 Conchas recovery challenge fundProject8Mistakes toMolecularMonitoring‹3 Monthly5421

404

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA) 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 CenterFranconia,(Million Barrels) Crude Oil Reserves in Nonproducing ReservoirsYear-MonthCoalbedPricethe1.PDF7.PDFTABLE8.PDF Table1.1

405

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA) 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 CenterFranconia,(Million Barrels) Crude Oil Reserves in Nonproducing ReservoirsYear-MonthCoalbedPricethe1.PDF7.PDFTABLE8.PDF Table1.18

406

Petroleum Supply Monthly  

U.S. Energy Information Administration (EIA) 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 CenterFranconia,(Million Barrels) Crude Oil Reserves in Nonproducing ReservoirsYear-MonthCoalbedPricethe1.PDF7.PDFTABLE8.PDF Table1.183

407

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g y PSeptember0 Monthly

408

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly

409

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly4

410

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly43

411

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly431

412

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly4310

413

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly43102

414

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary8 Monthly431025

415

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly

416

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly9

417

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly91

418

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly913

419

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly9132

420

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary85 Monthly91328

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary854 Monthly

422

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary854 Monthly6

423

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary854 Monthly60

424

Monthly Performance Report  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g yFebruary854 Monthly6049

425

Monthly Reports 2014  

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: VegetationEquipment SurfacesResource ProgramModification andinterface1 E n e r g EnvironmentalMonthly

426

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

427

ASAP progress and expenditure report for the month of February 1--29, 1996  

SciTech Connect (OSTI)

This is the ASAP progress and expenditure report for the month of February, 1996. The individual projects` report includes the sponsoring organization, the project identification, the principal investigator, long term objectives, short term objectives, accomplishments this reporting period, identification of issues or concerns, project budget estimate for the fiscal year, and monthly actual and year to date expenditures. The research project concerns a joint US/UK program to develop a high-priority radar system based on real aperture and synthetic aperature radar. Topics being researched include airborne RAR/SAR; radar data processor; ground-based SAR signal processing workstation; static airborne radar; radar field experiments; data analysis and detection theory; program management; modeling and analysis; UCSB wave tank; stratified wave tank; and experiments in a thermo-stratified tank at the Institute of Applied Physics, Russia.

Twogood, R.E.; Brase, J.M.; Chambers, D.H.; Mantrom, D.M.; Miller, M.G.; Newman, M.J.; Robey, H.F.; Vigars, M.

1996-03-20T23:59:59.000Z

428

An adaptive neural network approach to one-week ahead load forecasting  

SciTech Connect (OSTI)

A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline.'' An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.

Peng, T.M. (Pacific Gas and Electric Co., San Francisco, CA (United States)); Hubele, N.F.; Karady, G.G. (Arizona State Univ., Tempe, AZ (United States))

1993-08-01T23:59:59.000Z

429

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

430

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

431

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...

432

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...

433

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...

434

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

435

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.

436

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

437

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

438

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

439

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

440

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.

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

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

442

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

443

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

444

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

445

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

446

Petroleum Supply Monthly  

SciTech Connect (OSTI)

The Petroleum Supply Monthly (PSM) is one of a family of four publications produced by the Petroleum Supply Division within the Energy Information Administration (EIA) reflecting different levels of data timeliness and completeness. The other publications are the Weekly Petroleum Status Report (WPSR), the Winter Fuels Report, and the Petroleum Supply Annual (PSA). Data presented in the PSM describe the supply and disposition of petroleum products in the United States and major U.S. geographic regions. The data series describe production, imports and exports, inter-Petroleum Administration for Defense (PAD) District movements, and inventories by the primary suppliers of petroleum products in the United States (50 States and the District of Columbia). The reporting universe includes those petroleum sectors in primary supply. Included are: petroleum refiners, motor gasoline blenders, operators of natural gas processing plants and fractionators, inter-PAD transporters, importers, and major inventory holders of petroleum products and crude oil. When aggregated, the data reported by these sectors approximately represent the consumption of petroleum products in the United States. Data presented in the PSM are divided into two sections: Summary Statistics and Detailed Statistics.

NONE

1996-02-01T23:59:59.000Z

447

Petroleum supply monthly  

SciTech Connect (OSTI)

The Petroleum Supply Monthly (PSM) is one of a family of four publications produced by the Petroleum Supply Division within the Energy Information Administration (EIA) reflecting different levels of data timeliness and completeness. The other publications are the Weekly Petroleum Status Report (WPSR), the Winter Fuels Report, and the Petroleum Supply Annual (PSA). Data presented in the PSM describe the supply and disposition of petroleum products in the United States and major US geographic regions. The data series describe production, imports and exports, inter-Petroleum Administration for Defense (PAD) District movements, and inventories by the primary suppliers of petroleum products in the United States (50 States and the District of Columbia). The reporting universe includes those petroleum sectors in primary supply. Included are: petroleum refiners, motor gasoline blends, operators of natural gas processing plants and fractionators, inter-PAD transporters, importers, and major inventory holders of petroleum products and crude oil. When aggregated, the data reported by these sectors approximately represent the consumption of petroleum products in the United States.

NONE

1995-10-01T23:59:59.000Z

448

Monthly energy review, August 1986  

SciTech Connect (OSTI)

Statistics are cumulated monthly and annually for production, consumption, and imports for petroleum, natural gas, coal and electric power.

Not Available

1986-11-24T23:59:59.000Z

449

Monthly energy review, August 1997  

SciTech Connect (OSTI)

The Monthly Energy Review for the month of August 1997, presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of U.S. production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors.

NONE

1997-08-01T23:59:59.000Z

450

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

451

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

452

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

453

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

454

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

455

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

456

-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

457

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

458

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

459

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

460

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

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

Short-Term Exchange Financial Statement Non-degree, Short-Term, Special Students  

E-Print Network [OSTI]

for tuition with the estimated cost of your program. Do the same for each category A-G even if you to discuss Visa alternatives. STUDENT INFORMATION LAST/FAMILY NAME, capitalized First/Given Name Middle Name information regarding health insurance visit the ISSS website at http://www.vanderbilt.edu/isss/resources

462

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

463

Natural Gas Monthly, October 1993  

SciTech Connect (OSTI)

The (NGM) Natural Gas Monthly highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. This month`s feature articles are: US Production of Natural Gas from Tight Reservoirs: and Expanding Rule of Underground Storage.

Not Available

1993-11-10T23:59:59.000Z

464

Monthly energy review, January 1998  

SciTech Connect (OSTI)

This report presents an overview of recent monthly energy statistics. Major activities covered include production, consumption, trade, stocks, and prices for fossil fuels, electricity, and nuclear energy.

NONE

1998-01-01T23:59:59.000Z

465

Monthly energy review, December 1992  

SciTech Connect (OSTI)

The Monthly Energy Review contains summary data on energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy.

Not Available

1992-12-22T23:59:59.000Z

466

Monthly energy review, January 1993  

SciTech Connect (OSTI)

The Monthly Energy Review contains summary data on energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy.

Not Available

1993-01-26T23:59:59.000Z

467

Natural gas monthly, July 1997  

SciTech Connect (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. The feature article this month is entitled ``Intricate puzzle of oil and gas reserves growth.`` A special report is included on revisions to monthly natural gas data. 6 figs., 24 tabs.

NONE

1997-07-01T23:59:59.000Z

468

Monthly energy review. May 1998  

SciTech Connect (OSTI)

This report presents recent energy monthly statistics on the production, consumption, trade, stocks, and prices of petroleum, natural gas, coal, electricity, and nuclear energy.

NONE

1998-05-01T23:59:59.000Z

469

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

470

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

471

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

472

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

473

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

474

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

475

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

476

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

477

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.

478

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

479

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

480

SUMMARY OF 2007 ATLANTIC TROPICAL CYCLONE ACTIVITY AND VERIFICATION OF AUTHOR'S SEASONAL AND MONTHLY FORECASTS  

E-Print Network [OSTI]

10 12.25 8 5.75 Accumulated Cyclone Energy (ACE) (96.2) 130 170 170 150 148 100 68 Net Tropical't press us too hard on future events!!" 3 #12;DEFINITIONS Accumulated Cyclone Energy ­ (ACE) A measureSUMMARY OF 2007 ATLANTIC TROPICAL CYCLONE ACTIVITY AND VERIFICATION OF AUTHOR'S SEASONAL

Gray, William

Note: This page contains sample records for the topic "monthly short-term forecasts" 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

ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS  

SciTech Connect (OSTI)

Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-time level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.

Chiswell, S

2009-01-11T23:59:59.000Z

482

Monthly Energy Review, February 1996  

SciTech Connect (OSTI)

This monthly publication presents an overview of EIA`s recent monthly energy statistics, covering the major activities of U.S. production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. Two brief descriptions (`energy plugs`) on two EIA publications are presented at the start.

NONE

1996-02-26T23:59:59.000Z

483

Monthly energy review, October 1998  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 61 tabs.

NONE

1998-10-01T23:59:59.000Z

484

Monthly energy review, May 1999  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 61 tabs.

NONE

1999-05-01T23:59:59.000Z

485

Monthly energy review, July 1998  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs. 73 tabs.

NONE

1998-07-01T23:59:59.000Z

486

Monthly energy review, January 1999  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 61 tabs.

NONE

1999-01-01T23:59:59.000Z

487

Monthly energy review, November 1996  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 75 tabs.

NONE

1996-11-01T23:59:59.000Z

488

Monthly energy review, March 1999  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 74 tabs.

NONE

1999-03-01T23:59:59.000Z

489

Monthly energy review, November 1997  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 91 tabs.

NONE

1997-11-01T23:59:59.000Z

490

Monthly energy review, June 1998  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 36 figs., 61 tabs.

NONE

1998-06-01T23:59:59.000Z

491

Monthly energy review, November 1998  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 61 tabs.

NONE

1998-11-01T23:59:59.000Z

492

Monthly energy review: April 1996  

SciTech Connect (OSTI)

This monthly report presents an overview of energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. A section is also included on international energy. The feature paper which is included each month is entitled ``Energy equipment choices: Fuel costs and other determinants.`` 37 figs., 59 tabs.

NONE

1996-04-01T23:59:59.000Z

493

Natural gas monthly, January 1999  

SciTech Connect (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. 6 figs., 28 tabs.

NONE

1999-02-01T23:59:59.000Z

494

Natural gas monthly, November 1998  

SciTech Connect (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. 6 figs., 27 tabs.

NONE

1998-11-01T23:59:59.000Z

495

Monthly energy review, February 1999  

SciTech Connect (OSTI)

The Monthly Energy Review (MER) presents an overview of the Energy Information Administration`s recent monthly energy statistics. The statistics cover the major activities of US production, consumption, trade, stocks, and prices for petroleum, natural gas, coal, electricity, and nuclear energy. Also included are international energy and thermal and metric conversion factors. 37 figs., 73 tabs.

NONE

1999-02-01T23:59:59.000Z

496

Natural gas monthly, February 1999  

SciTech Connect (OSTI)

The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. 6 figs., 28 tabs.

NONE

1999-02-01T23:59:59.000Z

497

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.

498

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

499

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

500

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