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1

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

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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

12

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

13

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

14

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

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

15

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

16

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.

17

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

18

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

E-Print Network [OSTI]

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

Povinelli, Richard J.

19

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

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

20

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

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

22

Wind Forecasting Improvement Project | Department of Energy  

Office of Environmental Management (EM)

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

23

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...

24

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

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

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

25

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

26

Cylinder supplied ammonia scrubber testing in IDMS  

SciTech Connect (OSTI)

This report summarizes the results of the off-line testing the Integrated DWPF Melter System (IDMS) ammonia scrubbers using ammonia supplied from cylinders. Three additional tests with ammonia are planned to verify the data collected during off-line testing. Operation of the ammonia scrubber during IDMS SRAT and SME processing will be completed during the next IDMS run. The Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) scrubbers were successful in removing ammonia from the vapor stream to achieve ammonia vapor concentrations far below the 10 ppM vapor exit design basis. In most of the tests, the ammonia concentration in the vapor exit was lower than the detection limit of the analyzers so results are generally reported as <0.05 parts per million (ppM). During SRAT scrubber testing, the ammonia concentration was no higher than 2 ppM and during SME testing the ammonia concentration was no higher than 0.05 m.

Lambert, D.P.

1994-08-31T23:59:59.000Z

27

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

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

28

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

SciTech Connect (OSTI)

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

Lantz, E.; Hand, M.

2010-05-01T23:59:59.000Z

29

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

30

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

31

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS  

E-Print Network [OSTI]

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg.girodo@uni-oldenburg.de ABSTRACT Solar energy is expected to contribute major shares of the future global energy supply. Due to its and solar energy conversion processes has to account for this behaviour in respective operating strategies

Heinemann, Detlev

32

Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,  

E-Print Network [OSTI]

Leveraging Weather Forecasts in Renewable Energy Systems Navin Sharmaa, , Jeremy Gummesonb , David, Binghamton, NY 13902 Abstract Systems that harvest environmental energy must carefully regulate their us- age to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since

Shenoy, Prashant

33

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems  

E-Print Network [OSTI]

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin Sharma,gummeson,irwin,shenoy}@cs.umass.edu Abstract--To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands

Shenoy, Prashant

34

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.

35

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

E-Print Network [OSTI]

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

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

36

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

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

Gratton, Claudio

37

Exploiting weather forecasts for sizing photovoltaic energy bids  

E-Print Network [OSTI]

1 Exploiting weather forecasts for sizing photovoltaic energy bids Antonio Giannitrapani, Simone for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial set from an Italian PV plant. Index Terms--Energy market, bidding strategy, photovoltaic power

Giannitrapani, Antonello

38

Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids  

E-Print Network [OSTI]

1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan, prasanna}@usc.edu I. INTRODUCTION Smart Power Grids exemplify an emerging class of Cyber Physical-on paradigm to support operational needs. Smart Grids are an outcome of instrumentation, such as Phasor

Prasanna, Viktor K.

39

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect (OSTI)

Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

2011-03-28T23:59:59.000Z

40

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect (OSTI)

In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

2009-03-01T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

Three Essays on Energy Economics and Forecasting  

E-Print Network [OSTI]

This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil...

Shin, Yoon Sung

2012-02-14T23:59:59.000Z

42

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

43

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

E-Print Network [OSTI]

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

Genton, Marc G.

44

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

E-Print Network [OSTI]

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

Lang, K.

1982-01-01T23:59:59.000Z

45

Solar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting Questionnaire As someone who is familiar with solar energy issues, we hope that you will tak  

E-Print Network [OSTI]

is familiar with solar energy issues, we hope that you will take a few moments to answer this short survey on your needs for information on solar energy resources and forecasting. This survey is conducted with the California Solar Energy Collaborative (CSEC) and the California Solar Initiative (CSI) our objective

Islam, M. Saif

46

Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines  

E-Print Network [OSTI]

1 Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines H. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models values of the latest pre- dispatch price and demand information, made available by the Ontario

Cañizares, Claudio A.

47

Integrated DWPF Melter System (IDMS) campaign report: Hanford Waste Vitrification Plan (HWVP) process demonstration  

SciTech Connect (OSTI)

Vitrification facilities are being developed worldwide to convert high-level nuclear waste to a durable glass form for permanent disposal. Facilities in the United States include the Department of Energy`s Defense Waste Processing Facility (DWPF) at the Savannah River Site, the Hanford Waste Vitrification Plant (HWVP) at the Hanford Site and the West Valley Demonstration Project (WVDP) at West Valley, NY. At each of these sites, highly radioactive defense waste will be vitrified to a stable borosilicate glass. The DWPF and WVDP are near physical completion while the HWVP is in the design phase. The Integrated DWPF Melter System (IDMS) is a vitrification test facility at the Savannah River Technology Center (SRTC). It was designed and constructed to provide an engineering-scale representation of the DWPF melter and its associated feed preparation and off-gas treatment systems. Because of the similarities of the DWPF and HWVP processes, the IDMS facility has also been used to characterize the processing behavior of a reference NCAW simulant. The demonstration was undertaken specifically to determine material balances, to characterize the evolution of offgas products (especially hydrogen), to determine the effects of noble metals, and to obtain general HWVP design data. The campaign was conducted from November, 1991 to February, 1992.

Hutson, N.D.

1992-08-10T23:59:59.000Z

48

Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability  

Broader source: Energy.gov [DOE]

Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

49

NCAR WRF-based data assimilation and forecasting systems for wind energy applications power  

E-Print Network [OSTI]

NCAR WRF-based data assimilation and forecasting systems for wind energy applications power Yuewei of these modeling technologies w.r.t. wind energy applications. Then I'll discuss wind farm

Kim, Guebuem

50

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

51

The Incremental Benefits of the Nearest Neighbor Forecast of U.S. Energy Commodity Prices  

E-Print Network [OSTI]

This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil...

Kudoyan, Olga

2012-02-14T23:59:59.000Z

52

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

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

53

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

54

U.S. Department of Energy Workshop Report: Solar Resources and Forecasting  

SciTech Connect (OSTI)

This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

Stoffel, T.

2012-06-01T23:59:59.000Z

55

MPC for Wind Power Gradients --Utilizing Forecasts, Rotor Inertia, and Central Energy Storage  

E-Print Network [OSTI]

MPC for Wind Power Gradients -- Utilizing Forecasts, Rotor Inertia, and Central Energy Storage iterations. We demonstrate our method in simulations with various wind scenarios and prices for energy. INTRODUCTION Today, wind power is the most important renewable energy source. For the years to come, many

56

European Wind Energy Conference -Brussels, Belgium, April 2008 Data mining for wind power forecasting  

E-Print Network [OSTI]

European Wind Energy Conference - Brussels, Belgium, April 2008 Data mining for wind power-term forecasting of wind energy produc- tion up to 2-3 days ahead is recognized as a major contribution the improvement of predic- tion systems performance is recognised as one of the priorities in wind energy research

Paris-Sud XI, Université de

57

Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Department of Computer Science  

E-Print Network [OSTI]

and institutional campuses can significantly contribute to energy conservation. The rollout of smart grids of occupants, and is a micro-grid test-bed for the DoE sponsored Los Angeles Smart Grid Demonstration ProjectImproving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Saima Aman

Prasanna, Viktor K.

58

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

59

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

60

Energy: a historical perspective and 21st century forecast  

SciTech Connect (OSTI)

Contents are: Preface; Chapter 1: introduction, brief history, and chosen approach; Chapter 2: human population and energy consumption: the future; Chapter 4: sources of energy (including a section on coal); Chapter 5: electricity: generation and consumption; and Chapter 6: energy consumption and probable energy sources during the 21st century.

Salvador, Amos [University of Texas, Austin, TX (United States)

2005-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

Implementation of a Corporate Energy Accounting and Forecasting Model  

E-Print Network [OSTI]

The development and implementation of a Frito-Lay computer based energy consumption reporting and modeling program is discussed. The system has been designed to relate actual plant energy consumption to a standard consumption which incorporates...

Kympton, H. W.; Bowman, B. M.

1981-01-01T23:59:59.000Z

62

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

SciTech Connect (OSTI)

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

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

1983-07-01T23:59:59.000Z

63

Ammonia scrubber testing during IDMS SRAT and SME processing. Revision 1  

SciTech Connect (OSTI)

This report summarizes results of the Integrated DWPF (Defense Waste Processing Facility) Melter System (IDMS) ammonia scrubber testing during the PX-7 run (the 7th IDMS run with a Purex type sludge). Operation of the ammonia scrubber during IDMS Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) processing has been completed. The ammonia scrubber was successful in removing ammonia from the vapor stream to achieve NH3 concentrations far below the 10 ppM vapor exist design basis during SRAT processing. However, during SME processing, vapor NH3 concentrations as high as 450 ppM were measured exiting the scrubber. Problems during the SRAT and SME testing were vapor bypassing the scrubber and inefficient scrubbing of the ammonia at the end of the SME cycle (50% removal efficiency; 99.9% is design basis efficiency).

Lambert, D.P.

1995-04-28T23:59:59.000Z

64

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

65

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

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 ConchasPassiveSubmittedStatusButler Tina ButlerToday in Energy Today in Energy!EIA's Today

66

Text-Alternative Version LED Lighting Forecast | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarlyEnergyDepartment of EnergyProgram (Alabama)Technology for Tank April 7, 2014 Dr.bloom.pdfSamThe

67

TRANSPORTATION ENERGY FORECASTS AND ANALYSES FOR THE 2009  

E-Print Network [OSTI]

, Doug Leach, Matt Tobin Propel Biofuels/Jeff Stephens California Department of Food and Agriculture, Weights and Measurements/Gary Castro, Allan Morrison, John Mough, Ed Williams Clean Energy Fuels

68

Project Profile: Forecasting and Influencing Technological Progress in Solar Energy  

Broader source: Energy.gov [DOE]

The University of North Carolina at Charlotte, along with their partners at Arizona State University and the University of Oxford, under theSolar Energy Evolution and Diffusion Studies (SEEDS)...

69

Energy Department Forecasts Geothermal Achievements in 2015 | Department of  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataCombined Heat & PowerEnergy Blog Energy Blog RSS

70

DOE Announces Webinars on Real Time Energy Management, Solar Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarly Career Scientists'Montana. DOCUMENTS AVAILABLEReportEnergy EfficiencyDavis-BaconOffshore WindMetrics,

71

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

72

Sandia National Laboratories: Solar Energy Forecasting and Resource  

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 -theErikGroundbreaking WorkTransformationSitingMolten Salt Test Loop OnTutorial

73

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) | Open  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 NoPublic Utilities Address:011-DNA Jump37. It is classifiedProject)EnerVaultTechnologiesDelhi (NCT),IncEnergy

74

Long-term Industrial Energy Forecasting (LIEF) model (18-sector version)  

SciTech Connect (OSTI)

The new 18-sector Long-term Industrial Energy Forecasting (LIEF) model is designed for convenient study of future industrial energy consumption, taking into account the composition of production, energy prices, and certain kinds of policy initiatives. Electricity and aggregate fossil fuels are modeled. Changes in energy intensity in each sector are driven by autonomous technological improvement (price-independent trend), the opportunity for energy-price-sensitive improvements, energy price expectations, and investment behavior. Although this decision-making framework involves more variables than the simplest econometric models, it enables direct comparison of an econometric approach with conservation supply curves from detailed engineering analysis. It also permits explicit consideration of a variety of policy approaches other than price manipulation. The model is tested in terms of historical data for nine manufacturing sectors, and parameters are determined for forecasting purposes. Relatively uniform and satisfactory parameters are obtained from this analysis. In this report, LIEF is also applied to create base-case and demand-side management scenarios to briefly illustrate modeling procedures and outputs.

Ross, M.H. (Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Physics); Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. (Argonne National Lab., IL (United States))

1993-05-01T23:59:59.000Z

75

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

76

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

77

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

78

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

79

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

80

PRIOR FLARING AS A COMPLEMENT TO FREE MAGNETIC ENERGY FOR FORECASTING SOLAR ERUPTIONS  

SciTech Connect (OSTI)

From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1300 sunspot active regions across the 30 Degree-Sign radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's full-disk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days; (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions; and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F. [ZP13 MSFC/NASA, Huntsville, AL 35812 (United States); Khazanov, Igor [CSPAR, Cramer Hall/NSSTC, The University of Alabama in Huntsville, Huntsville, AL 35899 (United States)

2012-09-20T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

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

82

Large-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random Fields  

E-Print Network [OSTI]

smart grid operations [1]. Despite their ubiquity and the complexity of many forecasting methods, most predictions of upcoming values, typically to minimize a metric such as root mean squared error. However

Kolter, J. Zico

83

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

E-Print Network [OSTI]

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

84

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

85

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

86

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations  

SciTech Connect (OSTI)

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system breaking points, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

2010-09-01T23:59:59.000Z

87

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...

88

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

89

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

90

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....

91

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...

92

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

93

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

94

Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts  

SciTech Connect (OSTI)

The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

Reno Harnish

2011-08-16T23:59:59.000Z

95

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

96

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

97

Short and Long-Term Perspectives: The Impact on Low-Income Consumers of Forecasted Energy Price Increases in 2008 and A Cap & Trade Carbon Policy in 2030  

SciTech Connect (OSTI)

The Department of Energy's Energy Information Administration (EIA) recently released its short-term forecast for residential energy prices for the winter of 2007-2008. The forecast indicates increases in costs for low-income consumers in the year ahead, particularly for those using fuel oil to heat their homes. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation's low-income households by primary heating fuel type, nationally and by Census Region. The report provides an update of bill estimates provided in a previous study, "The Impact Of Forecasted Energy Price Increases On Low-Income Consumers" (Eisenberg, 2005). The statistics are intended for use by policymakers in the Department of Energy's Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2008 fiscal year. In addition to providing expenditure forecasts for the year immediately ahead, this analysis uses a similar methodology to give policy makers some insight into one of the major policy debates that will impact low-income energy expenditures well into the middle decades of this century and beyond. There is now considerable discussion of employing a cap-and-trade mechanism to first limit and then reduce U.S. emissions of carbon into the atmosphere in order to combat the long-range threat of human-induced climate change. The Energy Information Administration has provided an analysis of projected energy prices in the years 2020 and 2030 for one such cap-and-trade carbon reduction proposal that, when integrated with the RECS 2001 database, provides estimates of how low-income households will be impacted over the long term by such a carbon reduction policy.

Eisenberg, Joel Fred [ORNL

2008-01-01T23:59:59.000Z

98

> 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

99

New Forecasting Tools Enhance Wind Energy Integration In Idaho and Oregon  

Office of Environmental Management (EM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGY TAXBalanced Scorecard Federal2Energy SecondWells |Energy Services » NewNew

100

DOE Releases Latest Report on Energy Savings Forecast of Solid-State  

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

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

Energy Department Announces $2.5 Million to Improve Wind Forecasting |  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomentheATLANTA, GA5 & 6, 2012 MEETING OFCalifornia ConcentratingDepartment of

102

Energy Savings Forecast of Solid-State Lighting in General Illumination  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomentheATLANTA, GA5 & 6,Department ofDepartment ofDecember 3,TheSaving

103

Forecasting and Capturing Emission Reductions Using Industrial Energy Management and Reporting Systems  

E-Print Network [OSTI]

The Mandatory 2010 Green House Gas (GHG) Reporting Regulations and pending climate change legislation has increased interest in Energy Management and Reporting Systems (EMRS) as a means of both reducing and reporting GHG emissions. This paper...

Robinson, J.

2010-01-01T23:59:59.000Z

104

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

105

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

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. NaturalA. Michael SchaalNovember 26, 2008Product:7.1EnergyU OImport Area:

106

Energy Savings Forecast of Solid-State Lighting in General Illumination Applications  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "ofEarly Career Scientists'Montana.Program -Department oftoThese Web sitesEERECommercial2010EnergyThis

107

Beyond "Partly Sunny": A Better Solar Forecast | Department of Energy  

Energy Savers [EERE]

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion |Energy UsageAUDITVehiclesTankless orA BRIEFApril 2015Commerce

108

> 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

109

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

110

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...

111

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

112

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

E-Print Network [OSTI]

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

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

113

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

114

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

115

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

116

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network [OSTI]

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

117

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

118

Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets  

E-Print Network [OSTI]

2 2. Annual Energy Outlook (Administrations Annual Energy Outlook forecasted price (of Energy, Annual Energy Outlook 2004 with Projections to

Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

2005-01-01T23:59:59.000Z

119

DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation Proposed Newcatalyst phases onOrganization FY Middle SchoolARM-TR-01468This4 Mar 2015

120

DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation Proposed Newcatalyst phases onOrganization FY Middle SchoolARM-TR-01468This4 Mar 20155 Mar

Note: This page contains sample records for the topic "idm forecasts energy" 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

DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation Proposed Newcatalyst phases onOrganization FY Middle SchoolARM-TR-01468This4 Mar 20155 Mar3

122

RACORO Forecasting  

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

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

123

Forecasted Opportunities  

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

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

124

CALIFORNIA ENERGY CALIFORNIA ENERGY DEMAND 2010-2020  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2010-2020 ADOPTED FORECAST for this report: Kavalec, Chris and Tom Gorin, 2009. California Energy Demand 20102020, Adopted Forecast. California Energy Commission. CEC2002009012CMF #12; i Acknowledgments The demand forecast

125

Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed  

E-Print Network [OSTI]

solener.2011.02.014, Solar Energy. Lave, M. , Kleissl, J. ,smoothing. Submitted to Solar Energy. Linke, F. , 1922.24th European Photovoltaic Solar Energy Conference, Hamburg,

2011-01-01T23:59:59.000Z

126

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

127

The addition of a US Rare Earth Element (REE) supply-demand model improves the characterization and scope of the United States Department of Energy's effort to forecast US REE Supply and Demand  

E-Print Network [OSTI]

This paper presents the development of a new US Rare Earth Element (REE) Supply-Demand Model for the explicit forecast of US REE supply and demand in the 2010 to 2025 time period. In the 2010 Department of Energy (DOE) ...

Mancco, Richard

2012-01-01T23:59:59.000Z

128

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

129

California Energy Commission DRAFT STAFF REPORT  

E-Print Network [OSTI]

California Energy Commission DRAFT STAFF REPORT UPDATED CALIFORNIA ENERGY DEMAND FORECAST presents an update to the 2009 California Energy Demand electricity forecast adopted for the 2009 - Updated California Energy Demand Forecast 2011-2022. California Energy Commission, Electricity Analysis

130

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

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

NONE

1995-05-01T23:59:59.000Z

131

The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss  

E-Print Network [OSTI]

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

Auffhammer, Maximilian

2005-01-01T23:59:59.000Z

132

Issues in midterm analysis and forecasting, 1996  

SciTech Connect (OSTI)

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

NONE

1996-08-01T23:59:59.000Z

133

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network [OSTI]

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

134

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

135

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

136

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

137

CALIFORNIA INCREMENTAL IMPACTS OF ENERGY  

E-Print Network [OSTI]

INITIATIVES RELATIVE TO THE 2009 INTEGRATED ENERGY POLICY REPORT ADOPTED DEMAND FORECAST Initiatives Relative to the 2009 Integrated Energy Policy Report Adopted Demand Forecast. CEC2002009001CTF ....................................................................................................................... 7 Energy Commission Demand Forecast

138

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

139

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

140

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

142

Issues in midterm analysis and forecasting 1998  

SciTech Connect (OSTI)

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

NONE

1998-07-01T23:59:59.000Z

143

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

144

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

145

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network [OSTI]

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

Shenoy, Prashant

146

Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging  

E-Print Network [OSTI]

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

Raftery, Adrian

147

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

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

Hoff, William A.

148

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

149

ESTIMATES OF ADDITIONAL ACHIEVABLE ENERGY SAVINGS  

E-Print Network [OSTI]

Demand 20142024 Revised Forecast SEPTEMBER 2013 CEC2002013005SD CALIFORNIA ENERGY COMMISSION Edmund are already incorporated in the Energy Commission's current forecast, the California Energy Demand 20142024 and forecast stakeholders through the Demand Analysis Working Group (DAWG). These scenarios varied

150

California Energy Commission DRAFT STAFF REPORT  

E-Print Network [OSTI]

demand forecasts, demand-side management and energy efficiency impacts, private supply impacts, forecast, peak, self-generation, conservation, demand-side, energy, efficiency, price, retail, end use and Instructions for Electricity Demand Forecasts. California Energy Commission, Electricity Supply Analysis

151

NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

152

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

153

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 3 AUGUST 16, 2012  

E-Print Network [OSTI]

there is significant uncertainty in its future intensity, the current forecast is for a slowly strengthening TC which, 3) forecast output from global models, 4) the current and projected state of the Madden with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all

Gray, William

154

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 15 SEPTEMBER 28, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

155

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 27 OCTOBER 10, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

156

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 16 AUGUST 29, 2013  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

157

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 12 OCTOBER 25, 2012  

E-Print Network [OSTI]

to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for individual event parameters such as named storms and hurricanes. We issue forecasts for ACE using three

Gray, William

158

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

159

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 13 SEPTEMBER 26, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

160

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 18 AUGUST 31, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

Note: This page contains sample records for the topic "idm forecasts energy" 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

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 11 OCTOBER 24, 2013  

E-Print Network [OSTI]

to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for individual event parameters such as named storms and hurricanes. We issue forecasts for ACE using three

Gray, William

162

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 2 AUGUST 15, 2013  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

163

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

164

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 17 AUGUST 30, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

165

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4 AUGUST 17, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

166

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 29 OCTOBER 12, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

167

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 11 SEPTEMBER 24, 2014  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

168

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 30 SEPTEMBER 12, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

169

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2011  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the third year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Birner, Thomas

170

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 28 SEPTEMBER 10, 2014  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Collett Jr., Jeffrey L.

171

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

172

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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

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

2005-07-01T23:59:59.000Z

173

Systematic Approaches to Ensure Correct Representation of Measured Multi-Irradiance Module Performance in PV System Energy Forecasting Models  

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 AdministrationcontrollerNanocrystallineForeign Object Damage 3Nationalmimic keyProcessing -Kenneth

174

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

175

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

176

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

177

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

178

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

179

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

180

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

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

182

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

183

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

184

OCTOBER-NOVEMBER FORECAST FOR 2014 CARIBBEAN BASIN HURRICANE ACTIVITY  

E-Print Network [OSTI]

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

Collett Jr., Jeffrey L.

185

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

186

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

187

California Energy Commission DRAFT STAFF REPORT  

E-Print Network [OSTI]

FOR ELECTRICITY DEMAND FORECASTS Prepared in Support of the 2013 Integrated Energy Policy. The information relates to electricity demand forecasts, demand-side management and energy efficiency impacts-2012. Keywords: Electricity demand, consumption, forecast, peak, self-generation, conservation, demand

188

Verification of hourly forecasts of wind turbine power output  

SciTech Connect (OSTI)

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

Wegley, H.L.

1984-08-01T23:59:59.000Z

189

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

190

California Energy Commission September 14, 2012  

E-Print Network [OSTI]

Forecast: Estimates of Incremental Uncommitted Energy Savings Relative to the California Energy Demand Forecast (CED 2011), the demand forecast adopted by the California Energy Commission (Energy Commission, and Kate Sullivan. 2012. California Energy Demand Forecast 20122022 (Volume 1 and Volume 2) California

191

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

192

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

193

Forecasting Turbulent Modes with Nonparametric Diffusion Models  

E-Print Network [OSTI]

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

Tyrus Berry; John Harlim

2015-01-27T23:59:59.000Z

194

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

195

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

196

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

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

Poulin, L.

2013-01-01T23:59:59.000Z

197

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

198

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

199

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

E-Print Network [OSTI]

andvalidation. SolarEnergy. 73:5,307? Perez,R. ,irradianceforecastsforsolarenergyapplicationsbasedonusingsatellitedata. SolarEnergy67:1?3,139?150.

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

200

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

Note: This page contains sample records for the topic "idm forecasts energy" 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

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.

202

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network [OSTI]

bid is computed by exploiting the forecast energy price for the day ahead market, the historical wind renewable energy resources, such as wind and photovoltaic, has grown rapidly. It is well known the problem of optimizing energy bids for an independent Wind Power Producer (WPP) taking part

Giannitrapani, Antonello

203

CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 1 in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard. Margaret Sheridan contributed to the residential forecast. Mitch Tian prepared the peak demand

204

CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 2 Director #12; i ACKNOWLEDGEMENTS The demand forecast is the combined product prepared the peak demand forecast. Ravinderpal Vaid provided the projections of commercial

205

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

206

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

207

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

208

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

209

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

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

Skill forecasting from different wind power ensemble prediction methods This article has been downloaded from IOPscience. Please scroll down to see the full text article.  

E-Print Network [OSTI]

Skill forecasting from different wind power ensemble prediction methods This article has been Contact us My IOPscience #12;Skill forecasting from different wind power ensemble prediction methods uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation

Paris-Sud XI, Université de

212

Renewable Forecast Min-Max2020.xls  

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

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

213

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

214

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

215

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

216

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

217

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

218

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

219

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

220

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

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

222

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)  

SciTech Connect (OSTI)

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

Hodge, B.

2013-12-01T23:59:59.000Z

223

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

224

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

225

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

226

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 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 (TC) activity starting in early August. We have decided to discontinue our individual for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

227

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 1 SEPTEMBER 14, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all are not developing any new tropical cyclones after Earl and Fiona. We expect Earl to generate large amounts of ACE This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting

Gray, William

228

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

229

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

230

Development and Demonstration of a Relocatable Ocean OSSE System: Optimizing Ocean Observations for Hurricane Forecast  

E-Print Network [OSTI]

forecasts for individual storms and improved seasonal forecast of the ocean thermal energy availableDevelopment and Demonstration of a Relocatable Ocean OSSE System: Optimizing Ocean Observations in the Gulf of Mexico is being extended to provide NOAA the ability to evaluate new ocean observing systems

231

Technology Forecasting Scenario Development  

E-Print Network [OSTI]

analysis (LCA). - Per Dannemand Andersen (*) : M.Sc. (Mech. Eng.), B.Com. (Org.), Ph.D. (Management/science interaction, wind energy economics and implementing policies, decision sup- port to the Danish Energy Agency on wind energy issues, Danish executive committee member of IEA's wind energy agreement. - Dominic Idier

232

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...

233

Forecast calls for better models | EMSL  

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

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

234

NREL: Resource Assessment and Forecasting - Facilities  

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

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

235

NREL: Resource Assessment and Forecasting - Research Staff  

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

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

236

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

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

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

2010-05-01T23:59:59.000Z

237

Development and Deployment of an Advanced Wind Forecasting Technique  

E-Print Network [OSTI]

findings. Part 2 addresses how operators of wind power plants and power systems can incorporate advanced the output of advanced wind energy forecasts into decision support models for wind power plant and power and applications of power market simulation models around the world. Argonne's software tools are used extensively

Kemner, Ken

238

Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics  

E-Print Network [OSTI]

. Over the past two decades, ensembles of numerical weather prediction (NWP) models have been developed and phrases: Continuous ranked probability score; Density forecast; Ensem- ble system; Numerical weather prediction; Heteroskedastic censored regression; Tobit model; Wind energy. 1 #12;1 Introduction Accurate

Washington at Seattle, University of

239

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

240

Machine Learning for Demand Forecasting in Smart Grid Saima Aman, Wei Yin, Yogesh Simmhan, and Viktor Prasanna  

E-Print Network [OSTI]

Machine Learning for Demand Forecasting in Smart Grid Saima Aman, Wei Yin, Yogesh Simmhan for forecasting the energy consumption patterns in the USC campus microgrid, which can be used for energy use of AMIs and data collection in a Smart Grid environment means that all applications, including demand

Prasanna, Viktor K.

Note: This page contains sample records for the topic "idm forecasts energy" 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

Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network [OSTI]

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting-use forecasting of appliance energy use in the U.S. residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

242

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

243

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.

244

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

245

Electric Load Forecasting  

E-Print Network [OSTI]

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

246

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.

247

EWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology The Anemos Wind Power Forecasting Platform Technology -  

E-Print Network [OSTI]

EWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology 1 The Anemos Wind Power a professional, flexible platform for operating wind power prediction models, laying the main focus on state models from all over Europe are able to work on this platform. Keywords: wind energy, wind power

Boyer, Edmond

248

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

249

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

250

(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

251

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

252

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

253

TV Energy Consumption Trends and Energy-Efficiency Improvement Options  

E-Print Network [OSTI]

a forecast for total energy consumption in network standbyconsiderable impact on total energy consumption from TVs.factors affecting total energy consumption. Although further

Park, Won Young

2011-01-01T23:59:59.000Z

254

Use of wind power forecasting in operational decisions.  

SciTech Connect (OSTI)

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

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

2011-11-29T23:59:59.000Z

255

Sixth Northwest Conservation & Electric Power Plan Draft Wholesale Power Price Forecasts  

E-Print Network [OSTI]

Price Forecasts 4. Updated load-resource balance by zones\\ regions Energy Capacity 5. Impact Higher Coal Prices Medium Long-term Trend Forecasts for PNW Zones 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1 Northwest Power and Conservation Council Comparison of Annual Average Energy Draft 6th Plan vs. Interim

256

Wind Power Forecasting  

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

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

257

Characterizing emerging industrial technologies in energy models  

E-Print Network [OSTI]

EIA), 2001. Annual Energy Outlook 2002, Energy Informationas forecasted in the Annual Energy Outlook 2002, we estimateQuads based on the Annual Energy Outlook 2002 (AEO 2002) (

Laitner, John A. Skip; Worrell, Ernst; Galitsky, Christina; Hanson, Donald A.

2003-01-01T23:59:59.000Z

258

Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets  

SciTech Connect (OSTI)

This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.

Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

2005-02-09T23:59:59.000Z

259

Subscriber Services Complete Forecast  

E-Print Network [OSTI]

Travel Sunday Neighbors Real Estate Education School Report Card Religion Automotive Obituaries funded solar energy, then cut funding off, heavily funded electric vehicles, then cut funding off. Fusion has the advantages of nuclear (fission) power - abundant electricity with no greenhouse gas

260

STATE OF CALIFORNIA -NATURAL RESOURCES AGENCY CALIFORNIA ENERGY COMMISSION  

E-Print Network [OSTI]

demand forecast Every two years the Energy Commission prepares a 1O-year forecast of electricity and natural gas demand. The forecast is used in various forums, including the IEPR, the California Public will finalize the California Energy Demand Forecast 2012-20

Note: This page contains sample records for the topic "idm forecasts energy" 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

Wind Power Forecasting Data  

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

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

262

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

263

California Energy Commission STAFF REPORT  

E-Print Network [OSTI]

.............................................................................................................................2 Purpose of Transportation Fuel Price and Demand Forecasts.....................................................................................................................3 CHAPTER 2: Long-Term Fuel Demand Forecast MethodsCalifornia Energy Commission STAFF REPORT TRANSPORTATION FUEL PRICE CASES AND DEMAND SCENARIOS

264

A Cosmology Forecast Toolkit -- CosmoLib  

E-Print Network [OSTI]

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

Zhiqi Huang

2012-06-11T23:59:59.000Z

265

Learning Energy Demand Domain Knowledge via Feature Transformation  

E-Print Network [OSTI]

Learning Energy Demand Domain Knowledge via Feature Transformation Sanzad Siddique Department -- Domain knowledge is an essential factor for forecasting energy demand. This paper introduces a method knowledge substantially improves energy demand forecasting accuracy. However, domain knowledge may differ

Povinelli, Richard J.

266

Chameleon foreCAST  

E-Print Network [OSTI]

Dark energy models, such as the chameleon, where the acceleration of the expansion of the universe results from the dynamics of a scalar field coupled to matter, suffer from the potential existence of a fifth force. Three known mechanisms have been proposed to restore General Relativity in the solar system and the laboratory, which are the symmetron/Damour-Polyakov effect, the Vainshtein property and the chameleon screening. Here, we propose to probe the existence of chameleons in the laboratory, considering their particle physics consequences. We envisage the resonant and non-resonant production of chameleons in the sun and their back-conversion into X-ray photons in a solar helioscope pipe such as the one used by CAST. A detection of these X-rays would indicate the existence of chameleons. We focus on a template model for the solar magnetic field: a constant magnetic field in a narrow shell surrounding the tachocline. The X-ray photons in a helioscope pipe obtained from back-conversion of the chameleons created inside the sun have a spectrum which is peaked in the sub-keV region, just below the actual sensitivity range of the present axion helioscopes. Nevertheless they are detectable by present day magnetic helioscopes like CAST and Sumico, which were built originally for solar axions. We also propose a chameleon-through-a-wall experiment whereby X-ray photons from a synchroton radiation source could be converted into chameleons inside a dipole magnet, then pass a wall which is opaque to X-rays before being back-converted into X-ray photons in a second magnet downstream. We show that this could provide a direct signature for the existence of chameleon particles.

Philippe Brax; Axel Lindner; Konstantin Zioutas

2012-01-24T23:59:59.000Z

267

California Energy Commission DRAFT COMMITTEE REPORT  

E-Print Network [OSTI]

DEMAND FORECASTS Prepared in Support of the 2011 Integrated Energy Policy Report DECEMBER 2010 CEC demand forecasts, demand-side management and energy efficiency impacts, private supply impacts, forecast, peak, self-generation, conservation, demand-side, energy, efficiency, price, retail, end use

268

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

269

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

270

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

271

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...

272

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...

273

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

274

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.

275

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

276

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

277

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

278

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

279

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.

280

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

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


281

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

282

Energy Efficiency Design Options for Residential Water Heaters: Economic Impacts on Consumers  

E-Print Network [OSTI]

Administration. 2010. Annual Energy Outlook 2010 withthe price forecasts in EIAs Annual Energy Outlook 2010. The

Lekov, Alex

2011-01-01T23:59:59.000Z

283

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

284

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

285

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

286

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

287

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

288

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

289

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

290

-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

291

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

292

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

293

LED Lighting Forecast | Department of Energy  

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

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

294

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

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

295

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

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

296

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

297

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

298

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint  

SciTech Connect (OSTI)

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

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

2013-11-01T23:59:59.000Z

299

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

300

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

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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

Forecast of Contracting and Subcontracting Opportunities, Fiscal year 1995  

SciTech Connect (OSTI)

Welcome to the US Department of Energy`s Forecast of Contracting and Subcontracting Opportunities. This forecast, which is published pursuant to Public Low 100--656, ``Business Opportunity Development Reform Act of 1988,`` is intended to inform small business concerns, including those owned and controlled by socially and economically disadvantaged individuals, and women-owned small business concerns, of the anticipated fiscal year 1995 contracting and subcontracting opportunities with the Department of Energy and its management and operating contractors and environmental restoration and waste management contractors. This document will provide the small business contractor with advance notice of the Department`s procurement plans as they pertain to small, small disadvantaged and women-owned small business concerns.Opportunities contained in the forecast support the mission of the Department, to serve as advocate for the notion`s energy production, regulation, demonstration, conservation, reserve maintenance, nuclear weapons and defense research, development and testing, when it is a national priority. The Department`s responsibilities include long-term, high-risk research and development of energy technology, the marketing of Federal power, and maintenance of a central energy data collection and analysis program. A key mission for the Department is to identify and reduce risks, as well as manage waste at more than 100 sites in 34 states and territories, where nuclear energy or weapons research and production resulted in radioactive, hazardous, and mixed waste contamination. Each fiscal year, the Department establishes contracting goals to increase contracts to small business concerns and meet our mission objectives.

Not Available

1995-02-01T23:59:59.000Z

302

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

303

Energy Prices and California's Economic  

E-Print Network [OSTI]

1 Energy Prices and California's Economic Security David RolandHolst October, 2009 on Energy Prices, Renewables, Efficiency, and Economic Growth: Scenarios and Forecasts, financial support drivers, the course of fossil fuel energy prices, energy efficiency trends, and renewable energy

Sadoulet, Elisabeth

304

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

305

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

306

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

307

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

308

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

309

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

310

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.

311

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

312

Forecasting of wind speed using wavelets analysis and cascade-correlation neural networks  

E-Print Network [OSTI]

for the city of Perpignan (south of France). In this sense, forecasting average wind speeds was the main such as sunlight, wind, rain or geothermal heat. Wind energy is actually one of the fastest-growing forms of electricity generation because wind is a clean, indigenous and inexhaustible energy resource that can generate

Paris-Sud XI, Université de

313

Advanced statistical methods for shortterm wind power forecasting Research proposal draft  

E-Print Network [OSTI]

a promising Monte­Carlo training scheme (Neal 1995) to data from the wind­energy industry, with some successAdvanced statistical methods for short­term wind power forecasting Research proposal draft Alex 1994), but more powerful nonlinear techniques have received little attention (MacKay 1995). In the wind­energy

Barnett, Alex

314

Gaussian Processes for Short-Horizon Wind Power Forecasting Joseph Bockhorst, Chris Barber  

E-Print Network [OSTI]

on this task, and attention has shifted to statistical and machine learning approaches. Among the challenges of wind energy into electrical trans- mission systems. The importance of wind forecasts for wind energy throughout a power system must be nearly in balance at all times, 2) because it depends strongly on wind

Bockhorst, Joseph

315

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

316

Conservation The Role of Energy EfficiencyThe Role of Energy Efficiency  

E-Print Network [OSTI]

to "Engineering and Economic Determinist's" Forecastsand Economic Determinist's" Forecasts Utilities planned and and Economic Determinist's"Engineering and Economic Determinist's Forecasts and PlansForecasts and Plans #12Northwest Power and Conservation Council The Role of Energy EfficiencyThe Role of Energy Efficiency

317

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.

318

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

319

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

320

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

E-Print Network [OSTI]

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

Essays in International Macroeconomics and Forecasting  

E-Print Network [OSTI]

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

Bejarano Rojas, Jesus Antonio

2012-10-19T23:59:59.000Z

322

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

SciTech Connect (OSTI)

On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

Bolinger, Mark A.; Wiser, Ryan H.

2010-01-04T23:59:59.000Z

323

Sectoral trends in global energy use and greenhouse gas emissions  

E-Print Network [OSTI]

A1 scenario forecasts GDP energy intensity to continue toby activity levels and the energy intensity of the specificDemand Activity x Energy Intensity Additional information on

2006-01-01T23:59:59.000Z

324

Dynamic Algorithm for Space Weather Forecasting System  

E-Print Network [OSTI]

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

Fischer, Luke D.

2011-08-08T23:59:59.000Z

325

Nambe Pueblo Water Budget and Forecasting model.  

SciTech Connect (OSTI)

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

Brainard, James Robert

2009-10-01T23:59:59.000Z

326

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

327

Baseline data for the residential sector and development of a residential forecasting database  

SciTech Connect (OSTI)

This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

1994-05-01T23:59:59.000Z

328

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

E-Print Network [OSTI]

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

329

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

330

Tracking Progress Last updated 5/7/2014 Statewide Energy Demand 1  

E-Print Network [OSTI]

dollars) to $1.8 trillion in 2012 (2012 dollars). Forecast Electricity Demand Although the California Energy Commission's energy demand forecast includes multiple scenarios, the Energy Commission worked together1 to agree upon a single managed demand forecast that incorporates all energy efficiency

331

Electric Grid - Forecasting system licensed | ornl.gov  

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

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

332

NREL: Resource Assessment and Forecasting - Data and Resources  

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

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

333

Analysis of PG E's residential end-use metered data to improve electricity demand forecasts  

SciTech Connect (OSTI)

It is generally acknowledged that improvements to end-use load shape and peak demand forecasts for electricity are limited primarily by the absence of reliable end-use data. In this report we analyze recent end-use metered data collected by the Pacific Gas and Electric Company from more than 700 residential customers to develop new inputs for the load shape and peak demand electricity forecasting models used by the Pacific Gas and Electric Company and the California Energy Commission. Hourly load shapes are normalized to facilitate separate accounting (by the models) of annual energy use and the distribution of that energy use over the hours of the day. Cooling electricity consumption by central air-conditioning is represented analytically as a function of climate. Limited analysis of annual energy use, including unit energy consumption (UEC), and of the allocation of energy use to seasons and system peak days, is also presented.

Eto, J.H.; Moezzi, M.M.

1992-06-01T23:59:59.000Z

334

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

335

Term Structure of Commodities Futures. Forecasting and Pricing. Marcos Escobar, Nicols Hernndez, Luis Seco  

E-Print Network [OSTI]

1 Term Structure of Commodities Futures. Forecasting and Pricing. Marcos Escobar, Nicolás Hernández that often exhibit sudden changes from backwardation into contango (such as energy, agricultural products generation purposes. It also provides the "risk neutral" processes needed for derivatives pricing, answering

Seco, Luis A.

336

Impact of GPS Zenith Tropospheric Delay data on precipitation forecasts in Mediterranean France and Spain  

E-Print Network [OSTI]

implies that the GPS data has good potential for influencing numerical models in rapidly developing, high for the forecasting of rainfall. Water vapor plays an important role in energy transfer and in the formation of clouds. 1992]. Rocken et al. (1993)] demonstrated agreement between water vapor radiometers and GPS derived

Haase, Jennifer

337

Powering Up With Space-Time Wind Forecasting Amanda S. HERING and Marc G. GENTON  

E-Print Network [OSTI]

Powering Up With Space-Time Wind Forecasting Amanda S. HERING and Marc G. GENTON The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality be more realistically assessed with a loss measure that depends upon the power curve relating wind speed

Genton, Marc G.

338

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (TO APPEAR, 2014) 1 Electricity Market Forecasting  

E-Print Network [OSTI]

to spatially-varying energy prices, known as locational marginal prices (LMPs) [24], [17]. Schemes inference. Day-ahead price forecast- ing is cast as a low-rank kernel learning problem. Uniquely exploiting- temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes

Giannakis, Georgios

339

GENERAL TECHNICAL REPORT PSW-GTR-245 Forecasting Productivity in Forest Fire  

E-Print Network [OSTI]

, efficiency analysis) for economic analysis of the potential hazard posed by forest ecosystems conditionsGENERAL TECHNICAL REPORT PSW-GTR-245 50 Forecasting Productivity in Forest Fire Suppression Francisco Rodríguez y Silva2 and Armando González-Cabán3 Abstract The abandonment of land, the high energy

Standiford, Richard B.

340

Autoregressive Time Series Forecasting of Computational Demand  

E-Print Network [OSTI]

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

Sandholm, Thomas

2007-01-01T23:59:59.000Z

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While these samples are representative of the content of NLEBeta,
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

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

E-Print Network [OSTI]

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

Borenstein, Severin; Farrell, Joseph

2006-01-01T23:59:59.000Z

342

Potential to Improve Forecasting Accuracy: Advances in Supply Chain Management  

E-Print Network [OSTI]

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

Datta, Shoumen

2008-07-31T23:59:59.000Z

343

Market perceptions of efficiency and news in analyst forecast errors  

E-Print Network [OSTI]

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

Chevis, Gia Marie

2004-11-15T23:59:59.000Z

344

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

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

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

345

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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

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

2010-04-01T23:59:59.000Z

346

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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

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

2010-04-15T23:59:59.000Z

347

Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)  

SciTech Connect (OSTI)

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

Hodge, B. M.; Milligan, M.

2011-07-01T23:59:59.000Z

348

0 20 4010 Miles NOAA Harmful Algal Bloom Operational Forecast System  

E-Print Network [OSTI]

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

349

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

350

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

351

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1  

E-Print Network [OSTI]

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

Washington at Seattle, University of

352

Forecast Combinations of Computational Intelligence and Linear Models for the  

E-Print Network [OSTI]

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

Atiya, Amir

353

GET your forecast at the click of a button.  

E-Print Network [OSTI]

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

Greenslade, Diana

354

Compatibility of Stand Basal Area Predictions Based on Forecast Combination  

E-Print Network [OSTI]

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

Cao, Quang V.

355

MOUNTAIN WEATHER PREDICTION: PHENOMENOLOGICAL CHALLENGES AND FORECAST METHODOLOGY  

E-Print Network [OSTI]

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

Steenburgh, Jim

356

WP1: Targeted and informative forecast system design  

E-Print Network [OSTI]

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

Stevenson, Paul

357

Earnings forecast bias -a statistical analysis Franois Dossou  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

358

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta*  

E-Print Network [OSTI]

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

Ahmad, Sajjad

359

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

E-Print Network [OSTI]

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

360

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

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

Heinemann, Detlev

Note: This page contains sample records for the topic "idm forecasts energy" 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

Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1  

E-Print Network [OSTI]

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

362

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

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

Stevenson, Paul

363

Draft for Public Comment Appendix A. Demand Forecast  

E-Print Network [OSTI]

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

364

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

McBurney, Peter

365

Using Belief Functions to Forecast Demand for Mobile Satellite Services  

E-Print Network [OSTI]

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

McBurney, Peter

366

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

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

de Lijser, Peter

367

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

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

Boyer, Edmond

368

FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH  

E-Print Network [OSTI]

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

Perez, Richard R.

369

California Baseline Energy Demands to 2050 for Advanced Energy Pathways  

E-Print Network [OSTI]

ED2, September. CEC (2005b) Energy demand forecast methodsCalifornia Baseline Energy Demands to 2050 for Advancedof a baseline scenario for energy demand in California for a

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

370

Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000  

SciTech Connect (OSTI)

The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

Das, S.

1991-12-01T23:59:59.000Z

371

How Can China Lighten Up? Urbanization, Industrialization and Energy Demand Scenarios  

E-Print Network [OSTI]

on the forecast of total energy demand. Based on this, weIndustrialization and Energy Demand Scenarios Nathaniel T.adjustment spurred energy demand for construction of new

Aden, Nathaniel T.

2010-01-01T23:59:59.000Z

372

New Method and Reporting of Uncertainty in LBNL National Energy Modeling System Runs  

E-Print Network [OSTI]

PV Quad SO 2 TWh Annual Energy Outlook combined GPRA caseshows the most recent Annual Energy Outlook (AEO) value, theradical. The Annual Energy Outlook forecasts unrestricted

Gumerman, Etan Z.; LaCommare, Kristina Hamachi; Marnay, Chris

2002-01-01T23:59:59.000Z

373

Max Tech Appliance Design: Potential for Maximizing U.S. Energy Savings through Standards  

E-Print Network [OSTI]

Administration, Annual Energy Outlook. Can be downloaded at:forecasts in its Annual Energy Outlook (AEO) [2], based onaeo/overview/). In its Annual Energy Outlook (AEO) (http://

Garbesi, Karina

2011-01-01T23:59:59.000Z

374

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

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

Stevenson, Paul

375

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS MARINE SERVICE  

E-Print Network [OSTI]

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

Greenslade, Diana

376

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

377

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

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

Stoffelen, Ad

378

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

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

Howat, Ian M.

379

Facebook IPO updated valuation and user forecasting  

E-Print Network [OSTI]

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

380

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

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

Note: This page contains sample records for the topic "idm forecasts energy" 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

Segmenting Time Series for Weather Forecasting  

E-Print Network [OSTI]

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

Sripada, Yaji

382

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

383

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

384

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

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

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

385

Using Wikipedia to forecast disease  

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

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

386

Using Wikipedia to forecast diseases  

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

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

387

timber quality Modelling and forecasting  

E-Print Network [OSTI]

facilities match the more traditional requirements of timber production. As this policy evolves will also incorporate carbon and energy budgeting modules to assist in the cost­benefit analysis of forest aimed at the optimisation of sustainable management, the provision of renewable resources

388

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

389

California Energy Demand Scenario Projections to 2050  

E-Print Network [OSTI]

California Energy Demand Scenario Projections to 2050 RyanCEC (2003a) California energy demand 2003-2013 forecast.CEC (2005a) California energy demand 2006-2016: Staff energy

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

390

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL 2000 3000 Water Year 1963 0 50 100 150 200 250 300 350 0 200 400 Water Year 1964 0 50 100 150 200 250 Year 1968 0 50 100 150 200 250 300 350 0 1000 2000 3000 Water Year 1969 0 50 100 150 200 250 300 350 0

391

Sandia National Laboratories: solar forecasting  

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

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

392

Stellar Astrophysics Requirements NERSC Forecast  

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

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

393

Technology data characterizing refrigeration in commercial buildings: Application to end-use forecasting with COMMEND 4.0  

SciTech Connect (OSTI)

In the United States, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of the refrigeration end use in terms of specific technologies, however, is complicated by several factors. First, the number of configurations of refrigeration cases and systems is quite large. Also, energy use is a complex function of the refrigeration-case properties and the refrigeration-system properties. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. Expanding end-use forecasting models so that they address individual technology options requires characterization of the present floorstock in terms of service requirements, energy technologies used, and cost-efficiency attributes of the energy technologies that consumers may choose for new buildings and retrofits. This report describes the process by which we collected refrigeration technology data. The data were generated for COMMEND 4.0 but are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Sezgen, O.; Koomey, J.G.

1995-12-01T23:59:59.000Z

394

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

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

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

2014-12-30T23:59:59.000Z

395

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

396

P h y s i c a l O c e a n o g r a p h y D i v i s i o n Optimizing Ocean Observations for Hurricane Forecast Improvement  

E-Print Network [OSTI]

forecasts for individual storms and improved seasonal forecast of the ocean thermal energy availableP h y s i c a l O c e a n o g r a p h y D i v i s i o n Optimizing Ocean Observations for Hurricane to provide NOAA the ability to evaluate new ocean observing systems, and alternate deployments of existing

397

A sensitivity analysis of the treatment of wind energy in the AEO99 version of NEMS  

E-Print Network [OSTI]

Administration. 1998. Annual Energy Outlook 1999: WithDepartment of Energys Annual Energy Outlook (AEO) forecastDepartment of Energys Annual Energy Outlook 1999 (AEO99)

Osborn, Julie G.; Wood, Frances; Richey, Cooper; Sanders, Sandy; Short, Walter; Koomey, Jonathan

2001-01-01T23:59:59.000Z

398

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

E-Print Network [OSTI]

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

399

Wind power forecasting : state-of-the-art 2009.  

SciTech Connect (OSTI)

Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

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

2009-11-20T23:59:59.000Z

400

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

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...

402

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)...

403

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...

404

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%...

405

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

SciTech Connect (OSTI)

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

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

2012-09-01T23:59:59.000Z

406

Forecasting the underlying potential governing climatic time series  

E-Print Network [OSTI]

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

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

2012-01-01T23:59:59.000Z

407

Econometric model and futures markets commodity price forecasting  

E-Print Network [OSTI]

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

Just, Richard E.; Rausser, Gordon C.

1979-01-01T23:59:59.000Z

408

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network [OSTI]

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

Gordon, Geoffrey J.

409

California Energy Commission STAFF REPORT  

E-Print Network [OSTI]

document preparation support Chris Kavalec--demand forecast #12;ii #12;iii AND DEMAND OUTLOOK MAY 2012 CEC-200-2012-003 CALIFORNIA ENERGY COMMISSION Edmund ABSTRACT The Summer 2012 Electricity Supply and Demand Outlook is the California Energy

410

NREL: Energy Analysis - Marissa Hummon  

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

Marissa Hummon is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Engineer On staff since January 2010 Phone number: 303-275-3269...

411

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

412

Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts  

SciTech Connect (OSTI)

It is generally acknowledged that improvements to end-use load shape and peak demand forecasts for electricity are limited primarily by the absence of reliable end-use data. In this report we analyze recent end-use metered data collected by the Pacific Gas and Electric Company from more than 700 residential customers to develop new inputs for the load shape and peak demand electricity forecasting models used by the Pacific Gas and Electric Company and the California Energy Commission. Hourly load shapes are normalized to facilitate separate accounting (by the models) of annual energy use and the distribution of that energy use over the hours of the day. Cooling electricity consumption by central air-conditioning is represented analytically as a function of climate. Limited analysis of annual energy use, including unit energy consumption (UEC), and of the allocation of energy use to seasons and system peak days, is also presented.

Eto, J.H.; Moezzi, M.M.

1992-06-01T23:59:59.000Z

413

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

414

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

E-Print Network [OSTI]

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

415

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

E-Print Network [OSTI]

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

416

Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller  

E-Print Network [OSTI]

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

Teschner, Matthias

417

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

E-Print Network [OSTI]

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

Smith, Emily (Emily C.)

2010-01-01T23:59:59.000Z

418

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

419

National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment  

SciTech Connect (OSTI)

The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

1982-03-31T23:59:59.000Z

420

AN APPLICATION OF URBANSIM TO THE AUSTIN, TEXAS REGION: INTEGRATED-MODEL FORECASTS FOR THE YEAR 2030  

E-Print Network [OSTI]

AN APPLICATION OF URBANSIM TO THE AUSTIN, TEXAS REGION: INTEGRATED-MODEL FORECASTS FOR THE YEAR, as well as energy consumption and greenhouse gas emissions. This work describes the modeling of year-2030 policies significantly impact the region's future land use patterns, traffic conditions, greenhouse gas

Kockelman, Kara M.

Note: This page contains sample records for the topic "idm forecasts energy" 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

Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision  

SciTech Connect (OSTI)

A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

Not Available

1994-12-01T23:59:59.000Z

422

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

423

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

E-Print Network [OSTI]

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

424

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

E-Print Network [OSTI]

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

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

425

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

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

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

2014-10-27T23:59:59.000Z

426

Distribution Based Data Filtering for Financial Time Series Forecasting  

E-Print Network [OSTI]

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

Bailey, James

427

On the reliability of seasonal forecasts Antje Weisheimer  

E-Print Network [OSTI]

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

Stevenson, Paul

428

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

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

Boyer, Edmond

429

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

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

Keller, Arturo A.

430

Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1  

E-Print Network [OSTI]

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

Miami, University of

431

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

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

Hansens, Jim

432

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

SciTech Connect (OSTI)

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

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

2012-07-01T23:59:59.000Z

433

Quality Guidelines for Energy System Studies: Fuel Pricing  

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

Benefits 20 Fuel Prices for Selected Feedstocks in NETL Studies Quality Guidelines for Energy System Studies November 2012 Forecasting the delivered price of any coal to any...

434

2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.  

SciTech Connect (OSTI)

This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

United States. Bonneville Power Administration.

2006-07-01T23:59:59.000Z

435

2007 Wholesale Power Rate Case Initial Proposal : Market Price Forecast Study.  

SciTech Connect (OSTI)

This chapter presents BPA's market price forecasts, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's rates. AURORA is used as the primary tool for (a) calculation of the demand rate, (b) shaping the PF rate, (c) estimating the forward price for the IOU REP settlement benefits calculation for fiscal years 2008 and 2009, (d) estimating the uncertainty surrounding DSI payments, (e) informing the secondary revenue forecast and (f) providing a price input used for the risk analysis.

United States. Bonneville Power Administration.

2005-11-01T23:59:59.000Z

436

Wind speed forecasting at different time scales: a non parametric approach  

E-Print Network [OSTI]

The prediction of wind speed is one of the most important aspects when dealing with renewable energy. 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 reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model.

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

437

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

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

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

2014-07-09T23:59:59.000Z

438

Development and testing of improved statistical wind power forecasting methods.  

SciTech Connect (OSTI)

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

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

2011-12-06T23:59:59.000Z

439

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

a prime location for ocean thermal energy conversion (OTEC).resource, geothermal and ocean thermal energy are available

Sathaye, Jayant

2013-01-01T23:59:59.000Z

440

Technology data characterizing space conditioning in commercial buildings: Application to end-use forecasting with COMMEND 4.0  

SciTech Connect (OSTI)

In the US, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of space conditioning end uses in terms of specific technologies is complicated by several factors. First, the number of configurations of heating, ventilating, and air conditioning (HVAC) systems and heating and cooling plants is very large. Second, the properties of the building envelope are an integral part of a building`s HVAC energy consumption characteristics. Third, the characteristics of commercial buildings vary greatly by building type. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. This report describes the process by which the authors collected space-conditioning technology data and then mapped it into the COMMEND 4.0 input format. The data are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Sezgen, O.; Franconi, E.M.; Koomey, J.G.; Greenberg, S.E.; Afzal, A.; Shown, L.

1995-12-01T23:59:59.000Z

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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

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

442

Documentation of the Hourly Time Series NCEP Climate Forecast System Reanalysis  

E-Print Network [OSTI]

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

443

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

E-Print Network [OSTI]

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

Fenton, Norman

444

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

E-Print Network [OSTI]

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

445

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

E-Print Network [OSTI]

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

Farrell, Brian F.

446

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

447

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

E-Print Network [OSTI]

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

Wu, Dekai

448

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

E-Print Network [OSTI]

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

Shyy, Wei

449

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

450

ASSESSING THE QUALITY AND ECONOMIC VALUE OF WEATHER AND CLIMATE FORECASTS  

E-Print Network [OSTI]

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

Katz, Richard

451

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.

452

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

E-Print Network [OSTI]

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

453

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

454

Photovoltaic Forecasting: A state of the art B. Espinar, J.L. Aznarte, R. Girard, A.M. Moussa and G. Kariniotakis  

E-Print Network [OSTI]

Photovoltaic Forecasting: A state of the art B. Espinar, J.L. Aznarte, R. Girard, A.M. Moussa and G(0)493678967; FAX: +33 (0)493957535,bella.espinar@mines-paristech.fr Abstract Photovoltaic (PV) energy, together Introduction Photovoltaics (PV) for electricity generation is the fastest-growing energy technology since 2002

Paris-Sud XI, Universit de

455

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we once again find that the AEO 2007 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. Specifically, the NYMEX-AEO 2007 premium is $0.73/MMBtu levelized over five years. In other words, on average, one would have had to pay $0.73/MMBtu more than the AEO 2007 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

456

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

457

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

SciTech Connect (OSTI)

On December 9, the reference case projections from ''Annual Energy Outlook 2005 (AEO 2005)'' were posted on the Energy Information Administration's (EIA) web site. As some of you may be aware, we at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past four years, forward natural gas contracts (e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past four years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation (presuming that long-term price stability is valued). In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2005. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or, more recently (and briefly), http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past four AEO releases (AEO 2001-AE0 2004), we once again find that the AEO 2005 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEXAEO 2005 reference case comparison yields by far the largest premium--$1.11/MMBtu levelized over six years--that we have seen over the last five years. In other words, on average, one would have to pay $1.11/MMBtu more than the AEO 2005 reference case natural gas price forecast in order to lock in natural gas prices over the coming six years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation. Fixed-price renewables obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of six years.

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

458

STATEWIDE ENERGY EFFICIENCY POTENTIAL ESTIMATES AND TARGETS  

E-Print Network [OSTI]

rates of forecasted natural gas consumption, electricity consumption and peak electricity demand potential for electric consumption savings, 85 percent of the economic potential for peak demand savings Energy efficiency, energy savings, demand reduction, electricity consumption, natural gas consumption

459

Californias Energy Future: Transportation Energy Use in California  

E-Print Network [OSTI]

intensity and reducing carbon intensity. The equations belowin energy use and carbon intensity. We forecast that totalFleet Average a Fuel Carbon Intensity (C) kWh/mi gCO 2 /gge

Yang, Christopher; Ogden, Joan M; Hwang, Roland; Sperling, Daniel

2011-01-01T23:59:59.000Z

460

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

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

Yang, Xiaorong

2008-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap  

E-Print Network [OSTI]

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

Romo, Juan

462

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

E-Print Network [OSTI]

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

Kemner, Ken

463

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

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

464

Optimally controlling hybrid electric vehicles using path forecasting  

E-Print Network [OSTI]

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

Katsargyri, Georgia-Evangelina

2008-01-01T23:59:59.000Z

465

Variable Selection and Inference for Multi-period Forecasting Problems  

E-Print Network [OSTI]

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

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

466

Optimally Controlling Hybrid Electric Vehicles using Path Forecasting  

E-Print Network [OSTI]

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

Kolmanovsky, Ilya V.

467

Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1  

E-Print Network [OSTI]

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

Levinson, David M.

468

africa conditional forecasts: Topics by E-print Network  

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

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

469

An econometric analysis and forecasting of Seoul office market  

E-Print Network [OSTI]

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

Kim, Kyungmin

2011-01-01T23:59:59.000Z

470

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

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

Marroquin Cabrera, Juan Carlos

1998-01-01T23:59:59.000Z

471

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

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

Sadjadi, S. Masoud

472

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

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

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

473

Grid-scale Fluctuations and Forecast Error in Wind Power  

E-Print Network [OSTI]

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

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

2015-01-01T23:59:59.000Z

474

Grid-scale Fluctuations and Forecast Error in Wind Power  

E-Print Network [OSTI]

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

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

2015-03-29T23:59:59.000Z

475

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

E-Print Network [OSTI]

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

Adut, Davit

2004-09-30T23:59:59.000Z

476

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z

477

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

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

Lim, Eun-pa

478

Forecasting and strategic inventory placement for gas turbine aftermarket spares  

E-Print Network [OSTI]

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

Simmons, Joshua T. (Joshua Thomas)

2007-01-01T23:59:59.000Z

479

Mesoscale predictability and background error convariance estimation through ensemble forecasting  

E-Print Network [OSTI]

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

Ham, Joy L

2002-01-01T23:59:59.000Z

480

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

Oahu Development of geothermal energy can be pro~ moted only13 Role of Renewables Geothermal energy cabL;; system tv:illQ New Techno of hence geothermal energy would be available

Sathaye, Jayant

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "idm forecasts energy" 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

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...

482

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

483

Subhourly wind forecasting techniques for wind turbine operations  

SciTech Connect (OSTI)

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

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

1984-08-01T23:59:59.000Z

484

Streamflow forecasting for large-scale hydrologic systems  

E-Print Network [OSTI]

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

Awwad, Haitham Munir

1991-01-01T23:59:59.000Z

485

An Evaluation of the Sustainability and Scalability of Business Models that Support Low-cost Assisted Home Energy Assessments Using A Cost Benefit Analysis.  

E-Print Network [OSTI]

??Energy costs and forecasted climate change have recently prompted organizations withinthe residential building sector and homeowners alike to increase their attention towards reducingresidential energy consumption. (more)

Hinsey, Jason

2012-01-01T23:59:59.000Z

486

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network [OSTI]

that forecast US residential energy consumption by end-use.new unit energy consumption in the U.S. DOE appliancethe Residential Energy Consumption Survey, or RECS (US DOE

Wenzel, T.P.

2010-01-01T23:59:59.000Z

487

Evaluation Framework and Tools for Distributed Energy Resources  

E-Print Network [OSTI]

Administration. 2001. "Annual Energy Outlook 2002." DOE/EIA-SOAPP TAF T&D TSI Annual Energy Outlook 2002 combined heatEIA) 2002 Annual Energy Outlook (AEO) forecast anticipates

Gumerman, Etan Z.; Bharvirkar, Ranjit R.; LaCommare, Kristina Hamachi; Marnay, Chris

2003-01-01T23:59:59.000Z

488

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

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

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

2005-09-26T23:59:59.000Z

489

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

E-Print Network [OSTI]

fluctuations in renewable energy production (such as when aof renewable resources into the energy production portfolioof renewable energy are implemented, energy production is

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

490

A Statistical Solar Flare Forecast Method  

E-Print Network [OSTI]

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

M. S. Wheatland

2005-05-14T23:59:59.000Z

491

Hawaii energy strategy report, October 1995  

SciTech Connect (OSTI)

This is a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

NONE

1995-10-01T23:59:59.000Z

492

Hawaii energy strategy: Executive summary, October 1995  

SciTech Connect (OSTI)

This is an executive summary to a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

NONE

1995-10-01T23:59:59.000Z

493

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

E-Print Network [OSTI]

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

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

2010-01-01T23:59:59.000Z

494

Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results  

SciTech Connect (OSTI)

This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

Koomey, J.G.; Brown, R.E.; Richey, R. [and others

1995-12-01T23:59:59.000Z

495

2013DvorakandSailor'sEnergy Model Forecasting Accuracy Along  

E-Print Network [OSTI]

electric-load data Developed method to detect peaks for shown load regions over 4-5 years Found East a REpower 5M, 5 MW power curve, determined capacity factor out to 200-m depth Incredibly strong resource

Firestone, Jeremy

496

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of Inspector GeneralDepartmentAUDIT REPORTOpenWende NewSowitec doWinvestFlumeFinalAIRMaster+AMIS (Smart

497

ANL Software Improves Wind Power Forecasting | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Year in Review: Top Five EERE Blog Posts of(Revision 2) |6.pdfALIGNMENT: AFocus on Reducing

498

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

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

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

499

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

of a range of world oil prices for future energy demand andTo examine the ef feet of oil prices on energy demand andprojections of world oil prices. Th and demand. determined

Sathaye, Jayant

2013-01-01T23:59:59.000Z

500

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

SciTech Connect (OSTI)

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

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z