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1

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates...

Ana Corberán-Vallet; José D. Bermúdez; José V. Segura…

2010-01-01T23:59:59.000Z

2

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

Different spare parts are associated with different underlying demand patterns, which in turn require different forecasting methods. Consequently, there is a need to categorise stock keeping units (SKUs) and apply the most appropriate methods in each category. For intermittent demands, Croston's method (CRO) is currently regarded as the standard method used in industry to forecast the relevant inventory requirements; this is despite the bias associated with Croston's estimates. A bias adjusted modification to CRO (Syntetos–Boylan Approximation, SBA) has been shown in a number of empirical studies to perform very well and be associated with a very ‘robust’ behaviour. In a 2005 article, entitled ‘On the categorisation of demand patterns’ published by the Journal of the Operational Research Society, Syntetos et al. (2005) suggested a categorisation scheme, which establishes regions of superior forecasting performance between CRO and SBA. The results led to the development of an approximate rule that is expressed in terms of fixed cut-off values for the following two classification criteria: the squared coefficient of variation of the demand sizes and the average inter-demand interval. Kostenko and Hyndman (2006) revisited this issue and suggested an alternative scheme to distinguish between CRO and SBA in order to improve overall forecasting accuracy. Claims were made in terms of the superiority of the proposed approach to the original solution but this issue has never been assessed empirically. This constitutes the main objective of our work. In this paper the above discussed classification solutions are compared by means of experimentation on more than 10,000 \\{SKUs\\} from three different industries. The results enable insights to be gained into the comparative benefits of these approaches. The trade-offs between forecast accuracy and other implementation related considerations are also addressed.

G. Heinecke; A.A. Syntetos; W. Wang

2013-01-01T23:59:59.000Z

3

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode ... is proposed in this paper. Firstly,...

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

4

Short term forecasting of solar radiation based on satellite data  

E-Print Network (OSTI)

Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer University, D-26111 Oldenburg Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. Fluctuations of solar irradiance

Heinemann, Detlev

5

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect

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

6

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Table 2. Total Energy Consumption, Actual vs. Forecasts Table 3. Total Petroleum Consumption, Actual vs. Forecasts Table 4. Total Natural Gas Consumption, Actual vs. Forecasts Table 5. Total Coal Consumption, Actual vs. Forecasts Table 6. Total Electricity Sales, Actual vs. Forecasts Table 7. Crude Oil Production, Actual vs. Forecasts Table 8. Natural Gas Production, Actual vs. Forecasts Table 9. Coal Production, Actual vs. Forecasts Table 10. Net Petroleum Imports, Actual vs. Forecasts Table 11. Net Natural Gas Imports, Actual vs. Forecasts Table 12. Net Coal Exports, Actual vs. Forecasts Table 13. World Oil Prices, Actual vs. Forecasts Table 14. Natural Gas Wellhead Prices, Actual vs. Forecasts Table 15. Coal Prices to Electric Utilities, Actual vs. Forecasts

7

Weather-based forecasts of California crop yields  

SciTech Connect

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

8

Journey data based arrival forecasting for bicycle hire schemes  

E-Print Network (OSTI)

Journey data based arrival forecasting for bicycle hire schemes Marcel C. Guenther and Jeremy T. The global emergence of city bicycle hire schemes has re- cently received a lot of attention of future bicycle migration trends, as these assist service providers to ensure availability of bicycles

Imperial College, London

9

Fuzzy rule-based methodology for residential load behaviour forecasting during power systems restoration  

Science Journals Connector (OSTI)

Inadequate load pickup during power system restoration can lead to overload and underfrequency conditions, and even restart the blackout process, due to thermal energy losses. Thus, load behaviour estimation during restoration is desirable to avoid inadequate pickups. This work describes an artificial intelligence method to aid the operator in taking decisions during system restoration by estimating residential load behaviour parameters such as overload in buses and the necessary time to recover steady-state power consumption. This method uses a fuzzy rule-based system to forecast the residential load, obtaining correct estimates with low computational cost. Test results using actual substation data are presented.

Lia Toledo Moreira Mota; Alexandre Assis Mota; Andre Luiz Morelato Franca

2005-01-01T23:59:59.000Z

10

NURBS interpolation based on exponential smoothing forecasting  

Science Journals Connector (OSTI)

To meet the requirement of exploring a new generation of CNC systems based on STEP-NC, NURBS interpolation has been studied. In contrast to existing NURBS interpolation based on the Taylor’s expansion, this paper...

Zhou Kai; Wang Guanjun; Jin Houzhong…

2008-12-01T23:59:59.000Z

11

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect

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

12

Ensemble-based air quality forecasts: A multimodel approach applied to ozone  

E-Print Network (OSTI)

Ensemble-based air quality forecasts: A multimodel approach applied to ozone Vivien Mallet1., and B. Sportisse (2006), Ensemble-based air quality forecasts: A multimodel approach applied to ozone, J, the uncertainty in chem- istry transport models is a major limitation of air quality forecasting. The source

Boyer, Edmond

13

Log-normal distribution based EMOS models for probabilistic wind speed forecasting  

E-Print Network (OSTI)

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods an...

Baran, Sándor

2014-01-01T23:59:59.000Z

14

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

The information content, that is, the predictive capability, of a forecast system is often quantified with skill scores. This paper introduces two ranked mutual information skill (RMIS) scores, RMISO and RMISY, for the evaluation of probabilistic ...

Bodo Ahrens; André Walser

2008-01-01T23:59:59.000Z

15

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

16

A New Neuro-Based Method for Short Term Load Forecasting of Iran National Power System  

Science Journals Connector (OSTI)

This paper presents a new neuro-based method for short term load forecasting of Iran national power system (INPS). A MultiLayer Perceptron ( ... were selected through a peer investigation on historical data relea...

R. Barzamini; M. B. Menhaj; Sh. Kamalvand…

2005-01-01T23:59:59.000Z

17

Research on the risk forecast model in the coal mine system based on GSPA-Markov  

Science Journals Connector (OSTI)

Safety accidents in the coal mine occurred frequently, that how to reduce them became an important national task, which the hazards identification and the risk forecast work in the coal mine system can solve. In the process of risk forecast in the coal mine system, considering characteristics that system risk is different in different period, the IDO (identification, difference, opposition) change rule of the set pair which has element weight is analyzed, and on the basis of which, the system risk forecast model based on GSPA-MARKOV is put forward. The application example shows that the risk state in the coal mine system is forecasted by the transition probability and the ergodicity in the model, which embodies fully dynamic, predictable and so on , thus it provides a new method to determine the risk state in the coal mine system.

LI De-shun; XU Kai-li

2011-01-01T23:59:59.000Z

18

Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis  

Science Journals Connector (OSTI)

Abstract The prediction of GHG emissions is very important due to their negative impacts on climate and global warming. The aim of this study was to develop a model for GHG forecasting emissions at the national level using a new approach based on artificial neural networks (ANN) and broadly available sustainability, economical and industrial indicators acting as inputs. The ANN model architecture and training parameters were optimized, with inputs being selected using correlation analysis and principal component analysis. The developed ANN models were compared with the corresponding multiple linear regression (MLR) model, while an ANN model created using transformed inputs (principal components) was compared with a principal component regression (PCR) model. Since the best results were obtained with the ANN model based on correlation analysis, that particular model was selected for the actual 2011 GHG emissions forecasting. The relative errors of the 2010 GHG emissions predictions were used to adjust the ANN model predictions for 2011, which subsequently resulted in the adjusted 2011 predictions having a MAPE value of only 3.60%. Sensitivity analysis showed that gross inland energy consumption had the highest sensitivity to GHG emissions.

Davor Z. Antanasijevi?; Mirjana ?. Risti?; Aleksandra A. Peri?-Gruji?; Viktor V. Pocajt

2014-01-01T23:59:59.000Z

19

Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models  

Science Journals Connector (OSTI)

Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha?1 in June and 0.4 t ha?1 in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha?1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.

Felix Kogan; Nataliia Kussul; Tatiana Adamenko; Sergii Skakun; Oleksii Kravchenko; Oleksii Kryvobok; Andrii Shelestov; Andrii Kolotii; Olga Kussul; Alla Lavrenyuk

2013-01-01T23:59:59.000Z

20

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

E-Print Network (OSTI)

by one blank line, and from the paper body by two blank lines. 1. INTRODUCTION Fluctuations of solarSHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS Annette Solar World Congress. This portion of the paper is the abstract. The abstract should not exceed 250

Heinemann, Detlev

Note: This page contains sample records for the topic "actual base forecast" 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

Research of least squares support vector regression based on differential evolution algorithm in short-term load forecasting model  

Science Journals Connector (OSTI)

To improve the accuracy of short-term load forecasting a differential evolution algorithm (DE) based least squares support vector regression (LSSVR) method is proposed in this paper. Through optimizing the regularization parameter and kernel parameter of the LSSVR by DE a short-term load forecasting model which can take load affected factors such as meteorology weather and date types into account is built. The proposed LSSVR method is proved by implementing short-term load forecasting on the real historical data of Yangquan power system in China. The average forecasting error is less than 1.6% which shows better accuracy and stability than the traditional LSSVR and Support vector regression. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system more efficiently.

2014-01-01T23:59:59.000Z

22

ANL Software Improves Wind Power Forecasting | Department of...  

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

principal investigator for the project. For wind power point forecasting, ARGUS PRIMA trains a neural network using data from weather forecasts, observations, and actual wind...

23

A Scenario-Based Hydrocarbon Production Forecast for Louisiana  

Science Journals Connector (OSTI)

Fields are classified as oil or gas based on the volume of ... in cubic feet) per unit of produced oil (measured in barrels), and described through the gas–oil ratio (GOR). Cumulative GOR (CGOR) is the aggregate ...

Mark J. Kaiser; Yunke Yu

2012-03-01T23:59:59.000Z

24

Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant  

Science Journals Connector (OSTI)

This paper presents an artificial neural network (ANN) approach for forecasting the performance of electric energy generated output from a working 25-kWp grid connected solar PV system and a 100-kWp grid connected PV system installed at Minicoy Island of Union Territory of Lakshadweep Islands. The ANN interpolates among the solar PV generation output and relevant parameters such as solar radiation, module temperature and clearness index. In this study, three ANN models are implemented and validated with reasonable accuracy on real electric energy generation output data. The first model is univariate based on solar radiation and the output values. The second model is a multivariate model based on module temperature along with solar radiation. The third model is also a multivariate model based on module temperature, solar radiation and clearness index. A forecasting performance measure such as percentage root mean square error has been presented for each model. The second model, which gives the most accurate results, has been used in forecasting the generation output for another PV system with similar accuracy.

Imtiaz Ashraf; A. Chandra

2004-01-01T23:59:59.000Z

25

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

SciTech Connect

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

26

A suite of metrics for assessing the performance of solar power forecasting  

Science Journals Connector (OSTI)

Abstract Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encountered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. 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, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiative’s efforts to improve the accuracy of solar forecasting. 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, sensitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform forecasting improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes. Day-ahead and 1-hour-ahead forecasts for both simulated and actual solar power plants are analyzed. The results show that the proposed metrics can efficiently evaluate the quality of solar forecasts and assess the economic and reliability impacts of improved solar forecasting. Sensitivity analysis results show that (i) all proposed metrics are suitable to show the changes in the accuracy of solar forecasts with uniform forecasting improvements, and (ii) the metrics of skewness, kurtosis, and Rényi entropy are specifically suitable to show the changes in the accuracy of solar forecasts with ramp forecasting improvements and a ramp forecasting threshold.

Jie Zhang; Anthony Florita; Bri-Mathias Hodge; Siyuan Lu; Hendrik F. Hamann; Venkat Banunarayanan; Anna M. Brockway

2015-01-01T23:59:59.000Z

27

BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting  

Science Journals Connector (OSTI)

Abstract Rising carbon emission or carbon footprint imposes grave concern over the earth?s climatic condition, as it results in increasing average global temperature. Renewable energy sources seem to be the favorable solution in this regard. It can reduce the overall energy consumption rate globally. However, the renewable sources are intermittent in nature with very high initial installation price. Off-grid Small Autonomous Hybrid Power Systems (SAHPS) are good alternative for generating electricity locally in remote areas, where the transmission and distribution of electrical energy generated from conventional sources are otherwise complex, difficult and costly. In optimizing SAHPS, weather data over past several years are generally the main input, which include wind speed and solar radiation. The weather resources used in this optimization process have unsystematic variations based on the atmospheric and seasonal phenomenon and it also varies from year to year. While using past data in the analysis of SAHPS performance, it was assumed that the same pattern will be followed in the next year, which in reality is very unlikely to happen. In this paper, we use BBO optimization algorithm for SAHPS optimal component sizing by minimizing the cost of energy. We have also analysed the effect of using forecast weather data instead of past data on the SAHPS performance. ANNs, which are trained with back-propagation training algorithm, are used for wind speed and solar radiation forecasting. A case study was used for demonstrating the performance of BBO optimization algorithm along with forecasting effects. The simulation results clearly showed the advantages of utilizing wind speed and solar radiation forecasting in a SAHPS optimization problem.

R.A. Gupta; Rajesh Kumar; Ajay Kumar Bansal

2015-01-01T23:59:59.000Z

28

A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting  

Science Journals Connector (OSTI)

In existing researches, the investigations of oil price volatility are always performed based on daily data and squared daily return is always taken as the proxy of actual volatility. However, it is widely accepted that the popular realized volatility (RV) based on high frequency data is a more robust measure of actual volatility than squared return. Due to this motivation, we investigate dynamics of daily volatility of Shanghai fuel oil futures prices employing 5-minute high frequency data. First, using a nonparametric method, we find that RV displays strong long-range dependence and recent financial crisis can cause a lower degree of long-range dependence. Second, we model daily volatility using RV models and GARCH-class models. Our results indicate that RV models for intraday data overwhelmingly outperform GARCH-class models for daily data in forecasting fuel oil price volatility, regardless the proxy of actual volatility. Finally, we investigate the major source of such volatile prices and found that trader activity has major contribution to fierce variations of fuel oil prices.

Li Liu; Jieqiu Wan

2012-01-01T23:59:59.000Z

29

A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection  

Science Journals Connector (OSTI)

Abstract In competitive environment of deregulated electricity market, short-term load forecasting (STLF) is a major discussion for efficient operation of power systems. Therefore, the area of electricity load forecasting is still essential need for more accurate and stable load forecast algorithm. However, the electricity load is a non-linear signal with high degree of volatility. In this paper, a new forecasted method based on neural network (NN) and chaotic intelligent feature selection is presented. The proposed feature selection method selects the best set of candidate input which is used as input data for the forecasted. The theory of phase space reconstruction under Taken’s embedding theorem is used to prepare candidate features. Then, candidate inputs relevance to target value are measured by using correlation analysis. Forecast engine is a multilayer perception layer (MLP) NN with hybrid Levenberg–Marquardt (LM) and Differential Evolutionary (DE) learning algorithm. The proposed STLF is tested on PJM and New England electricity markets and compared with some of recent STLF techniques.

Sajjad Kouhi; Farshid Keynia; Sajad Najafi Ravadanegh

2014-01-01T23:59:59.000Z

30

RACORO Forecasting  

NLE Websites -- All DOE Office Websites (Extended Search)

Daniel Hartsock CIMMS, University of Oklahoma ARM AAF Wiki page Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance...

31

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

Much of product level forecasting is based upon time series techniques. However, traditional time series forecasting techniques have offered either smoothing constant adaptability or consideration of various t...

John T. Mentzer Ph.D.

32

A Displacement-Based Error Measure Applied in a Regional Ensemble Forecasting System  

Science Journals Connector (OSTI)

Errors in regional forecasts often take the form of phase errors, where a forecasted weather system is displaced in space or time. For such errors, a direct measure of the displacement is likely to be more valuable than traditional measures. A ...

Christian Keil; George C. Craig

2007-09-01T23:59:59.000Z

33

A compressed sensing based AI learning paradigm for crude oil price forecasting  

Science Journals Connector (OSTI)

Abstract Due to the complexity of crude oil price series, traditional statistics-based forecasting approach cannot produce a good prediction performance. In order to improve the prediction performance, a novel compressed sensing based learning paradigm is proposed through integrating compressed sensing based denoising (CSD) and certain artificial intelligence (AI), i.e., CSD-AI. In the proposed learning paradigm, CSD is first performed as a preprocessor for the original data of international crude oil price to eliminate the noise, and then a certain powerful AI tool is employed to conduct prediction for the cleaned data. In particular, the process of CSD aims to reduce the level of noise which pollutes the data, and to further enhance the prediction performance of the AI model. For verification purpose, international crude oil price series of West Texas Intermediate (WTI) are taken as sample data. Empirical results demonstrate that the proposed CSD-AI learning paradigm significantly outperforms all other benchmark models including single models without CSD process and hybrid models with other denoising techniques, in terms of level and directional accuracies. Furthermore, in the case of different data samples with different time ranges, the proposed model performs the best, indicating that the proposed CSD-AI learning paradigm is an effective and robust approach in crude oil price prediction.

Lean Yu; Yang Zhao; Ling Tang

2014-01-01T23:59:59.000Z

34

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm  

Science Journals Connector (OSTI)

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these \\{IMFs\\} could be accurately predicted. Finally, the prediction results of all \\{IMFs\\} are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm.

Lean Yu; Shouyang Wang; Kin Keung Lai

2008-01-01T23:59:59.000Z

35

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sanchez by Esmeralda Sanchez Errata -(7/14/04) The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors, expressed as the percentage difference between the Reference Case projection and actual historic value, shown for every AEO and for each year in the forecast horizon (for a given variable). The historical data are typically taken from the Annual Energy Review (AER). The last column of Table 1 provides a summary of the most recent average absolute forecast errors. The calculation of the forecast error is shown in more detail in Tables 2 through 18. Because data for coal prices to electric generating plants were not available from the AER, data from the Monthly Energy Review (MER), July 2003 were used.

36

Weather Forecasting System Based on Satellite Imageries Using Neuro-fuzzy Techniques  

Science Journals Connector (OSTI)

We have built an automated Satellite Images Forecasting System with Neuro-Fuzzy techniques. Firstly, Subtractive Clustering is applied on to a satellite image to extract the locations of the clouds. This is follo...

Chien-Wan Tham; Sion-Hui Tian; Liya Ding

2002-01-01T23:59:59.000Z

37

Energy Demand Forecasting in China Based on Dynamic RBF Neural Network  

Science Journals Connector (OSTI)

A dynamic radial basis function (RBF) network model is proposed for energy demand forecasting in this paper. Firstly, we ... detail. At last, the data of total energy demand in China are analyzed and experimental...

Dongqing Zhang; Kaiping Ma; Yuexia Zhao

2011-01-01T23:59:59.000Z

38

A comparative study on conventional and advanced exergetic analyses of geothermal district heating systems based on actual operational data  

Science Journals Connector (OSTI)

This paper comparatively evaluates exergy destructions of a geothermal district heating system (GDHS) using both conventional and advanced exergetic analysis methods to identify the potential for improvement and the interactions among the components. As a real case study, the Afyon GDHS in Afyonkarahisar, Turkey, is considered based on actual operational data. For the first time, advanced exergetic analysis is applied to the GDHSs, in which the exergy destruction rate within each component is split into unavoidable/avoidable and endogenous/exogenous parts. The results indicate that the interconnections among all the components are not very strong. Thus, one should focus on how to reduce the internal inefficiency (destruction) rates of the components. The highest priority for improvement in the advanced exergetic analysis is in the re-injection pump (PM-IX), while it is the heat exchanger (HEX-III) in the conventional analysis. In addition, there is a substantial influence on the overall system as the total avoidable exergy destruction rate of the heat exchanger (HEX-V) has the highest value. On the overall system basis, the value for the conventional exergetic efficiency is determined to be 29.29% while that for the modified exergetic efficiency is calculated to be 34.46% through improving the overall components.

Arif Hepbasli; Ali Keçeba?

2013-01-01T23:59:59.000Z

39

Improving an Accuracy of ANN-Based Mesoscale-Microscale Coupling Model by Data Categorization: With Application to Wind Forecast for Offshore and Complex Terrain Onshore Wind Farms  

Science Journals Connector (OSTI)

The ANN-based mesoscale-microscale coupling model forecasts wind speed and wind direction with high accuracy for wind parks located in complex terrain onshore, yet some weather regimes remains unresolved and f...

Alla Sapronova; Catherine Meissner…

2014-01-01T23:59:59.000Z

40

Forecasting the daily outbreak of topic-level political risk from social media using hidden Markov model-based techniques  

Science Journals Connector (OSTI)

Abstract Nowadays, as an arena of politics, social media ignites political protests, so analyzing topics discussed negatively in the social media has increased in importance for detecting a nation's political risk. In this context, this paper designs and examines an automatic approach to forecast the daily outbreak of political risk from social media at a topic level. It evaluates the forecasting performances of topic features, investigated among the previous works that analyze social media data for politics, hidden Markov model (HMM)-based techniques, widely used for the anomaly detection with time-series data, and detection models, into which the topic features and the detection techniques are combined. When applied to South Korea's Web forum, Daum Agora, statistical comparisons with the constraints of false positive rate of political risk, and eventually the predictive governance benefits the people.

Jong Hwan Suh

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation by Susan H. Holte In this paper, the Office of Integrated Analysis and Forecasting (OIAF) of the Energy Information Administration (EIA) evaluates the projections published in the Annual Energy Outlook (AEO), (1) by comparing the projections from the Annual Energy Outlook 1982 through the Annual Energy Outlook 2001 with actual historical values. A set of major consumption, production, net import, price, economic, and carbon dioxide emissions variables are included in the evaluation, updating similar papers from previous years. These evaluations also present the reasons and rationales for significant differences. The Office of Integrated Analysis and Forecasting has been providing an

42

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

4 4 Notes: This graph shows monthly average spot West Texas Intermediate crude oil prices. Spot WTI crude oil prices peaked last fall as anticipated boosts to world supply from OPEC and other sources did not show up in actual stocks data. So where do we see crude oil prices going from here? Crude oil prices are expected to be about $28-$30 per barrel for the rest of this year, but note the uncertainty bands on this projection. They give an indication of how difficult it is to know what these prices are going to do. Also, EIA does not forecast volatility. This relatively flat forecast could be correct on average, with wide swings around the base line. Let's explore why we think prices will likely remain high, by looking at an important market barometer - inventories - which measures the

43

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

44

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

45

PBL FY 2002 Third Quarter Review Forecast of Generation Accumulated...  

NLE Websites -- All DOE Office Websites (Extended Search)

Power Business Line Generation Accumulated Net Revenues Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) FY 2002 Third Quarter Review Forecast in Millions...

46

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

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

47

Forecast Prices  

Gasoline and Diesel Fuel Update (EIA)

Notes: Notes: Prices have already recovered from the spike, but are expected to remain elevated over year-ago levels because of the higher crude oil prices. There is a lot of uncertainty in the market as to where crude oil prices will be next winter, but our current forecast has them declining about $2.50 per barrel (6 cents per gallon) from today's levels by next October. U.S. average residential heating oil prices peaked at almost $1.50 as a result of the problems in the Northeast this past winter. The current forecast has them peaking at $1.08 next winter, but we will be revisiting the outlook in more detail next fall and presenting our findings at the annual Winter Fuels Conference. Similarly, diesel prices are also expected to fall. The current outlook projects retail diesel prices dropping about 14 cents per gallon

48

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

E-Print Network (OSTI)

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

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

2006-01-01T23:59:59.000Z

49

Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case  

Science Journals Connector (OSTI)

Abstract Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications.

Mauro Castelli; Leonardo Vanneschi; Matteo De Felice

2015-01-01T23:59:59.000Z

50

SPACE TECHNOLOGY Actual Estimate  

E-Print Network (OSTI)

SPACE TECHNOLOGY TECH-1 Actual Estimate Budget Authority (in $ millions) FY 2011 FY 2012 FY 2013 FY.7 247.0 Exploration Technology Development 144.6 189.9 202.0 215.5 215.7 214.5 216.5 Notional SPACE TECHNOLOGY OVERVIEW .............................. TECH- 2 SBIR AND STTR

51

Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting Task  

Science Journals Connector (OSTI)

We investigate accuracy, neural network complexity and sample size problem in multilayer perceptron (MLP) based (neuro-linear) feature extraction. For feature extraction we use weighted sums calculated in hidd...

Aistis Raudys

2005-01-01T23:59:59.000Z

52

A warranty forecasting model based on piecewise statistical distributions and stochastic simulation  

E-Print Network (OSTI)

industry and has a specific application to automotive electronics. The warranty prediction model is based is demonstrated using a case study of automotive electronics warranty returns. The approach developed b CALCE Electronic Products and Systems Center, Department of Mechanical Engineering, University

Sandborn, Peter

53

Trait-based approaches to conservation physiology: forecasting environmental change risks from the bottom up  

Science Journals Connector (OSTI)

...Such trait-based conservation physiology is illustrated...rising temperatures on water loss in ectotherms...especially to inform conservation biology. At times such...from the regional pool of individuals), a...performance (e.g. water-efficient organisms...

2012-01-01T23:59:59.000Z

54

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Download Adobe Acrobat Reader Printer friendly version on our site are provided in Adobe Acrobat Spreadsheets are provided in Excel Actual vs. Forecasts Formats Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF Table 12. World Oil Prices Excel, PDF Table 13. Natural Gas Wellhead Prices

55

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) MS Excel Viewer PDF (Acrobat Reader required Download Acrobat Reader ) Adobe Acrobat Reader Logo Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF

56

Building Energy Software Tools Directory: Energy Usage Forecasts  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy Usage Forecasts Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides free 14-day ahead energy usage forecasts based on the degree day forecasts for 1,200 stations in the U.S. and Canada. The user enters the daily non-weather base load and the usage per degree day weather factor; the tool applies the degree day forecast and displays the total energy usage forecast. Helpful FAQs explain the process and describe various options for the calculation of the base load and weather factor. Historical degree day reports and 14-day ahead degree day forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature, load calculation, energy simulation Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700.

57

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

This project applies Data Mining technology to the prediction of electric power system load forecast. It proposes a mining program of electric power load forecasting data based on the similarity of time series .....

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao…

2013-01-01T23:59:59.000Z

58

Aggregate vehicle travel forecasting model  

SciTech Connect

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

59

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

H Tables H Tables Appendix H Comparisons With Other Forecasts, and Performance of Past IEO Forecasts for 1990, 1995, and 2000 Forecast Comparisons Three organizations provide forecasts comparable with those in the International Energy Outlook 2005 (IEO2005). The International Energy Agency (IEA) provides “business as usual” projections to the year 2030 in its World Energy Outlook 2004; Petroleum Economics, Ltd. (PEL) publishes world energy forecasts to 2025; and Petroleum Industry Research Associates (PIRA) provides projections to 2015. For this comparison, 2002 is used as the base year for all the forecasts, and the comparisons extend to 2025. Although IEA’s forecast extends to 2030, it does not publish a projection for 2025. In addition to forecasts from other organizations, the IEO2005 projections are also compared with those in last year’s report (IEO2004). Because 2002 data were not available when IEO2004 forecasts were prepared, the growth rates from IEO2004 are computed from 2001.

60

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect

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

Note: This page contains sample records for the topic "actual base forecast" 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

Building Energy Software Tools Directory: Degree Day Forecasts  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecasts Forecasts Degree Day Forecasts example chart Quick and easy web-based tool that provides free 14-day ahead degree day forecasts for 1,200 stations in the U.S. and Canada. Degree Day Forecasts charts show this year, last year and three-year average. Historical degree day charts and energy usage forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700. Expertise Required No special expertise required. Simple to use. Users Over 1,000 weekly users. Audience Anyone who needs degree day forecasts (next 14 days) for the U.S. and Canada. Input Select a weather station (1,200 available) and balance point temperature. Output Charts show (1) degree day (heating and cooling) forecasts for the next 14

62

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) PDF (Acrobat Reader required) Table 2. Total Energy Consumption HTML, Excel, PDF Table 3. Total Petroleum Consumption HTML, Excel, PDF Table 4. Total Natural Gas Consumption HTML, Excel, PDF Table 5. Total Coal Consumption HTML, Excel, PDF Table 6. Total Electricity Sales HTML, Excel, PDF Table 7. Crude Oil Production HTML, Excel, PDF Table 8. Natural Gas Production HTML, Excel, PDF Table 9. Coal Production HTML, Excel, PDF Table 10. Net Petroleum Imports HTML, Excel, PDF Table 11. Net Natural Gas Imports HTML, Excel, PDF Table 12. Net Coal Exports HTML, Excel, PDF Table 13. World Oil Prices HTML, Excel, PDF

63

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

64

Wind Power Forecasting  

NLE Websites -- All DOE Office Websites (Extended Search)

Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email List Self Supplied Balancing Reserves Dynamic...

65

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

66

Wind Power Forecasting  

Science Journals Connector (OSTI)

The National Center for Atmospheric Research (NCAR) has configured a Wind Power Forecasting System for Xcel Energy that integrates high resolution and ensemble...

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

67

Energy Demand Forecasting  

Science Journals Connector (OSTI)

This chapter presents alternative approaches used in forecasting energy demand and discusses their pros and cons. It... Chaps. 3 and 4 ...

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

68

Electric Grid - Forecasting system licensed | ornl.gov  

NLE Websites -- All DOE Office Websites (Extended Search)

Electric Grid - Forecasting system licensed Location Based Technologies has signed an agreement to integrate and market an Oak Ridge National Laboratory technology that provides...

69

PBL FY 2003 Third Quarter Review Forecast of Generation Accumulated...  

NLE Websites -- All DOE Office Websites (Extended Search)

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2003 Third Quarter Review Forecast in Millions...

70

FY 2004 Second Quarter Review Forecast of Generation Accumulated...  

NLE Websites -- All DOE Office Websites (Extended Search)

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2004 Second Quarter Review Forecast In Millions...

71

Improving Inventory Control Using Forecasting  

E-Print Network (OSTI)

This project studied and analyzed Electronic Controls, Inc.’s forecasting process for three high-demand products. In addition, alternative forecasting methods were developed to compare to the current forecast method. The ...

Balandran, Juan

2005-12-16T23:59:59.000Z

72

A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey  

Science Journals Connector (OSTI)

Abstract The existing electricity systems have been substantially designed to allow only centralized power generation and unidirectional power flow. Therefore, the objective of improving the conventional power systems with the capabilities of decentralized generation and advanced control has conflicted with the present power system infrastructure and thus a profound change has necessitated in the current power grids. To that end, the concept of smart grid has been introduced at the last decades in order to accomplish the modernization of the power grid while incorporating various capabilities such as advanced metering, monitoring and self-healing to the systems. Among the various advanced components in smart grid structure, “smart home” is of vital importance due to its handling difficulties caused by the stochastic behaviors of inhabitants. However, limited studies concerning the implementation of smart homes have so far been reported in the literature. Motivated by this need, this paper investigates an experimental smart home with various renewable energy sources and storage systems in terms of several aspects such as in-home energy management, appliances control and power flow. Furthermore, the study represents one of the very first attempts to evaluate the contribution of power forecasting of renewable energy sources on the performance of smart home concepts.

A. Tascikaraoglu; A.R. Boynuegri; M. Uzunoglu

2014-01-01T23:59:59.000Z

73

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

74

CAPP 2010 Forecast.indd  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecast, Markets & Pipelines 1 Crude Oil Forecast, Markets & Pipelines June 2010 2 CANADIAN ASSOCIATION OF PETROLEUM PRODUCERS Disclaimer: This publication was prepared by the...

75

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

Abstract In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the daily Phelix index of the German and Austrian control area of the European Energy Exchange (???) wholesale electricity market. The forecast accuracy we achieved is 76.12% over a 200 day period.

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

76

Correspondence among the Correlation, RMSE, and Heidke Forecast Verification Measures; Refinement of the Heidke Score  

Science Journals Connector (OSTI)

The correspondence among the following three forecast verification scores, based on forecasts and their associated observations, is described: 1) the correlation score, 2) the root-mean-square error (RMSE) score, and 3) the Heidke score (based on ...

Anthony G. Barnston

1992-12-01T23:59:59.000Z

77

¿Era el actual superávit de neurocirujanos previsible en 2009? Análisis de la situación sobre la base de los datos del Informe de oferta y necesidad de especialistas médicos en España (2008-2025)  

Science Journals Connector (OSTI)

ResumenIntroducción En el año 2009 el Ministerio de Sanidad y Consumo (MSC), en el Informe de oferta y necesidad de especialistas médicos en España (2008-2025), categorizó el escenario de nuestra especialidad como de déficit moderado de especialistas. Sin embargo, la neurocirugía española vive actualmente una situación caracterizada por un excedente de neurocirujanos. Objetivos Determinar si, tras el informe del MSC, era posible en el año 2009 prever el exceso actual de neurocirujanos, así como cuál será la proyección más probable de oferta y demanda en el año 2017. Material y métodos A partir de los datos crudos extraídos del informe del MSC, del estudio sobre la edad de los neurocirujanos españoles realizado por la Junta Directiva de la SENEC en 2001 y de las tasas de mortalidad anual para los distintos rangos de edad ofrecidas por el Instituto Nacional de Estadística, realizamos una predicción de la evolución de la oferta y la demanda de neurocirujanos para los periodos 2008-2012 y 2013-2017. Resultados La situación actual de exceso de especialistas era previsible en 2009 y, de no tomarse las medidas oportunas, en el año 2017 probablemente existirá un superávit de más de 100 neurocirujanos en nuestro país, pudiendo alcanzarse una tasa de paro superior al 26% en el peor escenario. Conclusiones Es necesario y urgente limitar la oferta de plazas de residencia de neurocirugía y adecuarlas a la demanda real de especialistas existente. Para ello resulta imprescindible recabar información estructural actualizada y periódica de los distintos Servicios y Unidades de Neurocirugía, así como revisar las condiciones de acreditación de las más de 50 unidades docentes existentes en nuestro país. Introduction In 2009 the Spanish Ministry of Health (SMH) published the report of supply and demand of medical specialists in Spain (2008-2025), in which our specialty was considered as presenting a moderate deficit of consultants. However, Spanish neurosurgery is currently in a situation of having a surplus of neurosurgeons. Objectives To determine whether it was possible to predict the current excess of neurosurgeons in 2009 and to forecast the most likely perspective of supply and demand in 2017. Material and methods Raw data extracted from the SMH report, information on the ages of the Spanish neurosurgeons obtained from the study performed by our Board of Directors in 2001, and annual mortality rates for different age ranges provided by the National Institute of Statistics, were used to predict the evolution of supply and demand of neurosurgeons for the periods 2008-2012 and 2013-2017. Results The current situation of an excess of specialists was predictable in 2009, and if appropriate measures are not taken, a surplus of more than 100 neurosurgeons is likely in 2017, with an unemployment rate above 26% in the worst scenario. Conclusions In order to match the actual and future demand of specialists, it is necessary and urgent to reduce the number of neurosurgical in-training positions. To achieve this goal, it is essential to obtain periodical and up-to-date structural information of the different Neurosurgery Departments and Units, and to revisit the accreditation terms of the more than fifty current teaching units.

Rubén Martín-Láez; Javier Ibáñez; Alfonso Lagares; José Fernández-Alén; Ramiro Díez-Lobato

2012-01-01T23:59:59.000Z

78

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

energy data + forecasting energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. Links: FRED beta demo energy data + forecasting Syndicate content 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2084382122

79

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

80

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications  

E-Print Network (OSTI)

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange­based surface evapotranspiration

Niyogi, Dev

Note: This page contains sample records for the topic "actual base forecast" 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

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. ...

Steven T. Sonka; James W. Mjelde; Peter J. Lamb; Steven E. Hollinger; Bruce L. Dixon

1987-09-01T23:59:59.000Z

82

Comparing Forecast Skill  

Science Journals Connector (OSTI)

A basic question in forecasting is whether one prediction system is more skillful than another. Some commonly used statistical significance tests cannot answer this question correctly if the skills are computed on a common period or using a common ...

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

83

Short-Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling  

Science Journals Connector (OSTI)

This study examines 2–3-day solar irradiance forecasts with respect to their application in solar energy industries, such as yield prediction for the integration of the strongly fluctuating solar energy into the electricity grid. During cloud-...

Hanne Breitkreuz; Marion Schroedter-Homscheidt; Thomas Holzer-Popp; Stefan Dech

2009-09-01T23:59:59.000Z

84

Medium-term forecasting of demand prices on example of electricity prices for industry  

Science Journals Connector (OSTI)

In the paper, a method of forecasting demand prices for electric energy for the industry has been suggested. An algorithm of the forecast for 2006–2010 based on the data for 1997–2005 has been presented.

V. V. Kossov

2014-09-01T23:59:59.000Z

85

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

E-Print Network (OSTI)

and validation.   Solar Energy.   73:5, 307? Perez, R. , irradiance forecasts for solar energy applications based on forecast database.   Solar Energy.   81:6, 809?812.  

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

86

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02T23:59:59.000Z

87

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect

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

88

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network (OSTI)

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 waterFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil

Keller, Arturo A.

89

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

90

Sandia National Laboratories: solar forecasting  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

91

Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market  

Science Journals Connector (OSTI)

Abstract Modelling price formation in electricity markets is a notoriously difficult process, due to physical constraints on electricity generation and transmission, and the potential for market power. This difficulty has inspired the recent development of bottom-up agent-based algorithmic learning models of electricity markets. While these have proven quite successful in small models, few authors have attempted any validation of their model against real-world data in a more realistic model. In this paper we develop the SWEM model, where we take one of the most promising algorithms from the literature, a modified version of the Roth and Erev algorithm, and apply it to a 19-node simplification of the New Zealand electricity market. Once key variables such as water storage are accounted for, we show that our model can closely mimic short-run (weekly) electricity prices at these 19 nodes, given fundamental inputs such as fuel costs, network data, and demand. We show that agents in SWEM are able to manipulate market power when a line outage makes them an effective monopolist in the market. SWEM has already been applied to a wide variety of policy applications in the New Zealand market.22 This research was partly funded by a University of Auckland FDRF Grant #9554/3627082. The authors would like thank Andy Philpott, Golbon Zakeri, Anthony Downward, an anonymous referee, and participants at the EPOC Winter Workshop 2010 for their helpful comments.

David Young; Stephen Poletti; Oliver Browne

2014-01-01T23:59:59.000Z

92

Consensus Coal Production Forecast for  

E-Print Network (OSTI)

Rate Forecasts 19 5. EIA Forecast: Regional Coal Production 22 6. Wood Mackenzie Forecast: W.V. Steam to data currently published by the Energy Information Administration (EIA), coal production in the state in this report calls for state production to decline by 11.3 percent in 2009 to 140.2 million tons. During

Mohaghegh, Shahab

93

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 forecasting. I. INTRODUCTION HE actual large-scale integration of wind energy in several European countries enhance the position of wind energy compared to other dispatchable forms of generation. Predicting

Paris-Sud XI, Université de

94

On Sequential Probability Forecasting  

E-Print Network (OSTI)

at the same time. [Probability, Statistics and Truth, MacMillan 1957. page 11] ... the collective "denotes a collective wherein the attribute of the single event is the number of points thrown. [Probability, StatisticsOn Sequential Probability Forecasting David A. Bessler 1 David A. Bessler Texas A&M University

McCarl, Bruce A.

95

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sánchez The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors,

96

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect

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

97

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

98

Ensemble Forecasts and their Verification  

E-Print Network (OSTI)

· Ensemble forecast verification ­ Performance metrics: Brier Score, CRPSS · New concepts and developments of weather Sources: Insufficient spatial resolution, truncation errors in the dynamical equations

Maryland at College Park, University of

99

E-Print Network 3.0 - analytical energy forecasting Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

of PV energy production using... Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer... , Detlev Heinemann Energy and Semiconductor...

100

Probabilistic manpower forecasting  

E-Print Network (OSTI)

- ing E. Results- Probabilistic Forecasting . 26 27 Z8 29 31 35 36 38 39 IV. CONCLUSIONS. V. GLOSSARY 42 44 APPENDICES REFERENCES 50 70 LIST OF TABLES Table Page Outline of Job-Probability Matrix Job-Probability Matrix. Possible... Outcomes of Job A Possible Outcomes of Jobs A and B 10 Possible Outcomes of Jobs A, B and C II LIST GF FIGURES Figure Page Binary Representation of Numbers 0 Through 7 12 First Cumulative Probability Table 14 3. Graph of Cumulative Probability vs...

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

This paper reports on the development of a diagnostic approach that can be used to examine the sources of numerical model forecast error that contribute to degraded tropical cyclone (TC) motion forecasts. Tropical cyclone motion forecasts depend ...

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

102

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production.

Jianzhou Wang; Yao Dong; Jie Wu; Ren Mu; He Jiang

2011-01-01T23:59:59.000Z

103

Project Profile: Forecasting and Influencing Technological Progress...  

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

Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of...

104

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

105

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in...

106

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Title of Paper Annual Energy Outlook Forecast Evaluation Title of Paper Annual Energy Outlook Forecast Evaluation by Susan H. Holte OIAF has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Natural gas has generally been the fuel with the least accurate forecasts of consumption, production, and prices. Natural gas was the last fossil fuel to be deregulated following the strong regulation of energy markets in the 1970s and early 1980s. Even after deregulation, the behavior

107

Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe  

Science Journals Connector (OSTI)

Abstract This article combines and discusses three independent validations of global horizontal irradiance (GHI) multi-day forecast models that were conducted in the US, Canada and Europe. All forecast models are based directly or indirectly on numerical weather prediction (NWP). Two models are common to the three validation efforts – the ECMWF global model and the GFS-driven WRF mesoscale model – and allow general observations: (1) the GFS-based WRF- model forecasts do not perform as well as global forecast-based approaches such as ECMWF and (2) the simple averaging of models’ output tends to perform better than individual models.

Richard Perez; Elke Lorenz; Sophie Pelland; Mark Beauharnois; Glenn Van Knowe; Karl Hemker Jr.; Detlev Heinemann; Jan Remund; Stefan C. Müller; Wolfgang Traunmüller; Gerald Steinmauer; David Pozo; Jose A. Ruiz-Arias; Vicente Lara-Fanego; Lourdes Ramirez-Santigosa; Martin Gaston-Romero; Luis M. Pomares

2013-01-01T23:59:59.000Z

108

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

Forecasting Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels MICHAEL YE, ∗ JOHN ZYREN, ∗∗ AND JOANNE SHORE ∗∗ Abstract This paper presents a short-term monthly forecasting model of West Texas Intermedi- ate crude oil spot price using OECD petroleum inventory levels. Theoretically, petroleum inventory levels are a measure of the balance, or imbalance, between petroleum production and demand, and thus provide a good market barometer of crude oil price change. Based on an understanding of petroleum market fundamentals and observed market behavior during the post-Gulf War period, the model was developed with the objectives of being both simple and practical, with required data readily available. As a result, the model is useful to industry and government decision-makers in forecasting price and investigat- ing the impacts of changes on price, should inventories,

109

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

FRED FRED http://en.openei.org/community/group/fred Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. energy data + forecasting Fri, 22 Jun 2012 15:30:20 +0000 Dbrodt 34

110

FORSITE: a geothermal site development forecasting system  

SciTech Connect

The Geothermal Site Development Forecasting System (FORSITE) is a computer-based system being developed to assist DOE geothermal program managers in monitoring the progress of multiple geothermal electric exploration and construction projects. The system will combine conceptual development schedules with site-specific status data to predict a time-phased sequence of development likely to occur at specific geothermal sites. Forecasting includes estimation of industry costs and federal manpower requirements across sites on a year-by-year basis. The main advantage of the system, which relies on reporting of major, easily detectable industry activities, is its ability to use relatively sparse data to achieve a representation of status and future development.

Entingh, D.J.; Gerstein, R.E.; Kenkeremath, L.D.; Ko, S.M.

1981-10-01T23:59:59.000Z

111

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approaches provide the best basis for automated method selection, the Akaike information criteria having a slight edge over its information criteria counterparts.

Baki Billah; Maxwell L. King; Ralph D. Snyder; Anne B. Koehler

2006-01-01T23:59:59.000Z

112

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

113

Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation  

SciTech Connect

Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-08-13T23:59:59.000Z

114

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not ... Keywords: Artificial neural networks hybrid systems, Linear combination of forecasts, Maximum likelihood estimation, Time series forecasters, Unbiased forecasters

Paulo Renato A. Firmino; Paulo S. G. De Mattos Neto; Tiago A. E. Ferreira

2014-02-01T23:59:59.000Z

115

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

E-Print Network (OSTI)

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 N Collier N Charlotte S Charlotte NOAA Harmful Algal Bloom Operational Forecast System Southwest

116

A hybrid dynamic and fuzzy time series model for mid-term power load forecasting  

Science Journals Connector (OSTI)

Abstract A new hybrid model for forecasting the electric power load several months ahead is proposed. To allow for distinct responses from individual load sectors, this hybrid model, which combines dynamic (i.e., air temperature dependency of power load) and fuzzy time series approaches, is applied separately to the household, public, service, and industrial sectors. The hybrid model is tested using actual load data from the Seoul metropolitan area, and its predictions are compared with those from two typical dynamic models. Our investigation shows that, in the case of four-month forecasting, the proposed model gives the actual monthly power load of every sector with only less than 3% absolute error and satisfactory reduction of forecasting errors compared to other models from previous studies.

Woo-Joo Lee; Jinkyu Hong

2015-01-01T23:59:59.000Z

117

Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model  

E-Print Network (OSTI)

Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model, with a hindcast correlation over 16 seasons of 0.92 for South Africa and 0.62 for Zimbabwe. Over 17 seasons and actual maize water-stress in South Africa, and a correlation of 0.79 for the same relationship

Martin, Randall

118

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

Forecast Energy Forecast Energy Jump to: navigation, search Name Forecast Energy Address 2320 Marinship Way, Suite 300 Place Sausalito, California Zip 94965 Sector Services Product Intelligent Monitoring and Forecasting Services Year founded 2010 Number of employees 11-50 Company Type For profit Website http://www.forecastenergy.net Coordinates 37.865647°, -122.496315° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.865647,"lon":-122.496315,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

119

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

120

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

...Forrest Forecasting phenology under global warming Ines Ibanez 1 * Richard B. Primack...and site-specific responses to global warming. We found that for most species...climate change|East Asia, global warming|growing season, hierarchical...

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Demand Forecasting of New Products  

E-Print Network (OSTI)

Keeping Unit or SKU) employing attribute analysis techniques. The objective of this thesis is to improve Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock

Sun, Yu

122

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

123

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...

124

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

125

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

Solar Energy Market Forecast Solar Energy Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast Agency/Company /Organization: United States Department of Energy Sector: Energy Focus Area: Solar Topics: Market analysis, Technology characterizations Resource Type: Publications Website: giffords.house.gov/DOE%20Perspective%20on%20Solar%20Market%20Evolution References: Solar Energy Market Forecast[1] Summary " Energy markets / forecasts DOE Solar America Initiative overview Capital market investments in solar Solar photovoltaic (PV) sector overview PV prices and costs PV market evolution Market evolution considerations Balance of system costs Silicon 'normalization' Solar system value drivers Solar market forecast Additional resources"

126

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

E-Print Network (OSTI)

.32%, and a reduction in error from baseline models by up to 53%. Keywords-energy forecast models; energy informatics I that physically char- acterize a building, or are based on measured building performance data. Smart meters have analysis and machine learning methods can be used to mine sensor data and extract forecast models

Prasanna, Viktor K.

127

Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction  

E-Print Network (OSTI)

from numerical weather prediction models, which is based on a state-of-the-art circular-processing techniques for forecasts from numerical weather prediction models tend to become ineffective or inapplicableBias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction Le

Washington at Seattle, University of

128

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for ...

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

129

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: * Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the

130

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

We used experimental economics to test whether undergraduate students presented with a temperature forecast with uncertainty information in a table and bar graph format were able to use the extra information to interpret a given forecast. ...

Pricilla Marimo; Todd R. Kaplan; Ken Mylne; Martin Sharpe

131

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

132

Massachusetts state airport system plan forecasts.  

E-Print Network (OSTI)

This report is a first step toward updating the forecasts contained in the 1973 Massachusetts State System Plan. It begins with a presentation of the forecasting techniques currently available; it surveys and appraises the ...

Mathaisel, Dennis F. X.

133

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

For over 30 years, weather forecasting for the Antarctic continent and adjacent Southern Ocean has relied on weather satellites. Significant advancements in forecasting skill have come via the weather satellite. The advent of the high-resolution ...

Matthew A. Lazzara; Linda M. Keller; Charles R. Stearns; Jonathan E. Thom; George A. Weidner

2003-02-01T23:59:59.000Z

134

Forecasting Water Use in Texas Cities  

E-Print Network (OSTI)

In this research project, a methodology for automating the forecasting of municipal daily water use is developed and implemented in a microcomputer program called WATCAL. An automated forecast system is devised by modifying the previously...

Shaw, Douglas T.; Maidment, David R.

135

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

Accurate forecasting of energy demand plays a key role for utility companies, network operators, producers and suppliers of energy. Demand forecasts are utilized for unit commitment, market bidding, network operation and maintenance, integration of renewable ... Keywords: analytics, energy demand forecasting, machine learning, renewable energy sources, smart grids, smart meters

Mathieu Sinn

2014-06-01T23:59:59.000Z

136

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

137

Q. J. R. Meteorol. Soc. (2006), 132, pp. 29052923 doi: 10.1256/qj.06.25 Measuring forecast skill: is it real skill or is it the varying climatology?  

E-Print Network (OSTI)

that many commonly used systems of measurement (`metrics') in weather forecast verification are capable of weather forecasts from an accumulation of samples spanning many locations and dates. In calculating many is approximately invariant over all samples. If the event frequency actually varies among the samples, the metrics

Hamill, Tom

138

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

V. Shrivastava; R.B. Misra; R.C. Bansal

2012-01-01T23:59:59.000Z

139

Conceptual design of a geothermal site development forecasting system  

SciTech Connect

A site development forecasting system has been designed in response to the need to monitor and forecast the development of specific geothermal resource sites for electrical power generation and direct heat applications. The system is comprised of customized software, a site development status data base, and a set of complex geothermal project development schedules. The system would use site-specific development status information obtained from the Geothermal Progress Monitor and other data derived from economic and market penetration studies to produce reports on the rates of geothermal energy development, federal agency manpower requirements to ensure these developments, and capital expenditures and technical/laborer manpower required to achieve these developments.

Neham, E.A.; Entingh, D.J.

1980-03-01T23:59:59.000Z

140

Forecast of contracting and subcontracting opportunities. Fiscal year 1996  

SciTech Connect

This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

NONE

1996-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation Evaluation Annual Energy Outlook Forecast Evaluation by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the impacts were not incorporated in the AEO projections until their enactment or effective dates in accordance with EIA's requirement to remain policy neutral and include only current laws and regulations in the AEO reference case projections.

142

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation Analysis Papers > Annual Energy Outlook Forecast Evaluation Release Date: February 2005 Next Release Date: February 2006 Printer-friendly version Annual Energy Outlook Forecast Evaluation* Table 1.Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Printer Friendly Version Average Absolute Percent Error Variable AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Consumption Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8 Total Electricity Sales 1.9 2.0 2.3 2.3 2.3 2.4 Production Crude Oil Production 4.5 4.5 4.5 4.5 4.6 4.7

143

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

144

Essays on macroeconomics and forecasting  

E-Print Network (OSTI)

explanatory variables. Compared to Stock and Watson (2002)�s models, the models proposed in this chapter can further allow me to select the factors structurally for each variable to be forecasted. I find advantages to using the structural dynamic factor...

Liu, Dandan

2006-10-30T23:59:59.000Z

145

Table 13. Coal Production, Projected vs. Actual  

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

Coal Production, Projected vs. Actual" Coal Production, Projected vs. Actual" "Projected" " (million short tons)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",999,1021,1041,1051,1056,1066,1073,1081,1087,1098,1107,1122,1121,1128,1143,1173,1201,1223 "AEO 1995",,1006,1010,1011,1016,1017,1021,1027,1033,1040,1051,1066,1076,1083,1090,1108,1122,1137 "AEO 1996",,,1037,1044,1041,1045,1061,1070,1086,1100,1112,1121,1135,1156,1161,1167,1173,1184,1190 "AEO 1997",,,,1028,1052,1072,1088,1105,1110,1115,1123,1133,1146,1171,1182,1190,1193,1201,1209 "AEO 1998",,,,,1088,1122,1127.746338,1144.767212,1175.662598,1176.493652,1182.742065,1191.246948,1206.99585,1229.007202,1238.69043,1248.505981,1260.836914,1265.159424,1284.229736

146

Table 22. Energy Intensity, Projected vs. Actual  

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

Energy Intensity, Projected vs. Actual" Energy Intensity, Projected vs. Actual" "Projected" " (quadrillion Btu / real GDP in billion 2005 chained dollars)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",11.24893441,11.08565002,10.98332766,10.82852279,10.67400621,10.54170176,10.39583203,10.27184573,10.14478673,10.02575883,9.910410202,9.810812106,9.69894802,9.599821783,9.486985399,9.394733753,9.303329725,9.221322623 "AEO 1995",,10.86137373,10.75116461,10.60467959,10.42268977,10.28668187,10.14461664,10.01081222,9.883759026,9.759022105,9.627404949,9.513643295,9.400418762,9.311729546,9.226142899,9.147374752,9.071102491,8.99599906 "AEO 1996",,,10.71047701,10.59846153,10.43655044,10.27812088,10.12746866,9.9694713,9.824165152,9.714832565,9.621874334,9.532324916,9.428169355,9.32931308,9.232716414,9.170931044,9.086870061,9.019963901,8.945602337

147

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network (OSTI)

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc, 2000 Abstract The marketing team of a new telecommunications company is usually tasked with producing involved in doing so. Based on our three decades of experience working with telecommunications operators

Parsons, Simon

148

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.

149

Utility of SCaMPR Satellite versus Ground-Based Quantitative Precipitation Estimates in Operational Flood Forecasting: The Effects of TRMM Data Ingest  

Science Journals Connector (OSTI)

This study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one ...

Haksu Lee; Yu Zhang; Dong-Jun Seo; Robert J. Kuligowski; David Kitzmiller; Robert Corby

2014-06-01T23:59:59.000Z

150

Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning  

Science Journals Connector (OSTI)

Abstract Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO.

Peng Kou; Deliang Liang; Lin Gao; Jianyong Lou

2015-01-01T23:59:59.000Z

151

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect

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

152

Table 14. Coal Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Coal Production, Projected vs. Actual Coal Production, Projected vs. Actual (million short tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 914 939 963 995 1031 1080 AEO 1983 900 926 947 974 1010 1045 1191 AEO 1984 899 921 948 974 1010 1057 1221 AEO 1985 886 909 930 940 958 985 1015 1041 1072 1094 1116 AEO 1986 890 920 954 962 983 1017 1044 1073 1097 1126 1142 1156 1176 1191 1217 AEO 1987 917 914 932 962 978 996 1020 1043 1068 1149 AEO 1989* 941 946 977 990 1018 1039 1058 1082 1084 1107 1130 1152 1171 AEO 1990 973 987 1085 1178 1379 AEO 1991 1035 1002 1016 1031 1043 1054 1065 1079 1096 1111 1133 1142 1160 1193 1234 1272 1309 1349 1386 1433 AEO 1992 1004 1040 1019 1034 1052 1064 1074 1087 1102 1133 1144 1156 1173 1201 1229 1272 1312 1355 1397 AEO 1993 1039 1043 1054 1065 1076 1086 1094 1102 1125 1136 1148 1161 1178 1204 1237 1269 1302 1327 AEO 1994 999 1021

153

Machine learning techniques in disease forecasting: a case study on rice blast prediction  

Science Journals Connector (OSTI)

Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also ... a SVM-based web server f...

Rakesh Kaundal; Amar S Kapoor; Gajendra PS Raghava

2006-11-01T23:59:59.000Z

154

Probability Distributions and Threshold Selection for Monte Carlo–Type Tropical Cyclone Wind Speed Forecasts  

Science Journals Connector (OSTI)

Probabilistic wind speed forecasts for tropical cyclones from Monte Carlo–type simulations are assessed within a theoretical framework for a simple unbiased Gaussian system that is based on feature size and location error that mimic tropical ...

Michael E. Splitt; Steven M. Lazarus; Sarah Collins; Denis N. Botambekov; William P. Roeder

2014-10-01T23:59:59.000Z

155

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network (OSTI)

Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

Poulin, L.

2013-01-01T23:59:59.000Z

156

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts  

Science Journals Connector (OSTI)

Bayesian model averaging (BMA) probability quantitative precipitation forecast (PQPF) models were established by calibrating their parameters using 1–7-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 ...

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

157

Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts  

Science Journals Connector (OSTI)

The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (...

Felix Fundel; Andre Walser; Mark A. Liniger; Christoph Frei; Christof Appenzeller

2010-01-01T23:59:59.000Z

158

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...

159

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...

160

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

Huge market forecast for linear LDPE ... It now appears that the success of the new technology, which rests largely on energy and equipment cost savings, could be overwhelming. ...

1980-08-25T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

1998 With Projections to 2020 1998 With Projections to 2020 Annual Energy Outlook 1999 Report will be Available on December 9, 1998 Preface The Annual Energy Outlook 1998 (AEO98) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO98 reference case. The next section, “Legislation and Regulations,” describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. “Issues in Focus” discusses three current energy issues—electricity restructuring, renewable portfolio standards, and carbon emissions. It is followed by the analysis

162

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

163

MOSE: operational forecast of the optical turbulence and atmospheric parameters at European Southern Observatory ground-based sites – II. Atmospheric parameters in the surface layer 0–30 m  

Science Journals Connector (OSTI)

......filtering of the weak winds) and is always inferior...to the threshold of the wind speed. The improvements...orography of both sites (local peaks surrounded by mountainous...difficulties in forecasting the wind direction in such conditions...another mesocale model, wrf. Indeed, using a nesting......

F. Lascaux; E. Masciadri; L. Fini

2013-01-01T23:59:59.000Z

164

Annual Energy Outlook Forecast Evaluation - Table 1. Forecast Evaluations:  

Gasoline and Diesel Fuel Update (EIA)

Average Absolute Percent Errors from AEO Forecast Evaluations: Average Absolute Percent Errors from AEO Forecast Evaluations: 1996 to 2000 Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Variable 1996 Evaluation: AEO82 to AEO93 1997 Evaluation: AEO82 to AEO97 1998 Evaluation: AEO82 to AEO98 1999 Evaluation: AEO82 to AEO99 2000 Evaluation: AEO82 to AEO2000 Consumption Total Energy Consumption 1.8 1.6 1.7 1.7 1.8 Total Petroleum Consumption 3.2 2.8 2.9 2.8 2.9 Total Natural Gas Consumption 6.0 5.8 5.7 5.6 5.6 Total Coal Consumption 2.9 2.7 3.0 3.2 3.3 Total Electricity Sales 1.8 1.6 1.7 1.8 2.0 Production Crude Oil Production 5.1 4.2 4.3 4.5 4.5

165

Table 23. Energy Intensity, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Energy Intensity, Projected vs. Actual Energy Intensity, Projected vs. Actual (quadrillion Btu / $Billion Nominal GDP) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 20.1 18.5 16.9 15.5 14.4 13.2 AEO 1983 19.9 18.7 17.4 16.2 15.1 14.0 9.5 AEO 1984 20.1 19.0 17.7 16.5 15.5 14.5 10.2 AEO 1985 20.0 19.1 18.0 16.9 15.9 14.7 13.7 12.7 11.8 11.0 10.3 AEO 1986 18.3 17.8 16.8 16.1 15.2 14.3 13.4 12.6 11.7 10.9 10.2 9.5 8.9 8.3 7.8 AEO 1987 17.6 17.0 16.3 15.4 14.5 13.7 12.9 12.1 11.4 8.2 AEO 1989* 16.9 16.2 15.2 14.2 13.3 12.5 11.7 10.9 10.2 9.6 9.0 8.5 8.0 AEO 1990 16.1 15.4 11.7 8.6 6.4 AEO 1991 15.5 14.9 14.2 13.6 13.0 12.5 11.9 11.3 10.8 10.3 9.7 9.2 8.7 8.3 7.9 7.4 7.0 6.7 6.3 6.0 AEO 1992 15.0 14.5 13.9 13.3 12.7 12.1 11.6 11.0 10.5 10.0 9.5 9.0 8.6 8.1 7.7 7.3 6.9 6.6 6.2 AEO 1993 14.7 13.9 13.4 12.8 12.3 11.8 11.2 10.7 10.2 9.6 9.2 8.7 8.3 7.8 7.4 7.1 6.7 6.4

166

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

167

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened at a ...

Jing-Shan Hong; Chin-Tzu Fong; Ling-Feng Hsiao; Yi-Chiang Yu; Chian-You Tzeng

168

Ensemble Kalman Filter Analyses and Forecasts of a Severe Mesoscale Convective System Using Different Choices of Microphysics Schemes  

Science Journals Connector (OSTI)

A Weather Research and Forecasting Model (WRF)-based ensemble data assimilation system is used to produce storm-scale analyses and forecasts of the 4–5 July 2003 severe mesoscale convective system (MCS) over Indiana and Ohio, which produced ...

Dustan M. Wheatley; Nusrat Yussouf; David J. Stensrud

2014-09-01T23:59:59.000Z

169

A hybrid short-term load forecasting with a new data preprocessing framework  

Science Journals Connector (OSTI)

Abstract This paper proposes a hybrid load forecasting framework with a new data preprocessing algorithm to enhance the accuracy of prediction. Bayesian neural network (BNN) is used to predict the load. A discrete wavelet transform (DWT) decomposes the load components into proper levels of resolution determined by an entropy-based criterion. Time series and regression analysis are used to select the best set of inputs among the input candidates. A correlation analysis together with a neural network provides an estimation of the predictions for the forecasting outputs. A standardization procedure is proposed to take into account the correlation estimations of the outputs with their associated input series. The preprocessing algorithm uses the input selection, wavelet decomposition and the proposed standardization to provide the most appropriate inputs for BNNs. Genetic Algorithm (GA) is then used to optimize the weighting coefficients of different forecast components and minimize the forecast error. The performance and accuracy of the proposed short-term load forecasting (STLF) method is evaluated using New England load data. Our results show a significant improvement in the forecast accuracy when compared to the existing state-of-the-art forecasting techniques.

M. Ghayekhloo; M.B. Menhaj; M. Ghofrani

2015-01-01T23:59:59.000Z

170

Forecast of geothermal drilling activity  

SciTech Connect

The numbers of each type of geothermal well expected to be drilled in the United States for each 5-year period to 2000 AD are specified. Forecasts of the growth of geothermally supplied electric power and direct heat uses are presented. The different types of geothermal wells needed to support the forecasted capacity are quantified, including differentiation of the number of wells to be drilled at each major geothermal resource for electric power production. The rate of growth of electric capacity at geothermal resource areas is expected to be 15 to 25% per year (after an initial critical size is reached) until natural or economic limits are approached. Five resource areas in the United States should grow to significant capacity by the end of the century (The Geysers; Imperial Valley; Valles Caldera, NM; Roosevelt Hot Springs, UT; and northern Nevada). About 3800 geothermal wells are expected to be drilled in support of all electric power projects in the United States between 1981 and 2000 AD. Half of the wells are expected to be drilled in the Imperial Valley. The Geysers area is expected to retain most of the drilling activity for the next 5 years. By the 1990's, the Imperial Valley is expected to contain most of the drilling activity.

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

1981-10-01T23:59:59.000Z

171

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

172

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

173

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

174

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network (OSTI)

: Technology Forecast, Laws of Technical systems evolution, Analysis of Contradictions. 1. Introduction Let us: If technology forecasting practice remains at the present level, it is necessary to significantly improve to new demands (like Green House Gases - GHG Effect reduction or covering exploded nuclear reactor

Paris-Sud XI, Université de

175

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

176

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network (OSTI)

. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surfaceAmending Numerical Weather Prediction forecasts using GPS Integrated Water Vapour: a case study to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents

Stoffelen, Ad

177

Agent-Based Alerting for Forecasters  

E-Print Network (OSTI)

­ Temperature ­ Pressure ­ Wind direction ­ Wind speed ­ ... #12;Titan Radar System Provides current;Agents · Agent: software that is autonomous, situated in an environment · An intelligent agent is proactive, reactive, social · Also flexible, robust, rational #12;Agents vs. Objects · Agents are autonomous

Dance, Sandy

178

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network (OSTI)

??The research deals with improved prediction of runway usage for noise forecast. Since the accuracy of the noise forecast depends on the robustness of runway… (more)

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

179

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

Energy Savers (EERE)

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

180

Fundamentals, forecast combinations and nominal exchange-rate predictability  

Science Journals Connector (OSTI)

This paper investigates the out-predictability of fundamentals and forecast combinations. By adopting a panel-based specification, the paper obtains several interesting results. First, the Taylor-rule-based fundamental is the best among the four different fundamentals under consideration in out-of-sample contests. It provides strong evidence to out-predict the random walk over the PBW period. Second, relative to a single-equation prediction, panel predictions are generally able to enhance the statistical significance of beating the random walk. Third, combining forecasts from different fundamentals that have relatively strong out-predictability at a specific horizon does enhance both the statistical and economic significances of beating the random walk for the PBW period at short horizons.

Jyh-Lin Wu; Yi-Chiuan Wang

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

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

SciTech Connect

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

182

Annual Energy Outlook Forecast Evaluation - Tables 2-18  

Gasoline and Diesel Fuel Update (EIA)

Total Energy Consumption: AEO Forecasts, Actual Values, and Total Energy Consumption: AEO Forecasts, Actual Values, and Absolute and Percent Errors, 1985-1999 Publication 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Average Absolute Error (Quadrillion Btu) AEO82 79.1 79.6 79.9 80.8 82.0 83.3 1.8 AEO83 78.0 79.5 81.0 82.4 83.8 84.6 89.5 1.2 AEO84 78.5 79.4 81.2 83.1 85.0 86.4 93.5 1.5 AEO85 77.6 78.5 79.8 81.2 82.6 83.3 84.2 85.2 85.9 86.7 87.7 1.3 AEO86 77.0 78.8 79.8 80.6 81.5 82.9 84.0 84.8 85.7 86.5 87.9 88.4 87.8 88.7 3.6 AEO87 78.9 80.0 81.9 82.8 83.9 85.3 86.4 87.5 88.4 1.5 AEO89 82.2 83.7 84.5 85.4 86.4 87.3 88.2 89.2 90.8 91.4 90.9 91.7 1.8

183

3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information  

Open Energy Info (EERE)

TIER Environmental Forecast Group Inc 3TIER TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name 3TIER Environmental Forecast Group Inc (3TIER) Place Seattle, Washington Zip 98121 Sector Renewable Energy Product Seattle-based, renewable energy assessment and forecasting company. Coordinates 47.60356°, -122.329439° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":47.60356,"lon":-122.329439,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

184

Pose estimation of an uncooperative spacecraft from actual space imagery  

Science Journals Connector (OSTI)

This paper addresses the preliminary design of a spaceborne monocular vision-based navigation system for on-orbit-servicing and formation-flying applications. The aim is to estimate the pose of a passive space resident object using its known three-dimensional model and single low-resolution two-dimensional images collected on-board the active spacecraft. In contrast to previous work, no supportive means are available on the target satellite (e.g., light emitting diodes) and no a-priori knowledge of the relative position and attitude is available (i.e., lost-in-space scenario). Three fundamental mechanisms - perceptual organisation, true perspective projection, and random sample consensus - are exploited to overcome the limitations of monocular passive optical navigation in space. The preliminary design is conducted and validated making use of actual images collected in the frame of the PRISMA mission at about 700 km altitude and 10 m inter-spacecraft separation.

Simone D'Amico; Mathias Benn; John L. Jørgensen

2014-01-01T23:59:59.000Z

185

Forecast Calls for Better Models: Examining the Core  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecast Calls for Better Models: Examining the Core Forecast Calls for Better Models: Examining the Core Components of Arctic Clouds to Clear Their Influence on Climate For original submission and image(s), see ARM Research Highlights http://www.arm.gov/science/highlights/ Research Highlight Predicting how atmospheric aerosols influence cloud formation and the resulting feedback to climate is a challenge that limits the accuracy of atmospheric models. This is especially true in the Arctic, where mixed-phase (both ice- and liquid-based) clouds are frequently observed, but the processes that determine their composition are poorly understood. To obtain a closer look at what makes up Arctic clouds, scientists characterized cloud droplets and ice crystals collected at the North Slope of Alaska as part of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) field study

186

Annual Energy Outlook 2006 with Projections to 2030 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2006 with Projections to 2030 Only GII produces a comprehensive energy projection with a time horizon similar to that of AEO2006. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2006 projections. Economic Growth In the AEO2006 reference case, the projected growth in real GDP, based on 2000 chain-weighted dollars, is 3.0 percent per year from 2004 to 2030 (Table 19). For the period from 2004 to 2025, real GDP growth in the AEO2006 reference case is similar to the average annual growth projected in AEO2005. The AEO2006 projections of economic growth are based on the August short-term forecast of GII, extended by EIA through 2030 and modified to reflect EIAÂ’s view on energy prices, demand, and production.

187

1993 Solid Waste Reference Forecast Summary  

SciTech Connect

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

188

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

SciTech Connect

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

189

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

190

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network (OSTI)

Forecasting 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 study. Key words : wind power forecast, ramps, phase er- rors, forecasts ensemble. 1 Introduction Most

Boyer, Edmond

191

The effect of multinationality on management earnings forecasts  

E-Print Network (OSTI)

and number of countries withforeign subsidiaries) are significantly positively related to more optimistic management earnings forecasts....

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

192

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

SciTech Connect

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

193

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

194

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

195

Improved one day-ahead price forecasting using combined time series and artificial neural network models for the electricity market  

Science Journals Connector (OSTI)

The price forecasts embody crucial information for generators when planning bidding strategies to maximise profits. Therefore, generation companies need accurate price forecasting tools. Comparison of neural network and auto regressive integrated moving average (ARIMA) models to forecast commodity prices in previous researches showed that the artificial neural network (ANN) forecasts were considerably more accurate than traditional ARIMA models. This paper provides an accurate and efficient tool for short-term price forecasting based on the combination of ANN and ARIMA. Firstly, input variables for ANN are determined by time series analysis. This model relates the current prices to the values of past prices. Secondly, ANN is used for one day-ahead price forecasting. A three-layered feed-forward neural network algorithm is used for forecasting next-day electricity prices. The ANN model is then trained and tested using data from electricity market of Iran. According to previous studies, in the case of neural networks and ARIMA models, historical demand data do not significantly improve predictions. The results show that the combined ANNâ??ARIMA forecasts prices with high accuracy for short-term periods. Also, it is shown that policy-making strategies would be enhanced due to increased precision and reliability.

Ali Azadeh; Seyed Farid Ghaderi; Behnaz Pourvalikhan Nokhandan; Shima Nassiri

2011-01-01T23:59:59.000Z

196

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

2004 2004 * The Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) has produced annual evaluations of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and replacing the historical year of data with the most recent. The forecast evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute percent errors for several of the major variables for AEO82 through AEO2004. (There is no report titled Annual Energy Outlook 1988 due to a change in the naming convention of the AEOs.) The average absolute percent error is the simple mean of the absolute values of the percentage difference between the Reference Case projection and the

197

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Economic Growth World Oil Prices Total Energy Consumption Residential and Commercial Sectors Industrial Sector Transportation Sector Electricity Natural Gas Petroleum Coal Three other organizations—Standard & Poor’s DRI (DRI), the WEFA Group (WEFA), and the Gas Research Institute (GRI) [95]—also produce comprehensive energy projections with a time horizon similar to that of AEO2001. The most recent projections from those organizations (DRI, Spring/Summer 2000; WEFA, 1st Quarter 2000; GRI, January 2000), as well as other forecasts that concentrate on petroleum, natural gas, and international oil markets, are compared here with the AEO2001 projections. Economic Growth Differences in long-run economic forecasts can be traced primarily to

198

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

199

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

This paper addresses hierarchical forecasting in a production planning environment. Specifically, we examine the relative effectiveness of Top-Down (TD) and Bottom-Up (BU) strategies for forecasting the demand for a substitutable product (which belongs to a family) as well as the demand for the product family under different types of family demand processes. Through a simulation study, it is revealed that the TD strategy consistently outperforms the BU strategy for forecasting product family demand. The relative superiority of the TD strategy further improves by as much as 52% as the product demand variability increases and the degree of substitutability between the products decreases. This phenomenon, however, is not always true for forecasting the demand for the products within the family. In this case, it is found that there are a few situations wherein the BU strategy marginally outperforms the TD strategy, especially when the product demand variability is high and the degree of product substitutability is low.

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

200

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network (OSTI)

Testing Competing High-Resolution Precipitation Forecasts Eric Gilleland Research Prediction Comparison Test D1 D2 D = D1 ­ D2 copyright NCAR 2013 Loss Differential Field #12;Spatial Prediction Comparison Test Introduced by Hering and Genton

Gilleland, Eric

Note: This page contains sample records for the topic "actual base forecast" 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

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

The short-term forecasting of capital expenditure presents one of the most difficult problems ... reason is that year-to-year fluctuations in capital expenditure are extremely wide. Some simple methods which...

W. Gerstenberger

1977-01-01T23:59:59.000Z

202

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

...of each variable on GDP (13, 17), combined with global GDP projections (14...population, and per capita GDP, combined with projected...measure of agricultural demand for water, is forecast...Just as demand for energy is the major cause...

David Tilman; Joseph Fargione; Brian Wolff; Carla D'Antonio; Andrew Dobson; Robert Howarth; David Schindler; William H. Schlesinger; Daniel Simberloff; Deborah Swackhamer

2001-04-13T23:59:59.000Z

203

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

In contrast to short and extended range forecasts, predictions for periods beyond 5 days use time-averaged, midtropospheric height fields as their primary guidance. As time ranges are increased to 3O- and 90-day outlooks, guidance increasingly ...

A. James Wagner

1989-09-01T23:59:59.000Z

204

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

Certain satellite interpretation techniques have proven quite useful in the heavy snow forecast process. Those considered best are briefly reviewed, and another technique is introduced. This new technique was found to be most valuable in cyclonic ...

Edward C. Johnston

1995-06-01T23:59:59.000Z

205

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

Forecast Evaluation 2005 Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 * Then Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose or advocate future legislative and regulatory changes. All laws are assumed to remain as currently enacted; however, the impacts of emerging regulatory changes, when defined, are reflected.

206

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

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

January 23, 2008" ,"Next Update: October 2007" ,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected by North American Electric Reliability Council Region, " ,"2005...

207

Short-term wind forecast for the safety management of complex areas during hazardous wind events  

Science Journals Connector (OSTI)

Abstract This paper describes the short-term wind forecast system realised in the framework of the European Project “Wind and Ports: The forecast of wind for the management and safety of port areas”. The project?s aim is to contribute improving the safety and accessibility to the harbour areas of the largest ports in the Northern Tyrrhenian Sea, which are frequently exposed to hazardous winds, in order to minimise the risks for users, structures, transport means, stored goods and boats within the ports. The short-term wind forecast system is based on a mixed statistical-numerical procedure, trained by means of local wind measurements and implemented into an operational chain for the real-time prediction of the maximum expected wind velocity corresponding to three forecast horizons (30, 60 and 90 min) and three non-exceeding probabilities (90%, 95%, and 99%). The local wind measurements used to train the forecast algorithms have been recorded from the 15 ultra-sonic anemometers installed in the Ports of Savona, La Spezia, and Livorno. This wind-monitoring network is used also to carry out the short-term forecast system a posteriori verification and validation.

M. Burlando; M. Pizzo; M.P. Repetto; G. Solari; P. De Gaetano; M. Tizzi

2014-01-01T23:59:59.000Z

208

Evolving an Information Diffusion Model Using a Genetic Algorithm for Monthly River Discharge Time Series Interpolation and Forecasting  

Science Journals Connector (OSTI)

The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (...

Chengzu Bai; Mei Hong; Dong Wang; Ren Zhang; Longxia Qian

2014-12-01T23:59:59.000Z

209

Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE  

Science Journals Connector (OSTI)

Statistical postprocessing is an integral part of an ensemble prediction system. This study compares methods used to derive probabilistic quantitative precipitation forecasts based on the high-resolution version of the German-focused Consortium ...

Sabrina Bentzien; Petra Friederichs

2012-08-01T23:59:59.000Z

210

Shale Gas Production: Potential versus Actual GHG Emissions  

E-Print Network (OSTI)

Shale Gas Production: Potential versus Actual GHG Emissions Francis O'Sullivan and Sergey Paltsev://globalchange.mit.edu/ Printed on recycled paper #12;1 Shale Gas Production: Potential versus Actual GHG Emissions Francis O'Sullivan* and Sergey Paltsev* Abstract Estimates of greenhouse gas (GHG) emissions from shale gas production and use

211

Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting  

Science Journals Connector (OSTI)

......optimum value through a grid-search algorithm...method outperformed TD for estimating the aggregate data series...variable, there is no benefit of forecasting each subaggregate...forecasting strategies in estimating the `component'-level...WILLEMAIN, T. R., SMART, C. N., SHOCKOR......

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

212

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

SciTech Connect

This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

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

1993-12-01T23:59:59.000Z

213

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability?A Review  

Science Journals Connector (OSTI)

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

R. M. Holmukhe; Mrs. Sunita Dhumale; Mr. P. S. Chaudhari; Mr. P. P. Kulkarni

2010-01-01T23:59:59.000Z

214

Point-trained models in a grid environment: Transforming a potato late blight risk forecast for use with the National Digital Forecast Database  

E-Print Network (OSTI)

Point-trained models in a grid environment: Transforming a potato late blight risk forecast for use have come to expect. Potato late blight risk models were some of the earliest weather-based models. This analysis compares two types of potato late blight risk models that were originally trained on location

Douches, David S.

215

1360 IEEE Transactions on Power Systems, Vol. 12, No. 3, August 1997 Application of Fuzzy Logic Technology for Spatial Load Forecasting  

E-Print Network (OSTI)

of historical distribution load data [2]. The increasinglypopular, accurate, and affordable Geographic Informahon Systems (GIS) technology provides an excellent data base platform for spatial load forecasting on collecting relevant geographic data. Thus spatial load forecasting becomes even more attractive than before

Chow, Mo-Yuen

216

Data transforms with exponential smoothing methods of forecasting  

Science Journals Connector (OSTI)

Abstract In this paper, transforms are used with exponential smoothing, in the quest for better forecasts. Two types of transforms are explored: those which are applied to a time series directly, and those which are applied indirectly to the prediction errors. The various transforms are tested on a large number of time series from the M3 competition, and ANOVA is applied to the results. We find that the non-transformed time series is significantly worse than some transforms on the monthly data, and on a distribution-based performance measure for both annual and quarterly data.

Adrian N. Beaumont

2014-01-01T23:59:59.000Z

217

Radar-Derived Forecasts of Cloud-to-Ground Lightning Over Houston, Texas  

E-Print Network (OSTI)

Lightning Forecasts..........................................................................................45 2.7 First Flash Forecasts and Lead Times.....................................................................47 vii... Cell Number ? 25 August 2000..............................................68 3.4 First Flash Forecast Time........................................................................................70 3.5 Lightning Forecasting Algorithm (LFA) Development...

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

218

Estimation of Regional Actual Evapotranspiration in the Panama Canal Watershed  

Science Journals Connector (OSTI)

The upper Río Chagres basin is a part of the Panama Canal Watershed. The least known water balance...SEBAL...). We use an image from March 27, 2000, for estimation of the distribution of the regional actual evapo...

Jan M.H. Hendrickx; Wim G.M. Bastiaanssen; Edwin J.M. Noordman…

2005-01-01T23:59:59.000Z

219

12-32021E2_Forecast  

NLE Websites -- All DOE Office Websites (Extended Search)

FORECAST OF VACANCIES FORECAST OF VACANCIES Until end of 2014 (Issue No. 20) Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff at the P4-P5 levels: * Advanced university degree (or equivalent postgraduate degree); * 7 or 10 years, respectively, of experience in a field of relevance to the post; * Resource management experience; * Strong analytical skills; * Computer skills: standard Microsoft Office software; * Languages: Fluency in English. Working knowledge of other official languages (Arabic, Chinese, French, Russian, Spanish) advantageous; * Ability to work effectively in multidisciplinary and multicultural teams; * Ability to communicate effectively. Professional staff at the P1-P3 levels:

220

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network (OSTI)

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse three decades of experience working with telecommunications operators around the world we seek

McBurney, Peter

Note: This page contains sample records for the topic "actual base forecast" 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

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

Managing water resources generally and managing reservoir operations specifically have been touted as opportunities for applying forecasts to improve decision making. Previous studies have shown that the application of forecasts into water ...

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

222

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

An integrated hydrometeorological system was designed for the utilization of data from various sensors in the 3300 km2 Panama Canal Watershed for the purpose of producing ... forecasts. These forecasts are used b...

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

223

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network (OSTI)

Forecasts Using NEMS and GIS National Climatic Data Center.with Changing Boundaries." Use of GIS to Understand Socio-Forecasts Using NEMS and GIS Appendix A. Map Results Gallery

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

2005-01-01T23:59:59.000Z

224

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

Energy Savers (EERE)

Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar...

225

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

This paper presents the National Energy Board’s long term energy demand forecasting model in its present state of ... results of recent research at the NEB. Energy demand forecasts developed with the aid of this....

R. A. Preece; L. B. Harsanyi; H. M. Webster

1980-01-01T23:59:59.000Z

226

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

Demand side energy management has become an important issue for energy management. In order to support energy planning and policy decisions forecasting the future demand is very important. Thus, forecasting the f...

?Irem Uçal Sar?; Ba¸sar Öztay¸si

2012-01-01T23:59:59.000Z

227

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Oil Markets Oil Markets IEO2005 projects that world crude oil prices in real 2003 dollars will decline from their current level by 2010, then rise gradually through 2025. In the International Energy Outlook 2005 (IEO2005) reference case, world demand for crude oil grows from 78 million barrels per day in 2002 to 103 million barrels per day in 2015 and to just over 119 million barrels per day in 2025. Much of the growth in oil consumption is projected for the emerging Asian nations, where strong economic growth results in a robust increase in oil demand. Emerging Asia (including China and India) accounts for 45 percent of the total world increase in oil use over the forecast period in the IEO2005 reference case. The projected increase in world oil demand would require an increment to world production capability of more than 42 million barrels per day relative to the 2002 crude oil production capacity of 80.0 million barrels per day. Producers in the Organization of Petroleum Exporting Countries (OPEC) are expected to be the major source of production increases. In addition, non-OPEC supply is expected to remain highly competitive, with major increments to supply coming from offshore resources, especially in the Caspian Basin, Latin America, and deepwater West Africa. The estimates of incremental production are based on current proved reserves and a country-by-country assessment of ultimately recoverable petroleum. In the IEO2005 oil price cases, the substantial investment capital required to produce the incremental volumes is assumed to exist, and the investors are expected to receive at least a 10-percent return on investment.

228

Wind power forecasting in U.S. electricity markets.  

SciTech Connect

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

229

Wind power forecasting in U.S. Electricity markets  

SciTech Connect

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

230

Sandia National Laboratories: Solar Energy Forecasting and Resource...  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

231

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

Logistics parks’ demand is an important basis of establishing the development policy of logistics industry and logistics infrastructure for planning. In order to improve the forecast accuracy of logistics parks’ demand, a combination forecasting ... Keywords: Logistics parks' demand, combine, simulated annealing algorithm, grey forecast model, exponential smoothing method

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

232

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network (OSTI)

in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

Boyer, Edmond

233

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

234

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

235

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data in California and for climate zones within those areas. The staff California Energy Demand 2008-2018 forecast

236

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.

237

Impact of vegetation fraction from Indian geostationary satellite on short-range weather forecast  

Science Journals Connector (OSTI)

Indian economy is largely depending upon the agricultural productivity and thus influences the trade among the SAARC countries. High-resolution and good-quality regional weather forecasts are necessary for planners, resource managers, insurers and national agro-advisory services. In this study, high resolution updated land-surface state in terms of vegetation fraction (VF) from operational vegetation index products of Indian geostationary satellite (INSAT 3A) sensor (CCD) was utilized in numerical weather prediction (NWP) model (e.g. WRF) to investigate its impact on short-range weather forecast over the control run. Results showed that the updated vegetation fraction from INSAT 3A CCD improved the low-level 24 h temperature (?18%) and moisture (?10%) forecast in comparison to control run. The 24 h rainfall forecast was also improved (more than 5%) over central and southern India with the use of updated vegetation fraction compared to control experiment. INSAT 3A VF based experiment also showed a net improvement of 27% in surface sensible heat fluxes from WRF in comparison to control experiment when both were compared with area-averaged measurements from Large Aperture Scintillometer (LAS). This triggers the need of more and more use of realistic and updated land surface states through satellite remote sensing data as well as in situ micrometeorological measurements to improve the forecast quality, skill and consistency.

Prashant Kumar; Bimal K. Bhattacharya; P.K. Pal

2013-01-01T23:59:59.000Z

238

Application of a statistical post-processing technique to a gridded, operational, air quality forecast  

Science Journals Connector (OSTI)

Abstract An automated air quality forecast bias correction scheme based on the short-term persistence of model bias with respect to recent observations is described. The scheme has been implemented in the operational Met Office five day regional air quality forecast for the UK. It has been evaluated against routine hourly pollution observations for a year-long hindcast. The results demonstrate the value of the scheme in improving performance. For the first day of the forecast the post-processing reduces the bias from 7.02 to 0.53 ?g m?3 for O3, from ?4.70 to ?0.63 ?g m?3 for NO2, from ?4.00 to ?0.13 ?g m?3 for PM2.5 and from ?7.70 to ?0.25 ?g m?3 for PM10. Other metrics also improve for all species. An analysis of the variation of forecast skill with lead-time is presented and demonstrates that the post-processing increases forecast skill out to five days ahead.

L.S. Neal; P. Agnew; S. Moseley; C. Ordóñez; N.H. Savage; M. Tilbee

2014-01-01T23:59:59.000Z

239

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

E-Print Network (OSTI)

An important determinant of our energy future is the rate at which energy conservation technologies, once developed, are put into use. At Synergic Resources Corporation, we have adapted and applied a methodology to forecast the use of conservation...

Lang, K.

1982-01-01T23:59:59.000Z

240

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network (OSTI)

Forecasting the Locational Dynamics of Transnational Terrorism: A Network Analytic Approach Bruce A-0406 Fax: (919) 962-0432 Email: skyler@unc.edu Abstract--Efforts to combat and prevent transnational terror of terrorism. We construct the network of transnational terrorist attacks, in which source (sender) and target

Massachusetts at Amherst, University of

Note: This page contains sample records for the topic "actual base forecast" 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

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

242

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

... For decades, companies have tailored public weather data for private customers from farmers to airlines. On Wednesday, a group of businesses said that they ... utilities and satellite operators. But Terry Onsager, a physicist at the SWPC, says that private forecasting firms are starting to realize that they can add value to these predictions. ...

Eric Hand

2012-04-27T23:59:59.000Z

243

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

244

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.

245

Customized forecasting tool improves reserves estimation  

SciTech Connect

Unique producing characteristics of the Teapot sandstone formation, Powder River basin, Wyoming, necessitated the creation of individualized production forecasting methods for wells producing from this reservoir. The development and use of a set of production type curves and correlations for Teapot wells are described herein.

Mian, M.A.

1986-04-01T23:59:59.000Z

246

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

...But the WRF has no immediate...being tuned to local conditions...temperatures and winds with altitude...resulting WRF forecasts...captured the local sea-breeze winds better...spread the local operation of mesoscale...to be the WRF model now...

Richard A. Kerr

2004-05-14T23:59:59.000Z

247

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

248

Online short-term solar power forecasting  

SciTech Connect

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

249

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting  

Science Journals Connector (OSTI)

Abstract Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily.

Zhongyue Su; Jianzhou Wang; Haiyan Lu; Ge Zhao

2014-01-01T23:59:59.000Z

250

EVALUATION OF PV GENERATION CAPICITY CREDIT FORECAST ON DAY-AHEAD UTILITY MARKETS  

E-Print Network (OSTI)

City, and Sacramento Municipal Utility District, in California 1. BACKGROUND The effective capacity comfortable if capacity could be ascertained operationally by knowing in advance what the output of solar of the NDFD-based solar radiation forecasts for several climatically distinct locations, the evaluation is now

Perez, Richard R.

251

ENVIRONMENTAL INFORMATION SYSTEM FOR ANALYSIS AND FORECAST OF AIR POLLUTION (APPLICATION TO SANTIAGO DE CHILE)  

E-Print Network (OSTI)

ENVIRONMENTAL INFORMATION SYSTEM FOR ANALYSIS AND FORECAST OF AIR POLLUTION (APPLICATION Chile and other cities in Chile, air pollution is a dramatic problem. An Environmental Information planning. Using a model-based EIS for air pollution it is possible (i) to study complex source

Bertossi, Leopoldo

252

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect

We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

253

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect

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

254

Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting  

Science Journals Connector (OSTI)

Abstract Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 2010–2030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967–2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010–2030.

F.J. Ardakani; M.M. Ardehali

2014-01-01T23:59:59.000Z

255

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

ANL Wind Power Forecasting and Electricity Markets ANL Wind Power Forecasting and Electricity Markets Jump to: navigation, search Logo: Wind Power Forecasting and Electricity Markets Name Wind Power Forecasting and Electricity Markets Agency/Company /Organization Argonne National Laboratory Partner Institute for Systems and Computer Engineering of Porto (INESC Porto) in Portugal, Midwest Independent System Operator and Horizon Wind Energy LLC, funded by U.S. Department of Energy Sector Energy Focus Area Wind Topics Pathways analysis, Technology characterizations Resource Type Software/modeling tools Website http://www.dis.anl.gov/project References Argonne National Laboratory: Wind Power Forecasting and Electricity Markets[1] Abstract To improve wind power forecasting and its use in power system and electricity market operations Argonne National Laboratory has assembled a team of experts in wind power forecasting, electricity market modeling, wind farm development, and power system operations.

256

Self-actualization as it relates to aerobic physical fitness  

E-Print Network (OSTI)

higher than the aerobic and archery group on the TC, Ex, and C scales. The archery group was significantly higher than the preaerobic and aerobic groups on the Fr and S scales. Females from the preaerobic group were significantly lower than archery... Inventory Sav Self-actualization values measures how well a person holds and lives by values of se 1f- ac tualizing people Ex Existentiality measures ability to flexibly apply self-actualizing values to one's own life Fr Feeling reactivity measures...

Russell, Kathryn Terese Vecchio

2012-06-07T23:59:59.000Z

257

experiment actually sees," Smith says. "When we were  

E-Print Network (OSTI)

experiment actually sees," Smith says. "When we were finished, we got much more ­ a method in science depend on atoms and molecules moving," Smith says. "We want to create movies of molecules science development," Smith says.--Morgan McCorkle A theoretical technique developed at ORNL is bringing

Pennycook, Steve

258

COORDINATING ADVICE AND ACTUAL TREATMENT Thomas A. Russ  

E-Print Network (OSTI)

. Unfortunately, this information is not always immediately available. For example, the exact fluid infused via an intravenous line can only be determined after someone checks the infusion bottle to determine how much fluid differ in timing and exact amount from what is actually done. For example, an infusion order might call

Russ, Thomas A.

259

Voluntary Green Power Market Forecast through 2015  

NLE Websites -- All DOE Office Websites (Extended Search)

158 158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 * www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Operated by the Alliance for Sustainable Energy, LLC Contract No. DE-AC36-08-GO28308 Technical Report NREL/TP-6A2-48158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory

260

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Highlights Highlights World energy consumption is projected to increase by 57 percent from 2002 to 2025. Much of the growth in worldwide energy use in the IEO2005 reference case forecast is expected in the countries with emerging economies. Figure 1. World Marketed Energy Consumptiion by Region, 1970-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data In the International Energy Outlook 2005 (IEO2005) reference case, world marketed energy consumption is projected to increase on average by 2.0 percent per year over the 23-year forecast horizon from 2002 to 2025—slightly lower than the 2.2-percent average annual growth rate from 1970 to 2002. Worldwide, total energy use is projected to grow from 412 quadrillion British thermal units (Btu) in 2002 to 553 quadrillion Btu in

Note: This page contains sample records for the topic "actual base forecast" 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

Steam System Forecasting and Management  

E-Print Network (OSTI)

by manipulation of operating schedules to avoid steam balances that result in steam venting, off gas-flaring, excessive condensing on extraction/condensing turbines, and ineffective use of extraction turbines. For example, during the fourth quarter of 1981... minimum turndown levels. Several boilers would have oeen shut down; by-product fuel gas would have been flared; and surplus low level steam would have been vented to the atmosphere. Several scenarios were studied with SFC and evaluated based...

Mongrue, D. M.; Wittke, D. O.

1982-01-01T23:59:59.000Z

262

Depositional sequences and integrated recovery efficiency forecast models for San Andres and Clearfork Units in the Central Basin Platform and the Northern Shelf, west Texas  

E-Print Network (OSTI)

This paper develops depositional sequences of the carbonate ramp and the carbonate shelf models for an idealized cycle and multiple cycles of depositions. Based on the developed depositional sequences, the integrated recovery efficiency forecast...

Shao, Hongbin

2012-06-07T23:59:59.000Z

263

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

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather 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 power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

264

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2004 with Projections to 2025 Forecast Comparisons Index (click to jump links) Economic Growth World Oil Prices Total Energy Consumption Electricity Natural Gas Petroleum Coal The AEO2004 forecast period extends through 2025. One other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a similar time horizon. Several others provide forecasts that address one or more aspects of energy markets over different time horizons. Recent projections from GII and others are compared here with the AEO2004 projections. Economic Growth Printer Friendly Version Average annual percentage growth Forecast 2002-2008 2002-2013 2002-2025 AEO2003 3.2 3.3 3.1 AEO2004 Reference 3.3 3.2 3.0

265

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

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather 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 power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

266

Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Final July 01, 2010 Final July 01, 2010 1 Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance On Real Property 1. The following is the FY 2010 implementation procedures for the field offices/sites to determine and report deferred maintenance on real property as required by the Statement of Federal Financial Accounting Standards (SFFAS) No. 6, Accounting for Property, Plant, and Equipment (PP&E) and DOE Order 430.1B, Real Property Asset Management (RPAM). a. This document is intended to assist field offices/sites in consistently and accurately applying the appropriate methods to determine and report deferred maintenance estimates and reporting of annual required and actual maintenance costs. b. This reporting satisfies the Department's obligation to recognize and record deferred

267

Table 5. Domestic Crude Oil Production, Projected vs. Actual  

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

Domestic Crude Oil Production, Projected vs. Actual Domestic Crude Oil Production, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 2508 2373 2256 2161 2088 2022 1953 1891 1851 1825 1799 1781 1767 1759 1778 1789 1807 1862 AEO 1995 2402 2307 2205 2095 2037 1967 1953 1924 1916 1905 1894 1883 1887 1887 1920 1945 1967 AEO 1996 2387 2310 2248 2172 2113 2062 2011 1978 1953 1938 1916 1920 1927 1949 1971 1986 2000 AEO 1997 2362 2307 2245 2197 2143 2091 2055 2033 2015 2004 1997 1989 1982 1975 1967 1949 AEO 1998 2340 2332 2291 2252 2220 2192 2169 2145 2125 2104 2087 2068 2050 2033 2016 AEO 1999 2340 2309 2296 2265 2207 2171 2141 2122 2114 2092 2074 2057 2040 2025 AEO 2000 2193 2181 2122 2063 2016 1980 1957 1939 1920 1904 1894 1889 1889

268

Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Draft July 9, 2009 Draft July 9, 2009 1 Attachment Implementation Procedures to Report Deferred, Actual, and Required Maintenance On Real Property 1. The following is the FY 2009 implementation procedures for the field offices/sites to determine and report deferred maintenance on real property as required by the Statement of Federal Financial Accounting Standards (SFFAS) No. 6, Accounting for Property, Plant, and Equipment (PP&E) and DOE Order 430.1B, Real Property Asset Management (RPAM). a. This document is intended to assist field offices/sites in consistently and accurately applying the appropriate methods to determine and report deferred maintenance estimates and reporting of annual required and actual maintenance costs. b. This reporting satisfies the Department's obligation to recognize and record deferred

269

Table 12. Total Coal Consumption, Projected vs. Actual  

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

Coal Consumption, Projected vs. Actual" Coal Consumption, Projected vs. Actual" "Projected" " (million short tons)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",920,928,933,938,943,948,953,958,962,967,978,990,987,992,1006,1035,1061,1079 "AEO 1995",,935,940,941,947,948,951,954,958,963,971,984,992,996,1002,1013,1025,1039 "AEO 1996",,,937,942,954,962,983,990,1004,1017,1027,1033,1046,1067,1070,1071,1074,1082,1087 "AEO 1997",,,,948,970,987,1003,1017,1020,1025,1034,1041,1054,1075,1086,1092,1092,1099,1104 "AEO 1998",,,,,1009,1051,1043.875977,1058.292725,1086.598145,1084.446655,1089.787109,1096.931763,1111.523926,1129.833862,1142.338257,1148.019409,1159.695312,1162.210815,1180.029785

270

Table 4. Total Petroleum Consumption, Projected vs. Actual  

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

Petroleum Consumption, Projected vs. Actual Petroleum Consumption, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 6450 6566 6643 6723 6811 6880 6957 7059 7125 7205 7296 7377 7446 7523 7596 7665 7712 7775 AEO 1995 6398 6544 6555 6676 6745 6822 6888 6964 7048 7147 7245 7337 7406 7472 7537 7581 7621 AEO 1996 6490 6526 6607 6709 6782 6855 6942 7008 7085 7176 7260 7329 7384 7450 7501 7545 7581 AEO 1997 6636 6694 6826 6953 7074 7183 7267 7369 7461 7548 7643 7731 7793 7833 7884 7924 AEO 1998 6895 6906 7066 7161 7278 7400 7488 7597 7719 7859 7959 8074 8190 8286 8361 AEO 1999 6884 7007 7269 7383 7472 7539 7620 7725 7841 7949 8069 8174 8283 8351 AEO 2000 7056 7141 7266 7363 7452 7578 7694 7815 7926 8028 8113 8217 8288

271

Table 6. Petroleum Net Imports, Projected vs. Actual Projected  

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

Petroleum Net Imports, Projected vs. Actual Petroleum Net Imports, Projected vs. Actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 2935 3201 3362 3504 3657 3738 3880 3993 4099 4212 4303 4398 4475 4541 4584 4639 4668 4672 AEO 1995 2953 3157 3281 3489 3610 3741 3818 3920 4000 4103 4208 4303 4362 4420 4442 4460 4460 AEO 1996 3011 3106 3219 3398 3519 3679 3807 3891 3979 4070 4165 4212 4260 4289 4303 4322 4325 AEO 1997 3099 3245 3497 3665 3825 3975 4084 4190 4285 4380 4464 4552 4617 4654 4709 4760 AEO 1998 3303 3391 3654 3713 3876 4053 4137 4298 4415 4556 4639 4750 4910 4992 5087 AEO 1999 3380 3442 3888 4022 4153 4238 4336 4441 4545 4652 4780 4888 4999 5073 AEO 2000 3599 3847 4036 4187 4320 4465 4579 4690 4780 4882 4968 5055 5113

272

Tropical Africa: Calculated Actual Aboveground Live Biomass in Open and  

NLE Websites -- All DOE Office Websites (Extended Search)

Calculated Actual Aboveground Live Biomass in Open and Calculated Actual Aboveground Live Biomass in Open and Closed Forests (1980) image Brown, S., and G. Gaston. 1996. Tropical Africa: Land Use, Biomass, and Carbon Estimates For 1980. ORNL/CDIAC-92, NDP-055. Carbon Dioxide Information Analysis Center, U.S. Department of Energy, Oak Ridge National Laboratory, Oak Ridge, Tennessee, U.S.A. More Maps Land Use Maximum Potential Biomass Density Area of Closed Forests (By Country) Mean Biomass of Closed Forests (By Country) Area of Open Forests (By Country) Mean Biomass of Open Forests (By County) Percent Forest Cover (By Country) Total Forest Biomass (By Country) Population Density - 1990 (By Administrative Unit) Population Density - 1980 (By Administrative Unit) Population Density - 1970 (By Administrative Unit) Population Density - 1960 (By Administrative Unit)

273

Table 7b. Natural Gas Wellhead Prices, Projected vs. Actual  

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

b. Natural Gas Wellhead Prices, Projected vs. Actual" b. Natural Gas Wellhead Prices, Projected vs. Actual" "Projected Price in Nominal Dollars" " (nominal dollars per thousand cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",1.983258692,2.124739238,2.26534793,2.409252566,2.585728477,2.727400662,2.854942053,2.980927152,3.13861755,3.345819536,3.591100993,3.849544702,4.184279801,4.510016556,4.915074503,5.29147351,5.56022351,5.960471854 "AEO 1995",,1.891706924,1.998384058,1.952818035,2.064227053,2.152302174,2.400016103,2.569033816,2.897681159,3.160088567,3.556344605,3.869033816,4.267391304,4.561932367,4.848599034,5.157246377,5.413405797,5.660917874 "AEO 1996",,,1.630674532,1.740334763,1.862956911,1.9915856,2.10351261,2.194934146,2.287655669,2.378991658,2.476043002,2.589847464,2.717610782,2.836870306,2.967124845,3.117719429,3.294003735,3.485657428,3.728419409

274

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

275

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect

The history of the petrochemical industry over the past ten years clearly shows that forecasting in a turbulent world is like trying to predict tomorrow's headlines.

Struth, B.W.

1984-08-01T23:59:59.000Z

276

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 pollution, greenhouse gas emissions, and soil erosion (Nash, 2007; Searchinger et al., 2008). On the other

Gratton, Claudio

277

Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030  

Science Journals Connector (OSTI)

Natural gas is the primary source for electricity production in Turkey. However, Turkey does not have indigenous resources and imports more than 98.0% of the natural gas it consumes. In 2011, more than 20.0% of Turkey's annual trade deficit was due to imported natural gas, estimated at US$ 20.0 billion. Turkish government has very ambitious targets for the country's energy sector in the next decade according to the Vision 2023 agenda. Previously, we have estimated that Turkey's annual electricity demand would be 530,000 GWh at the year 2023. Considering current energy market dynamics it is almost evident that a substantial amount of this demand would be supplied from natural gas. However, meticulous analysis of the Vision 2023 goals clearly showed that the information about the natural gas sector is scarce. Most importantly there is no demand forecast for natural gas in the Vision 2023 agenda. Therefore, in this study the aim was to generate accurate forecasts for Turkey's natural gas demand between 2013 and 2030. For this purpose, two semi-empirical models based on econometrics, gross domestic product (GDP) at purchasing power parity (PPP) per capita, and demographics, population change, were developed. The logistic equation, which can be used for long term natural gas demand forecasting, and the linear equation, which can be used for medium term demand forecasting, fitted to the timeline series almost seamlessly. In addition, these two models provided reasonable fits according to the mean absolute percentage error, MAPE %, criteria. Turkey's natural gas demand at the year 2030 was calculated as 76.8 billion m3 using the linear model and 83.8 billion m3 based on the logistic model. Consequently, found to be in better agreement with the official Turkish petroleum pipeline corporation (BOTAS) forecast, 76.4 billion m3, than results published in the literature.

Mehmet Melikoglu

2013-01-01T23:59:59.000Z

278

Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables  

Science Journals Connector (OSTI)

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios were developed by either assigning arbitrary values or assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and inputted to the scenario set. This article focuses on the long-term forecasting of electricity demand using autoregressive, simple linear and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario's electricity demand as a case study, the annual energy, peak load and base load demand were forecasted up to the year 2025. In order to generate different scenarios, different ranges in the economic, demographic and climatic variables were used. [Received 16 October 2007; Revised 31 May 2008; Revised 25 October 2008; Accepted 1 November 2008

F. Chui; A. Elkamel; R. Surit; E. Croiset; P.L. Douglas

2009-01-01T23:59:59.000Z

279

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

Exponential smoothing, often used in sales forecasting for inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains appropriate under more general conditions, where the variance is allowed to grow or contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular, the problem of finding the predictive distribution of aggregate lead-time demand, for use in inventory control calculations, is considered using a bootstrap approach. A method for establishing order-up-to levels directly from the simulated predictive distribution is also explored.

Ralph D. Snyder; Anne B. Koehler; J.Keith Ord

2002-01-01T23:59:59.000Z

280

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

Probabilistic forecasts of tropical cyclogenesis have been evaluated for two samples: a near-homogeneous sample of ECMWF and Weather Research and Forecasting (WRF) Model–ensemble Kalman filter (EnKF) ensemble forecasts during the National Science ...

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

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281

Actual and Estimated Energy Savings Comparison for Deep Energy Retrofits in the Pacific Northwest  

SciTech Connect

Seven homes from the Pacific Northwest were selected to evaluate the differences between estimated and actual energy savings achieved from deep energy retrofits. The energy savings resulting from these retrofits were estimated, using energy modeling software, to save at least 30% on a whole-house basis. The modeled pre-retrofit energy use was trued against monthly utility bills. After the retrofits were completed, each of the homes was extensively monitored, with the exception of one home which was monitored pre-retrofit. This work is being conducted by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy Building Technologies Program as part of the Building America Program. This work found many discrepancies between actual and estimated energy savings and identified the potential causes for the discrepancies. The differences between actual energy use and modeled energy use also suggest improvements to improve model accuracy. The difference between monthly whole-house actual and estimated energy savings ranged from 75% more energy saved than predicted by the model to 16% less energy saved for all the monitored homes. Similarly, the annual energy savings difference was between 36% and -14%, which was estimated based on existing monitored savings because an entire year of data is not available. Thus, on average, for all six monitored homes the actual energy use is consistently less than estimates, indicating home owners are saving more energy than estimated. The average estimated savings for the eight month monitoring period is 43%, compared to an estimated savings average of 31%. Though this average difference is only 12%, the range of inaccuracies found for specific end-uses is far greater and are the values used to directly estimate energy savings from specific retrofits. Specifically, the monthly post-retrofit energy use differences for specific end-uses (i.e., heating, cooling, hot water, appliances, etc.) ranged from 131% under-predicted to 77% over-predicted by the model with respect to monitored energy use. Many of the discrepancies were associated with occupant behavior which influences energy use, dramatically in some cases, actual versus modeled weather differences, modeling input limitations, and complex homes that are difficult to model. The discrepancy between actual and estimated energy use indicates a need for better modeling tools and assumptions. Despite the best efforts of researchers, the estimated energy savings are too inaccurate to determine reliable paybacks for retrofit projects. While the monitored data allows researchers to understand why these differences exist, it is not cost effective to monitor each home with the level of detail presented here. Therefore an appropriate balance between modeling and monitoring must be determined for more widespread application in retrofit programs and the home performance industry. Recommendations to address these deficiencies include: (1) improved tuning process for pre-retrofit energy use, which currently utilized broad-based monthly utility bills; (2) developing simple occupant-based energy models that better address the many different occupant types and their impact on energy use; (3) incorporating actual weather inputs to increase accuracy of the tuning process, which uses utility bills from specific time period; and (4) developing simple, cost-effective monitoring solutions for improved model tuning.

Blanchard, Jeremy; Widder, Sarah H.; Giever, Elisabeth L.; Baechler, Michael C.

2012-10-01T23:59:59.000Z

282

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

SciTech Connect

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

283

Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model  

Science Journals Connector (OSTI)

Abstract Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.

Weigang Zhao; Jianzhou Wang; Haiyan Lu

2014-01-01T23:59:59.000Z

284

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

Abstract Exponential smoothing models represent an important prediction tool both in business and in macroeconomics. This paper provides the analytical forecasting properties of the random coefficient exponential smoothing model in the “multiple source of error” framework. The random coefficient state-space representation allows for switching between simple exponential smoothing and local linear trend. Therefore it enables controlling, in a flexible manner, the random changing dynamic behavior of the time series. The paper establishes the algebraic mapping between the state-space parameters and the implied reduced form ARIMA parameters. In addition, it shows that the parametric mapping allows overcoming the difficulties that are likely to emerge in estimating directly the random coefficient state-space model. Finally, it presents an empirical application comparing the forecast accuracy of the suggested model vis-à-vis other benchmark models, both in the ARIMA and in the exponential smoothing class. Using time series relative to wholesalers inventories in the USA, the out-of-sample results show that the reduced form of the random coefficient exponential smoothing model tends to be superior to its competitors.

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

285

Voluntary Green Power Market Forecast through 2015  

SciTech Connect

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

286

Expert Panel: Forecast Future Demand for Medical Isotopes  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Expert Panel: Expert Panel: Forecast Future Demand for Medical Isotopes March 1999 Expert Panel: Forecast Future Demand for Medical Isotopes September 25-26, 1998 Arlington, Virginia The Expert Panel ............................................................................................. Page 1 Charge To The Expert Panel........................................................................... Page 2 Executive Summary......................................................................................... Page 3 Introduction ...................................................................................................... Page 4 Rationale.......................................................................................................... Page 6 Economic Analysis...........................................................................................

287

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired ... Keywords: Financial forecasting, Hybrid models, Quantum-inspired evolutionary algorithm, Qubit multilayer perceptron, Random walk dilemma

Ricardo de A. Araújo

2012-03-01T23:59:59.000Z

288

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

In this paper is presented a web tool for electricity forecast for Kosovo market for the upcoming ten years. The input data i.e. electricity generation capacities, demand and consume are taken from the document "Kosovo Energy Strategy 2009-2018" compiled ... Keywords: .NET, database, electricity forecast, internet, simulation, web

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

289

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network (OSTI)

production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size. On the other hand if forecasted high production events do not occur, the cost of de- optimisation Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract

Paris-Sud XI, Université de

290

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

291

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

292

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

293

E-Print Network 3.0 - africa conditional forecasts Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

Search Powered by Explorit Topic List Advanced Search Sample search results for: africa conditional forecasts Page: << < 1 2 3 4 5 > >> 1 COLORADO STATE UNIVERSITY FORECAST...

294

Fusion of artificial neural network and fuzzy system for short term weather forecasting  

Science Journals Connector (OSTI)

Weather forecasting is the challenging problem for the modern life. Some researches have been conducted to design the accurate prediction in some past years but still it is incomplete. In this paper, we propose the system of short period weather forecasting designed based on the current weather parameter consisted of temperature, humidity, air pressure, wind direction and speed and present weather condition. This system uses fusion of feed forward artificial neural network (ANN) and fuzzy system architecture as main algorithm of weather prediction, Lavendberg-Marquadt as learning algorithm and fuzzy C-mean (FCM) as clustering method in initialisation step. Based on the system architecture, this method can predict the weather continuously despite the change of unpredictable patterns. Furthermore, this system has clear reasoning logic on the fuzzy logic instead of its adaptation ability on its neural network architecture. The performance of proposed system has accuracy up to 78% for validity among three possible weathers, i.e., shiny, cloudy and rainy.

Budiman Putra; Bagus Tris Atmaja; Syahroni Hidayat

2012-01-01T23:59:59.000Z

295

Net Interchange Schedule Forecasting of Electric Power Exchange for RTO/ISOs  

SciTech Connect

Neighboring independent system operators (ISOs) exchange electric power to enable efficient and reliable operation of the grid. Net interchange (NI) schedule is the sum of the transactions (in MW) between an ISO and its neighbors. Effective forecasting of the amount of actual NI can improve grid operation efficiency. This paper presents results of a preliminary investigation into various methods of prediction that may result in improved prediction accuracy. The methods studied are linear regression, forward regression, stepwise regression, and support vector machine (SVM) regression. The work to date is not yet conclusive. The hope is to explore the effectiveness of other prediction methods and apply all methods to at least one new data set. This should enable more confidence in the conclusions.

Ferryman, Thomas A.; Haglin, David J.; Vlachopoulou, Maria; Yin, Jian; Shen, Chao; Tuffner, Francis K.; Lin, Guang; Zhou, Ning; Tong, Jianzhong

2012-07-26T23:59:59.000Z

296

Table 10. Natural Gas Net Imports, Projected vs. Actual  

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

Natural Gas Net Imports, Projected vs. Actual" Natural Gas Net Imports, Projected vs. Actual" "Projected" " (trillion cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",2.02,2.4,2.66,2.74,2.81,2.85,2.89,2.93,2.95,2.97,3,3.16,3.31,3.5,3.57,3.63,3.74,3.85 "AEO 1995",,2.46,2.54,2.8,2.87,2.87,2.89,2.9,2.9,2.92,2.95,2.97,3,3.03,3.19,3.35,3.51,3.6 "AEO 1996",,,2.56,2.75,2.85,2.88,2.93,2.98,3.02,3.06,3.07,3.09,3.12,3.17,3.23,3.29,3.37,3.46,3.56 "AEO 1997",,,,2.82,2.96,3.16,3.43,3.46,3.5,3.53,3.58,3.64,3.69,3.74,3.78,3.83,3.87,3.92,3.97 "AEO 1998",,,,,2.95,3.19,3.531808376,3.842532873,3.869043112,3.894513845,3.935930967,3.976293564,4.021911621,4.062207222,4.107616425,4.164502144,4.221304417,4.277039051,4.339964867

297

Table 12. Total Coal Consumption, Projected vs. Actual Projected  

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

Total Coal Consumption, Projected vs. Actual Total Coal Consumption, Projected vs. Actual Projected (million short tons) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 920 928 933 938 943 948 953 958 962 967 978 990 987 992 1006 1035 1061 1079 AEO 1995 935 940 941 947 948 951 954 958 963 971 984 992 996 1002 1013 1025 1039 AEO 1996 937 942 954 962 983 990 1004 1017 1027 1033 1046 1067 1070 1071 1074 1082 1087 AEO 1997 948 970 987 1003 1017 1020 1025 1034 1041 1054 1075 1086 1092 1092 1099 1104 AEO 1998 1009 1051 1044 1058 1087 1084 1090 1097 1112 1130 1142 1148 1160 1162 1180 AEO 1999 1040 1075 1092 1109 1113 1118 1120 1120 1133 1139 1150 1155 1156 1173 AEO 2000 1053 1086 1103 1124 1142 1164 1175 1184 1189 1194 1199 1195 1200 AEO 2001 1078 1112 1135 1153 1165 1183 1191 1220 1228 1228 1235 1240

298

Table 22. Total Carbon Dioxide Emissions, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Total Carbon Dioxide Emissions, Projected vs. Actual Total Carbon Dioxide Emissions, Projected vs. Actual (million metric tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 AEO 1983 AEO 1984 AEO 1985 AEO 1986 AEO 1987 AEO 1989* AEO 1990 AEO 1991 AEO 1992 AEO 1993 5009 5053 5130 5207 5269 5335 5401 5449 5504 5562 5621 5672 5724 5771 5819 5867 5918 5969 AEO 1994 5060 5130 5185 5240 5287 5335 5379 5438 5482 5529 5599 5658 5694 5738 5797 5874 5925 AEO 1995 5137 5174 5188 5262 5309 5361 5394 5441.3 5489.0 5551.3 5621.0 5679.7 5727.3 5775.0 5841.0 5888.7 AEO 1996 5182 5224 5295 5355 5417 5464 5525 5589 5660 5735 5812 5879 5925 5981 6030 AEO 1997 5295 5381 5491 5586 5658 5715 5781 5863 5934 6009 6106 6184 6236 6268 AEO 1998 5474 5621 5711 5784 5893 5957 6026 6098 6192 6292 6379 6465 6542 AEO 1999 5522 5689 5810 5913 5976 6036 6084 6152 6244 6325 6418 6493 AEO 2000

299

Table 16. Total Electricity Sales, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Electricity Sales, Projected vs. Actual Electricity Sales, Projected vs. Actual (billion kilowatt-hours) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 2364 2454 2534 2626 2708 2811 AEO 1983 2318 2395 2476 2565 2650 2739 3153 AEO 1984 2321 2376 2461 2551 2637 2738 3182 AEO 1985 2317 2360 2427 2491 2570 2651 2730 2808 2879 2949 3026 AEO 1986 2363 2416 2479 2533 2608 2706 2798 2883 2966 3048 3116 3185 3255 3324 3397 AEO 1987 2460 2494 2555 2622 2683 2748 2823 2902 2977 3363 AEO 1989* 2556 2619 2689 2760 2835 2917 2994 3072 3156 3236 3313 3394 3473 AEO 1990 2612 2689 3083 3488.0 3870.0 AEO 1991 2700 2762 2806 2855 2904 2959 3022 3088 3151 3214 3282 3355 3427 3496 3563 3632 3704 3776 3846 3916 AEO 1992 2746 2845 2858 2913 2975 3030 3087 3146 3209 3276 3345 3415 3483 3552 3625 3699 3774 3847 3921 AEO 1993 2803 2840 2893 2946 2998 3052 3104 3157 3214 3271 3327

300

Table 5. Domestic Crude Oil Production, Projected vs. Actual  

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

Domestic Crude Oil Production, Projected vs. Actual" Domestic Crude Oil Production, Projected vs. Actual" "Projected" " (million barrels)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",2507.55,2372.5,2255.7,2160.8,2087.8,2022.1,1952.75,1890.7,1850.55,1825,1799.45,1781.2,1766.6,1759.3,1777.55,1788.5,1806.75,1861.5 "AEO 1995",,2401.7,2306.8,2204.6,2095.1,2036.7,1967.35,1952.75,1923.55,1916.25,1905.3,1894.35,1883.4,1887.05,1887.05,1919.9,1945.45,1967.35 "AEO 1996",,,2387.1,2310.45,2248.4,2171.75,2113.35,2062.25,2011.15,1978.3,1952.75,1938.15,1916.25,1919.9,1927.2,1949.1,1971,1985.6,2000.2 "AEO 1997",,,,2361.55,2306.8,2244.75,2197.3,2142.55,2091.45,2054.95,2033.05,2014.8,2003.85,1996.55,1989.25,1981.95,1974.65,1967.35,1949.1

Note: This page contains sample records for the topic "actual base forecast" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
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301

Table 16. Total Energy Consumption, Projected vs. Actual  

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

Total Energy Consumption, Projected vs. Actual" Total Energy Consumption, Projected vs. Actual" "Projected" " (quadrillion Btu)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",88.02,89.53,90.72,91.73,92.71,93.61,94.56,95.73,96.69,97.69,98.89,100,100.79,101.7,102.7,103.6,104.3,105.23 "AEO 1995",,89.21,89.98,90.57,91.91,92.98,93.84,94.61,95.3,96.19,97.18,98.38,99.37,100.3,101.2,102.1,102.9,103.88 "AEO 1996",,,90.6,91.26,92.54,93.46,94.27,95.07,95.94,96.92,97.98,99.2,100.38,101.4,102.1,103.1,103.8,104.69,105.5 "AEO 1997",,,,92.64,93.58,95.13,96.59,97.85,98.79,99.9,101.2,102.4,103.4,104.7,105.8,106.6,107.2,107.9,108.6 "AEO 1998",,,,,94.68,96.71,98.61027527,99.81855774,101.254303,102.3907928,103.3935776,104.453476,105.8160553,107.2683716,108.5873566,109.8798981,111.0723877,112.166893,113.0926208

302

Table 7a. Natural Gas Wellhead Prices, Projected vs. Actual  

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

a. Natural Gas Wellhead Prices, Projected vs. Actual" a. Natural Gas Wellhead Prices, Projected vs. Actual" "Projected Price in Constant Dollars" " (constant dollars per thousand cubic feet in ""dollar year"" specific to each AEO)" ,"AEO Dollar Year",1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",1992,1.9399,2.029,2.1099,2.1899,2.29,2.35,2.39,2.42,2.47,2.55,2.65,2.75,2.89,3.01,3.17,3.3,3.35,3.47 "AEO 1995",1993,,1.85,1.899,1.81,1.87,1.8999,2.06,2.14,2.34,2.47,2.69,2.83,3.02,3.12,3.21,3.3,3.35,3.39 "AEO 1996",1994,,,1.597672343,1.665446997,1.74129355,1.815978527,1.866241336,1.892736554,1.913619637,1.928664207,1.943216205,1.964540124,1.988652706,2.003382921,2.024799585,2.056392431,2.099974155,2.14731431,2.218094587

303

Table 14a. Average Electricity Prices, Projected vs. Actual  

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

a. Average Electricity Prices, Projected vs. Actual a. Average Electricity Prices, Projected vs. Actual Projected Price in Constant Dollars (constant dollars, cents per kilowatt-hour in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1995 1993 6.80 6.80 6.70 6.70 6.70 6.70 6.70 6.80 6.80 6.90 6.90 6.90 7.00 7.00 7.10 7.10 7.20 AEO 1996 1994 7.09 6.99 6.94 6.93 6.96 6.96 6.96 6.97 6.98 6.97 6.98 6.95 6.95 6.94 6.96 6.95 6.91 AEO 1997 1995 6.94 6.89 6.90 6.91 6.86 6.84 6.78 6.73 6.66 6.60 6.58 6.54 6.49 6.48 6.45 6.36

304

Table 4. Total Petroleum Consumption, Projected vs. Actual  

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

Total Petroleum Consumption, Projected vs. Actual" Total Petroleum Consumption, Projected vs. Actual" "Projected" " (million barrels)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",6449.55,6566.35,6643,6723.3,6810.9,6880.25,6956.9,7059.1,7124.8,7205.1,7296.35,7376.65,7446,7522.65,7595.65,7665,7712.45,7774.5 "AEO 1995",,6398.45,6544.45,6555.4,6675.85,6745.2,6821.85,6887.55,6964.2,7048.15,7146.7,7245.25,7336.5,7405.85,7471.55,7537.25,7581.05,7621.2 "AEO 1996",,,6489.7,6526.2,6606.5,6708.7,6781.7,6854.7,6942.3,7008,7084.65,7175.9,7259.85,7329.2,7383.95,7449.65,7500.75,7544.55,7581.05 "AEO 1997",,,,6635.7,6694.1,6825.5,6953.25,7073.7,7183.2,7267.15,7369.35,7460.6,7548.2,7643.1,7730.7,7792.75,7832.9,7884,7924.15

305

Table 9. Natural Gas Production, Projected vs. Actual  

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

Natural Gas Production, Projected vs. Actual" Natural Gas Production, Projected vs. Actual" "Projected" " (trillion cubic feet)" ,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011 "AEO 1994",17.71,17.68,17.84,18.12,18.25,18.43,18.58,18.93,19.28,19.51,19.8,19.92,20.13,20.18,20.38,20.35,20.16,20.19 "AEO 1995",,18.28,17.98,17.92,18.21,18.63,18.92,19.08,19.2,19.36,19.52,19.75,19.94,20.17,20.28,20.6,20.59,20.88 "AEO 1996",,,18.9,19.15,19.52,19.59,19.59,19.65,19.73,19.97,20.36,20.82,21.25,21.37,21.68,22.11,22.47,22.83,23.36 "AEO 1997",,,,19.1,19.7,20.17,20.32,20.54,20.77,21.26,21.9,22.31,22.66,22.93,23.38,23.68,23.99,24.25,24.65 "AEO 1998",,,,,18.85,19.06,20.34936142,20.27427673,20.60257721,20.94442177,21.44076347,21.80969238,22.25416183,22.65365219,23.176651,23.74545097,24.22989273,24.70069313,24.96691322

306

Table 7a. Natural Gas Wellhead Prices, Projected vs. Actual  

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

a. Natural Gas Wellhead Prices, Projected vs. Actual a. Natural Gas Wellhead Prices, Projected vs. Actual Projected Price in Constant Dollars (constant dollars per thousand cubic feet in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1992 1.94 2.03 2.11 2.19 2.29 2.35 2.39 2.42 2.47 2.55 2.65 2.75 2.89 3.01 3.17 3.30 3.35 3.47 AEO 1995 1993 1.85 1.90 1.81 1.87 1.90 2.06 2.14 2.34 2.47 2.69 2.83 3.02 3.12 3.21 3.30 3.35 3.39 AEO 1996 1994 1.60 1.67 1.74 1.82 1.87 1.89 1.91 1.93 1.94 1.96 1.99 2.00 2.02 2.06 2.10 2.15 2.22

307

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

SciTech Connect

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

308

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Electricity Electricity Electricity consumption nearly doubles in the IEO2005 projection period. The emerging economies of Asia are expected to lead the increase in world electricity use. Figure 58. World Net Electricity Consumption, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 59. World Net Electricity Consumption by Region, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data The International Energy Outlook 2005 (IEO2005) reference case projects that world net electricity consumption will nearly double over the next two decades.10 Over the forecast period, world electricity demand is projected to grow at an average rate of 2.6 percent per year, from 14,275 billion

309

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts The International Energy Outlook is prepared by the Energy Information Administration (EIA). General questions concerning the contents of the report should be referred to John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Office of Integrated Analysis and Forecasting. Specific questions about the report should be referred to Linda E. Doman (202/586-1041) or the following analysts: World Energy and Economic Outlook Linda Doman (linda.doman@eia.doe.gov, 202-586-1041) Macroeconomic Assumptions Nasir Khilji (nasir.khilji@eia.doe.gov, 202-586-1294) Energy Consumption by End-Use Sector Residential Energy Use John Cymbalsky (john.cymbalsky@eia.doe.gov, 202-586-4815) Commercial Energy Use Erin Boedecker (erin.boedecker@eia.doe.gov, 202-586-4791)

310

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Natural Gas Natural gas is the fastest growing primary energy source in the IEO2005 forecast. Consumption of natural gas is projected to increase by nearly 70 percent between 2002 and 2025, with the most robust growth in demand expected among the emerging economies. Figure 34. World Natural Gas Consumption, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center on 202-586-8800. Figure Data Figure 35. Natural Gas Consumption by Region, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 36. Increase in Natural Gas Consumption by Region and Country, 2002-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data

311

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End-Use Sector Energy Consumption by End-Use Sector In the IEO2005 projections, end-use energy consumption in the residential, commercial, industrial, and transportation sectors varies widely among regions and from country to country. One way of looking at the future of world energy markets is to consider trends in energy consumption at the end-use sector level. With the exception of the transportation sector, which is almost universally dominated by petroleum products at present, the mix of energy use in the residential, commercial, and industrial sectors can vary widely from country to country, depending on a combination of regional factors, such as the availability of energy resources, the level of economic development, and political, social, and demographic factors. This chapter outlines the International Energy Outlook 2005 (IEO2005) forecast for regional energy consumption by end-use sector.

312

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

Adaptive exponential smoothing methods allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the time series. This paper presents a new adaptive method for predicting the volatility in financial returns. It enables the smoothing parameter to vary as a logistic function of user-specified variables. The approach is analogous to that used to model time-varying parameters in smooth transition generalised autoregressive conditional heteroskedastic (GARCH) models. These non-linear models allow the dynamics of the conditional variance model to be influenced by the sign and size of past shocks. These factors can also be used as transition variables in the new smooth transition exponential smoothing (STES) approach. Parameters are estimated for the method by minimising the sum of squared deviations between realised and forecast volatility. Using stock index data, the new method gave encouraging results when compared to fixed parameter exponential smoothing and a variety of GARCH models.

James W. Taylor

2004-01-01T23:59:59.000Z

313

Prediction of Indian summer monsoon onset using dynamical sub-seasonal forecasts: effects of realistic initialization of the atmosphere  

Science Journals Connector (OSTI)

Ensembles of retrospective 2-months dynamical forecasts initiated May 1st are used to predict the onset of the Indian Summer Monsoon (ISM) for the period 1989-2005. The Sub-Seasonal Predictions (SSPs) are based on a Coupled General Circulation ...

Andrea Alessandri; Andrea Borrelli; Annalisa Cherchi; Stefano Materia; Antonio Navarra; June-Yi Lee; Bin Wang

314

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Average Absolute Percent Error Variable AEO82 to AEO98 AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 Consumption Total Energy Consumption 1.7 1.7 1.8 1.9 1.9 2.1 Total Petroleum Consumption 2.9 2.8 2.9 3.0 2.9 2.9 Total Natural Gas Consumption 5.7 5.6 5.6 5.5 5.5 6.5 Total Coal Consumption 3.0 3.2 3.3 3.5 3.6 3.7 Total Electricity Sales 1.7 1.8 1.9 2.4 2.5 2.4 Production Crude Oil Production 4.3 4.5 4.5 4.5 4.5 4.7 Natural Gas Production 4.8 4.7 4.6 4.6 4.4 4.4 Coal Production 3.6 3.6 3.5 3.7 3.6 3.8 Imports and Exports Net Petroleum Imports 9.5 8.8 8.4 7.9 7.4 7.5 Net Natural Gas Imports 16.7 16.0 15.9 15.8 15.8 15.4

315

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect

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

316

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

317

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

SciTech Connect

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

318

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

E-Print Network (OSTI)

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

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

319

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

This paper uses the approach suggested by Akrigay (1989), Tse and Tung (1992) and Dimson and Marsh (1990) to examine the forecasting accuracy of stock price index models for industrialised markets. The focus of this paper is to compare the Mean Absolute Percentage Error (MAPE) of three models, that is, the Random Walk model, the Single Exponential Smoothing model and the Conditional Heteroskedastic model with the MAPE of the benchmark Naive Forecast 1 case. We do not evidence that a single model to provide better forecasting accuracy results compared to other models.

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

320

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

Note: This page contains sample records for the topic "actual base forecast" 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

18 Bureau of Meteorology Annual Report 201314 Hazards, warnings and forecasts  

E-Print Network (OSTI)

and numerical prediction models. #12;19Bureau of Meteorology Annual Report 2013­14 2 Performance Performance programs: · Weather forecasting services; · Flood forecasting and warning services; · Hazard prediction, Warnings and Forecasts portfolio provides a range of forecast and warning services covering weather, ocean

Greenslade, Diana

322

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy  

Science Journals Connector (OSTI)

Abstract Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10 min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for the development of residential energy forecasting models, our results have practical significance for the deployment and installation of advanced smart metering devices. Ultimately, accurate and cost effective wide-scale energy prediction is a vital step towards next-generation energy efficiency initiatives, which will require not only consideration of the methods, but the scales for which data can be distilled into meaningful information.

Rishee K. Jain; Kevin M. Smith; Patricia J. Culligan; John E. Taylor

2014-01-01T23:59:59.000Z

323

Table 18. Total Delivered Commercial Energy Consumption, Projected vs. Actual  

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

Total Delivered Commercial Energy Consumption, Projected vs. Actual Total Delivered Commercial Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 6.8 6.9 6.9 7.0 7.1 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.4 7.5 7.5 7.5 7.5 7.6 AEO 1995 6.9 6.9 7.0 7.0 7.0 7.1 7.1 7.1 7.1 7.1 7.2 7.2 7.2 7.2 7.3 7.3 7.3 AEO 1996 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.5 7.6 7.6 7.7 7.7 7.8 7.9 8.0 8.0 8.1 AEO 1997 7.4 7.4 7.4 7.5 7.5 7.6 7.7 7.7 7.8 7.8 7.9 7.9 8.0 8.1 8.1 8.2 AEO 1998 7.5 7.6 7.7 7.8 7.9 8.0 8.0 8.1 8.2 8.3 8.4 8.4 8.5 8.6 8.7 AEO 1999 7.4 7.8 7.9 8.0 8.1 8.2 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 AEO 2000 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.5 8.7 8.7 8.8 AEO 2001 7.8 8.1 8.3 8.6 8.7 8.9 9.0 9.2 9.3 9.5 9.6 9.7 AEO 2002 8.2 8.4 8.7 8.9 9.0 9.2 9.4 9.6 9.7 9.9 10.1

324

Table 21. Total Transportation Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Transportation Energy Consumption, Projected vs. Actual Transportation Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 18.6 18.2 17.7 17.3 17.0 16.9 AEO 1983 19.8 20.1 20.4 20.4 20.5 20.5 20.7 AEO 1984 19.2 19.0 19.0 19.0 19.1 19.2 20.1 AEO 1985 20.0 19.8 20.0 20.0 20.0 20.1 20.3 AEO 1986 20.5 20.8 20.8 20.6 20.7 20.3 21.0 AEO 1987 21.3 21.5 21.6 21.7 21.8 22.0 22.0 22.0 21.9 22.3 AEO 1989* 21.8 22.2 22.4 22.4 22.5 22.5 22.5 22.5 22.6 22.7 22.8 23.0 23.2 AEO 1990 22.0 22.4 23.2 24.3 25.5 AEO 1991 22.1 21.6 21.9 22.1 22.3 22.5 22.8 23.1 23.4 23.8 24.1 24.5 24.8 25.1 25.4 25.7 26.0 26.3 26.6 26.9 AEO 1992 21.7 22.0 22.5 22.9 23.2 23.4 23.6 23.9 24.1 24.4 24.8 25.1 25.4 25.7 26.0 26.3 26.6 26.9 27.1 AEO 1993 22.5 22.8 23.4 23.9 24.3 24.7 25.1 25.4 25.7 26.1 26.5 26.8 27.2 27.6 27.9 28.1 28.4 28.7 AEO 1994 23.6

325

Table 10. Natural Gas Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Production, Projected vs. Actual Production, Projected vs. Actual (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 14.74 14.26 14.33 14.89 15.39 15.88 AEO 1983 16.48 16.27 16.20 16.31 16.27 16.29 14.89 AEO 1984 17.48 17.10 17.44 17.58 17.52 17.32 16.39 AEO 1985 16.95 17.08 17.11 17.29 17.40 17.33 17.32 17.27 17.05 16.80 16.50 AEO 1986 16.30 16.27 17.15 16.68 16.90 16.97 16.87 16.93 16.86 16.62 16.40 16.33 16.57 16.23 16.12 AEO 1987 16.21 16.09 16.38 16.32 16.30 16.30 16.44 16.62 16.81 17.39 AEO 1989* 16.71 16.71 16.94 17.01 16.83 17.09 17.35 17.54 17.67 17.98 18.20 18.25 18.49 AEO 1990 16.91 17.25 18.84 20.58 20.24 AEO 1991 17.40 17.48 18.11 18.22 18.15 18.22 18.39 18.82 19.03 19.28 19.62 19.89 20.13 20.07 19.95 19.82 19.64 19.50 19.30 19.08 AEO 1992 17.43 17.69 17.95 18.00 18.29 18.27 18.51 18.75 18.97

326

Table 17. Total Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption, Projected vs. Actual Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 79.1 79.6 79.9 80.8 82.1 83.3 AEO 1983 78.0 79.5 81.0 82.4 83.9 84.6 89.0 AEO 1984 78.5 79.4 81.2 83.1 85.1 86.4 93.0 AEO 1985 77.6 78.5 79.8 81.2 82.7 83.3 84.2 85.0 85.7 86.3 87.2 AEO 1986 77.0 78.8 79.8 80.7 81.5 82.9 83.8 84.6 85.3 86.0 86.6 87.4 88.3 89.4 90.2 AEO 1987 78.9 80.0 82.0 82.8 83.9 85.1 86.2 87.1 87.9 92.5 AEO 1989* 82.2 83.8 84.5 85.4 86.2 87.1 87.8 88.7 89.5 90.4 91.4 92.4 93.5 AEO 1990 84.2 85.4 91.9 97.4 102.8 AEO 1991 84.4 85.0 86.0 87.0 87.9 89.1 90.4 91.8 93.1 94.3 95.6 97.1 98.4 99.4 100.3 101.4 102.5 103.6 104.7 105.8 AEO 1992 84.7 87.0 88.0 89.2 90.5 91.4 92.4 93.4 94.5 95.6 96.9 98.0 99.0 100.0 101.2 102.2 103.2 104.3 105.2 AEO 1993 87.0 88.3 89.8 91.4 92.7 94.0 95.3 96.3 97.5 98.6

327

Table 3. Gross Domestic Product, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Gross Domestic Product, Projected vs. Actual Gross Domestic Product, Projected vs. Actual (cumulative average percent growth in projected real GDP from first year shown for each AEO) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 4.3% 3.8% 3.6% 3.3% 3.2% 3.2% AEO 1983 3.3% 3.3% 3.4% 3.3% 3.2% 3.1% 2.7% AEO 1984 2.7% 2.4% 2.9% 3.1% 3.1% 3.1% 2.7% AEO 1985 2.3% 2.2% 2.7% 2.8% 2.9% 3.0% 3.0% 3.0% 2.9% 2.8% 2.8% AEO 1986 2.6% 2.5% 2.7% 2.5% 2.5% 2.6% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% 2.5% AEO 1987 2.7% 2.3% 2.4% 2.5% 2.5% 2.6% 2.6% 2.5% 2.4% 2.3% AEO 1989* 4.0% 3.4% 3.1% 3.0% 2.9% 2.8% 2.7% 2.7% 2.7% 2.6% 2.6% 2.6% 2.6% AEO 1990 2.9% 2.3% 2.5% 2.5% 2.4% AEO 1991 0.8% 1.0% 1.7% 1.8% 1.8% 1.9% 2.0% 2.1% 2.1% 2.1% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% AEO 1992 -0.1% 1.6% 2.0% 2.2% 2.3% 2.2% 2.2% 2.2% 2.2% 2.3% 2.3% 2.3% 2.3% 2.2%

328

Table 20. Total Industrial Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Industrial Energy Consumption, Projected vs. Actual Industrial Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 24.0 24.1 24.4 24.9 25.5 26.1 AEO 1983 23.2 23.6 23.9 24.4 24.9 25.0 25.4 AEO 1984 24.1 24.5 25.4 25.5 27.1 27.4 28.7 AEO 1985 23.2 23.6 23.9 24.4 24.8 24.8 24.4 AEO 1986 22.2 22.8 23.1 23.4 23.4 23.6 22.8 AEO 1987 22.4 22.8 23.7 24.0 24.3 24.6 24.6 24.7 24.9 22.6 AEO 1989* 23.6 24.0 24.1 24.3 24.5 24.3 24.3 24.5 24.6 24.8 24.9 24.4 24.1 AEO 1990 25.0 25.4 27.1 27.3 28.6 AEO 1991 24.6 24.5 24.8 24.8 25.0 25.3 25.7 26.2 26.5 26.1 25.9 26.2 26.4 26.6 26.7 27.0 27.2 27.4 27.7 28.0 AEO 1992 24.6 25.3 25.4 25.6 26.1 26.3 26.5 26.5 26.0 25.6 25.8 26.0 26.1 26.2 26.4 26.7 26.9 27.2 27.3 AEO 1993 25.5 25.9 26.2 26.8 27.1 27.5 27.8 27.4 27.1 27.4 27.6 27.8 28.0 28.2 28.4 28.7 28.9 29.1 AEO 1994 25.4 25.9

329

Table 8. Natural Gas Wellhead Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Wellhead Prices, Projected vs. Actual Natural Gas Wellhead Prices, Projected vs. Actual (current dollars per thousand cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 4.32 5.47 6.67 7.51 8.04 8.57 AEO 1983 2.93 3.11 3.46 3.93 4.56 5.26 12.74 AEO 1984 2.77 2.90 3.21 3.63 4.13 4.79 9.33 AEO 1985 2.60 2.61 2.66 2.71 2.94 3.35 3.85 4.46 5.10 5.83 6.67 AEO 1986 1.73 1.96 2.29 2.54 2.81 3.15 3.73 4.34 5.06 5.90 6.79 7.70 8.62 9.68 10.80 AEO 1987 1.83 1.95 2.11 2.28 2.49 2.72 3.08 3.51 4.07 7.54 AEO 1989* 1.62 1.70 1.91 2.13 2.58 3.04 3.48 3.93 4.76 5.23 5.80 6.43 6.98 AEO 1990 1.78 1.88 2.93 5.36 9.2 AEO 1991 1.77 1.90 2.11 2.30 2.42 2.51 2.60 2.74 2.91 3.29 3.75 4.31 5.07 5.77 6.45 7.29 8.09 8.94 9.62 10.27 AEO 1992 1.69 1.85 2.03 2.15 2.35 2.51 2.74 3.01 3.40 3.81 4.24 4.74 5.25 5.78 6.37 6.89 7.50 8.15 9.05 AEO 1993 1.85 1.94 2.09 2.30

330

Table 16. Total Energy Consumption, Projected vs. Actual Projected  

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

Total Energy Consumption, Projected vs. Actual Total Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 88.0 89.5 90.7 91.7 92.7 93.6 94.6 95.7 96.7 97.7 98.9 100.0 100.8 101.7 102.7 103.6 104.3 105.2 AEO 1995 89.2 90.0 90.6 91.9 93.0 93.8 94.6 95.3 96.2 97.2 98.4 99.4 100.3 101.2 102.1 102.9 103.9 AEO 1996 90.6 91.3 92.5 93.5 94.3 95.1 95.9 96.9 98.0 99.2 100.4 101.4 102.1 103.1 103.8 104.7 105.5 AEO 1997 92.6 93.6 95.1 96.6 97.9 98.8 99.9 101.2 102.4 103.4 104.7 105.8 106.6 107.2 107.9 108.6 AEO 1998 94.7 96.7 98.6 99.8 101.3 102.4 103.4 104.5 105.8 107.3 108.6 109.9 111.1 112.2 113.1 AEO 1999 94.6 97.0 99.2 100.9 102.0 102.8 103.6 104.7 106.0 107.2 108.5 109.7 110.8 111.8

331

Table 9. Natural Gas Production, Projected vs. Actual Projected  

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

Natural Gas Production, Projected vs. Actual Natural Gas Production, Projected vs. Actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 17.71 17.68 17.84 18.12 18.25 18.43 18.58 18.93 19.28 19.51 19.80 19.92 20.13 20.18 20.38 20.35 20.16 20.19 AEO 1995 18.28 17.98 17.92 18.21 18.63 18.92 19.08 19.20 19.36 19.52 19.75 19.94 20.17 20.28 20.60 20.59 20.88 AEO 1996 18.90 19.15 19.52 19.59 19.59 19.65 19.73 19.97 20.36 20.82 21.25 21.37 21.68 22.11 22.47 22.83 23.36 AEO 1997 19.10 19.70 20.17 20.32 20.54 20.77 21.26 21.90 22.31 22.66 22.93 23.38 23.68 23.99 24.25 24.65 AEO 1998 18.85 19.06 20.35 20.27 20.60 20.94 21.44 21.81 22.25 22.65 23.18 23.75 24.23 24.70 24.97 AEO 1999 18.80 19.13 19.28 19.82 20.23 20.77 21.05 21.57 21.98 22.47 22.85 23.26 23.77 24.15

332

Table 19. Total Delivered Industrial Energy Consumption, Projected vs. Actual  

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

Total Delivered Industrial Energy Consumption, Projected vs. Actual Total Delivered Industrial Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 25.4 25.9 26.3 26.7 27.0 27.1 26.8 26.6 26.9 27.2 27.7 28.1 28.3 28.7 29.1 29.4 29.7 30.0 AEO 1995 26.2 26.3 26.5 27.0 27.3 26.9 26.6 26.8 27.1 27.5 27.9 28.2 28.4 28.7 29.0 29.3 29.6 AEO 1996 26.5 26.6 27.3 27.5 26.9 26.5 26.7 26.9 27.2 27.6 27.9 28.2 28.3 28.5 28.7 28.9 29.2 AEO 1997 26.2 26.5 26.9 26.7 26.6 26.8 27.1 27.4 27.8 28.0 28.4 28.7 28.9 29.0 29.2 29.4 AEO 1998 27.2 27.5 27.2 26.9 27.1 27.5 27.7 27.9 28.3 28.7 29.0 29.3 29.7 29.9 30.1 AEO 1999 26.7 26.4 26.4 26.8 27.1 27.3 27.5 27.9 28.3 28.6 28.9 29.2 29.5 29.7 AEO 2000 25.8 25.5 25.7 26.0 26.5 26.9 27.4 27.8 28.1 28.3 28.5 28.8 29.0

333

Table 18. Total Residential Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Residential Energy Consumption, Projected vs. Actual Residential Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 10.1 10.1 10.1 10.1 10.2 10.2 AEO 1983 9.8 9.9 10.0 10.1 10.2 10.1 10.0 AEO 1984 9.9 9.9 10.0 10.2 10.3 10.3 10.5 AEO 1985 9.8 10.0 10.1 10.3 10.6 10.6 10.9 AEO 1986 9.6 9.8 10.0 10.3 10.4 10.8 10.9 AEO 1987 9.9 10.2 10.3 10.3 10.4 10.5 10.5 10.5 10.5 10.6 AEO 1989* 10.3 10.5 10.4 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 10.5 AEO 1990 10.4 10.7 10.8 11.0 11.3 AEO 1991 10.2 10.7 10.7 10.8 10.8 10.8 10.9 10.9 10.9 11.0 11.0 11.0 11.1 11.2 11.2 11.3 11.4 11.4 11.5 11.6 AEO 1992 10.6 11.1 11.1 11.1 11.1 11.1 11.2 11.2 11.3 11.3 11.4 11.5 11.5 11.6 11.7 11.8 11.8 11.9 12.0 AEO 1993 10.7 10.9 11.0 11.0 11.0 11.1 11.1 11.1 11.1 11.2 11.2 11.2 11.2 11.3 11.3 11.4 11.4 11.5 AEO 1994 10.3 10.4 10.4 10.4

334

Table 6. Domestic Crude Oil Production, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Domestic Crude Oil Production, Projected vs. Actual Domestic Crude Oil Production, Projected vs. Actual (million barrels per day) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 8.79 8.85 8.84 8.80 8.66 8.21 AEO 1983 8.67 8.71 8.66 8.72 8.80 8.63 8.11 AEO 1984 8.86 8.70 8.59 8.45 8.28 8.25 7.19 AEO 1985 8.92 8.96 9.01 8.78 8.38 8.05 7.64 7.27 6.89 6.68 6.53 AEO 1986 8.80 8.63 8.30 7.90 7.43 6.95 6.60 6.36 6.20 5.99 5.80 5.66 5.54 5.45 5.43 AEO 1987 8.31 8.18 8.00 7.63 7.34 7.09 6.86 6.64 6.54 6.03 AEO 1989* 8.18 7.97 7.64 7.25 6.87 6.59 6.37 6.17 6.05 6.00 5.94 5.90 5.89 AEO 1990 7.67 7.37 6.40 5.86 5.35 AEO 1991 7.23 6.98 7.10 7.11 7.01 6.79 6.48 6.22 5.92 5.64 5.36 5.11 4.90 4.73 4.62 4.59 4.58 4.53 4.46 4.42 AEO 1992 7.37 7.17 6.99 6.89 6.68 6.45 6.28 6.16 6.06 5.91 5.79 5.71 5.66 5.64 5.62 5.63 5.62 5.55 5.52 AEO 1993 7.20 6.94 6.79 6.52 6.22 6.00 5.84 5.72

335

Table 17. Total Delivered Residential Energy Consumption, Projected vs. Actual  

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

Total Delivered Residential Energy Consumption, Projected vs. Actual Total Delivered Residential Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 10.3 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.5 10.5 10.5 10.5 10.5 10.6 10.6 AEO 1995 11.0 10.8 10.8 10.8 10.8 10.8 10.8 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.8 10.8 10.9 AEO 1996 10.4 10.7 10.7 10.7 10.8 10.8 10.9 10.9 11.0 11.2 11.2 11.3 11.4 11.5 11.6 11.7 11.8 AEO 1997 11.1 10.9 11.1 11.1 11.2 11.2 11.2 11.3 11.4 11.5 11.5 11.6 11.7 11.8 11.9 12.0 AEO 1998 10.7 11.1 11.2 11.4 11.5 11.5 11.6 11.7 11.8 11.9 11.9 12.1 12.1 12.2 12.3 AEO 1999 10.5 11.1 11.3 11.3 11.4 11.5 11.5 11.6 11.6 11.7 11.8 11.9 12.0 12.1 AEO 2000 10.7 10.9 11.0 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 12.0

336

Table 2. Real Gross Domestic Product, Projected vs. Actual  

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

Real Gross Domestic Product, Projected vs. Actual Real Gross Domestic Product, Projected vs. Actual Projected Real GDP Growth Trend (cumulative average percent growth in projected real GDP from first year shown for each AEO) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 3.1% 3.2% 2.9% 2.8% 2.7% 2.7% 2.6% 2.6% 2.6% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% AEO 1995 3.7% 2.8% 2.5% 2.7% 2.7% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% AEO 1996 2.6% 2.2% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% 2.2% 2.2% 1.6% AEO 1997 2.1% 1.9% 2.0% 2.2% 2.3% 2.3% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.1% 2.1% 1.5% AEO 1998 3.4% 2.9% 2.6% 2.5% 2.4% 2.4% 2.3% 2.3% 2.3% 2.3% 2.3% 2.3% 2.3% 2.2% 1.8% AEO 1999 3.4% 2.5% 2.5% 2.4% 2.4% 2.4% 2.3% 2.4% 2.4% 2.4% 2.4% 2.4% 2.4% 1.8% AEO 2000 3.8% 2.9% 2.7% 2.6% 2.6% 2.6% 2.6% 2.6% 2.5% 2.5%

337

Table 7. Petroleum Net Imports, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Net Imports, Projected vs. Actual Petroleum Net Imports, Projected vs. Actual (million barrels per day) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 7.58 7.45 7.12 6.82 6.66 7.09 AEO 1983 5.15 5.44 5.73 5.79 5.72 5.95 6.96 AEO 1984 4.85 5.11 5.53 5.95 6.31 6.59 8.65 AEO 1985 4.17 4.38 4.73 4.93 5.36 5.72 6.23 6.66 7.14 7.39 7.74 AEO 1986 5.15 5.38 5.46 5.92 6.46 7.09 7.50 7.78 7.96 8.20 8.47 8.74 9.04 9.57 9.76 AEO 1987 5.81 6.04 6.81 7.28 7.82 8.34 8.71 8.94 8.98 10.01 AEO 1989* 6.28 6.84 7.49 7.96 8.53 8.83 9.04 9.28 9.60 9.64 9.75 10.02 10.20 AEO 1990 7.20 7.61 9.13 9.95 11.02 AEO 1991 7.28 7.25 7.34 7.48 7.72 8.10 8.57 9.09 9.61 10.07 10.51 11.00 11.44 11.72 11.86 12.11 12.30 12.49 12.71 12.91 AEO 1992 6.86 7.42 7.88 8.16 8.55 8.80 9.06 9.32 9.50 9.80 10.17 10.35 10.56 10.61 10.85 11.00 11.15 11.29 11.50 AEO 1993 7.25 8.01 8.49 9.06

338

Table 7b. Natural Gas Wellhead Prices, Projected vs. Actual  

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

b. Natural Gas Wellhead Prices, Projected vs. Actual b. Natural Gas Wellhead Prices, Projected vs. Actual Projected Price in Nominal Dollars (nominal dollars per thousand cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 1.98 2.12 2.27 2.41 2.59 2.73 2.85 2.98 3.14 3.35 3.59 3.85 4.18 4.51 4.92 5.29 5.56 5.96 AEO 1995 1.89 2.00 1.95 2.06 2.15 2.40 2.57 2.90 3.16 3.56 3.87 4.27 4.56 4.85 5.16 5.41 5.66 AEO 1996 1.63 1.74 1.86 1.99 2.10 2.19 2.29 2.38 2.48 2.59 2.72 2.84 2.97 3.12 3.29 3.49 3.73 AEO 1997 2.03 1.82 1.90 1.99 2.06 2.13 2.21 2.32 2.43 2.54 2.65 2.77 2.88 3.00 3.11 3.24 AEO 1998 2.30 2.20 2.26 2.31 2.38 2.44 2.52 2.60 2.69 2.79 2.93 3.06 3.20 3.35 3.48 AEO 1999 1.98 2.15 2.20 2.32 2.43 2.53 2.63 2.76 2.90 3.02 3.12 3.23 3.35 3.47

339

Table 20. Total Delivered Transportation Energy Consumption, Projected vs. Actual  

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

Total Delivered Transportation Energy Consumption, Projected vs. Actual Total Delivered Transportation Energy Consumption, Projected vs. Actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 23.6 24.1 24.5 24.7 25.1 25.4 25.7 26.2 26.5 26.9 27.2 27.6 27.9 28.3 28.6 28.9 29.2 29.5 AEO 1995 23.3 24.0 24.2 24.7 25.1 25.5 25.9 26.2 26.5 26.9 27.3 27.7 28.0 28.3 28.5 28.7 28.9 AEO 1996 23.9 24.1 24.5 24.8 25.3 25.7 26.0 26.4 26.7 27.1 27.5 27.8 28.1 28.4 28.6 28.9 29.1 AEO 1997 24.7 25.3 25.9 26.4 27.0 27.5 28.0 28.5 28.9 29.4 29.8 30.3 30.6 30.9 31.1 31.3 AEO 1998 25.3 25.9 26.7 27.1 27.7 28.3 28.8 29.4 30.0 30.6 31.2 31.7 32.3 32.8 33.1 AEO 1999 25.4 26.0 27.0 27.6 28.2 28.8 29.4 30.0 30.6 31.2 31.7 32.2 32.8 33.1 AEO 2000 26.2 26.8 27.4 28.0 28.5 29.1 29.7 30.3 30.9 31.4 31.9 32.5 32.9

340

Table 22. Energy Intensity, Projected vs. Actual Projected  

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

Energy Intensity, Projected vs. Actual Energy Intensity, Projected vs. Actual Projected (quadrillion Btu / real GDP in billion 2005 chained dollars) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 11.2 11.1 11.0 10.8 10.7 10.5 10.4 10.3 10.1 10.0 9.9 9.8 9.7 9.6 9.5 9.4 9.3 9.2 AEO 1995 10.9 10.8 10.6 10.4 10.3 10.1 10.0 9.9 9.8 9.6 9.5 9.4 9.3 9.2 9.1 9.1 9.0 AEO 1996 10.7 10.6 10.4 10.3 10.1 10.0 9.8 9.7 9.6 9.5 9.4 9.3 9.2 9.2 9.1 9.0 8.9 AEO 1997 10.3 10.3 10.2 10.1 9.9 9.8 9.7 9.6 9.5 9.4 9.3 9.2 9.2 9.1 9.0 8.9 AEO 1998 10.1 10.1 10.1 10.0 9.9 9.8 9.7 9.6 9.5 9.5 9.4 9.3 9.2 9.1 9.0 AEO 1999 9.6 9.7 9.7 9.7 9.6 9.4 9.3 9.1 9.0 8.9 8.8 8.7 8.6 8.5 AEO 2000 9.4 9.4 9.3 9.2 9.1 9.0 8.9 8.8 8.7 8.7 8.6 8.5 8.4 AEO 2001 8.7 8.6 8.5 8.4 8.3 8.1 8.0 7.9 7.8 7.6 7.5 7.4

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341

Table 15. Average Electricity Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Average Electricity Prices, Projected vs. Actual Average Electricity Prices, Projected vs. Actual (nominal cents per kilowatt-hour) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 6.38 6.96 7.63 8.23 8.83 9.49 AEO 1983 6.85 7.28 7.74 8.22 8.68 9.18 13.12 AEO 1984 6.67 7.05 7.48 7.89 8.25 8.65 11.53 AEO 1985 6.62 6.94 7.32 7.63 7.89 8.15 8.46 8.85 9.20 9.61 10.04 AEO 1986 6.67 6.88 7.05 7.18 7.35 7.52 7.65 7.87 8.31 8.83 9.41 10.01 10.61 11.33 12.02 AEO 1987 6.63 6.65 6.92 7.12 7.38 7.62 7.94 8.36 8.86 11.99 AEO 1989* 6.50 6.75 7.14 7.48 7.82 8.11 8.50 8.91 9.39 9.91 10.49 11.05 11.61 AEO 1990 6.49 6.72 8.40 10.99 14.5 AEO 1991 6.94 7.31 7.59 7.82 8.18 8.38 8.54 8.73 8.99 9.38 9.83 10.29 10.83 11.36 11.94 12.58 13.21 13.88 14.58 15.21 AEO 1992 6.97 7.16 7.32 7.56 7.78 8.04 8.29 8.57 8.93 9.38 9.82 10.26 10.73 11.25 11.83 12.37 12.96 13.58 14.23 AEO 1993

342

Table 11. Natural Gas Net Imports, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Net Imports, Projected vs. Actual Natural Gas Net Imports, Projected vs. Actual (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 1.19 1.19 1.19 1.19 1.19 1.19 AEO 1983 1.08 1.16 1.23 1.23 1.23 1.23 1.23 AEO 1984 0.99 1.05 1.16 1.27 1.43 1.57 2.11 AEO 1985 0.94 1.00 1.19 1.45 1.58 1.86 1.94 2.06 2.17 2.32 2.44 AEO 1986 0.74 0.88 0.62 1.03 1.05 1.27 1.39 1.47 1.66 1.79 1.96 2.17 2.38 2.42 2.43 AEO 1987 0.84 0.89 1.07 1.16 1.26 1.36 1.46 1.65 1.75 2.50 AEO 1989* 1.15 1.32 1.44 1.52 1.61 1.70 1.79 1.87 1.98 2.06 2.15 2.23 2.31 AEO 1990 1.26 1.43 2.07 2.68 2.95 AEO 1991 1.36 1.53 1.70 1.82 2.11 2.30 2.33 2.36 2.42 2.49 2.56 2.70 2.75 2.83 2.90 2.95 3.02 3.09 3.17 3.19 AEO 1992 1.48 1.62 1.88 2.08 2.25 2.41 2.56 2.68 2.70 2.72 2.76 2.84 2.92 3.05 3.10 3.20 3.25 3.30 3.30 AEO 1993 1.79 2.08 2.35 2.49 2.61 2.74 2.89 2.95 3.00 3.05 3.10

343

Table 8. Total Natural Gas Consumption, Projected vs. Actual  

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

Total Natural Gas Consumption, Projected vs. Actual Total Natural Gas Consumption, Projected vs. Actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1994 19.87 20.21 20.64 20.99 21.20 21.42 21.60 21.99 22.37 22.63 22.95 23.22 23.58 23.82 24.09 24.13 24.02 24.14 AEO 1995 20.82 20.66 20.85 21.21 21.65 21.95 22.12 22.25 22.43 22.62 22.87 23.08 23.36 23.61 24.08 24.23 24.59 AEO 1996 21.32 21.64 22.11 22.21 22.26 22.34 22.46 22.74 23.14 23.63 24.08 24.25 24.63 25.11 25.56 26.00 26.63 AEO 1997 22.15 22.75 23.24 23.64 23.86 24.13 24.65 25.34 25.82 26.22 26.52 27.00 27.35 27.70 28.01 28.47 AEO 1998 21.84 23.03 23.84 24.08 24.44 24.81 25.33 25.72 26.22 26.65 27.22 27.84 28.35 28.84 29.17 AEO 1999 21.35 22.36 22.54 23.18 23.65 24.17 24.57 25.19 25.77 26.41 26.92 27.42 28.02 28.50

344

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

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

and 2009 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid" ,"Projected Year Base","Year",,"FRCC",...

345

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

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

and 2007 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid" ,"Projected Year Base","Year",,"FRCC",...

346

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

Open Energy Info (EERE)

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Non-renewable Energy Topics: Baseline projection, Co-benefits assessment, GHG inventory Resource Type: Software/modeling tools User Interface: Spreadsheet Complexity/Ease of Use: Simple Website: www.esmap.org/esmap/EFFECT Cost: Free Equivalent URI: www.esmap.org/esmap/EFFECT Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Screenshot

347

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

348

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

349

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 ...

Yang, Xiaorong

2008-01-01T23:59:59.000Z

350

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

Exponential smoothing methods are widely used as forecasting techniques in industry and business. Their usual formulation, however, does not allow covariates to be used for introducing extra information into the forecasting process. In this paper, we analyse an extension of the exponential smoothing formulation that allows the use of covariates and the joint estimation of all the unknowns in the model, which improves the forecasting results. The whole procedure is detailed with a real example on forecasting the daily demand for electricity in Spain. The time series of daily electricity demand contains two seasonal patterns: here the within-week seasonal cycle is modelled as usual in exponential smoothing, while the within-year cycle is modelled using covariates, specifically two harmonic explanatory variables. Calendar effects, such as national and local holidays and vacation periods, are also introduced using covariates. [Received 28 September 2010; Revised 6 March 2011, 2 October 2011; Accepted 16 October 2011

José D. Bermúdez

2013-01-01T23:59:59.000Z

351

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

In this paper we analyze the importance of initial conditions in exponential smoothing models on forecast errors and prediction intervals. We work with certain exponential smoothing models, namely Holt’s additive...

E. Vercher; A. Corberán-Vallet; J. V. Segura; J. D. Bermúdez

2012-07-01T23:59:59.000Z

352

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

353

Review/Verify Strategic Skills Needs/Forecasts/Future Mission...  

NLE Websites -- All DOE Office Websites (Extended Search)

ReviewVerify Strategic Skills NeedsForecastsFuture Mission Shifts Annual Lab Plan (1-10 yrs) Fermilab Strategic Agenda (2-5 yrs) Sector program Execution Plans (1-3...

354

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the ...

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

355

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

356

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

357

Recently released EIA report presents international forecasting data  

SciTech Connect

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

358

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network (OSTI)

......................................................................... 11 3. Demand Side Management (DSM) Program Impacts................................... 13 4. Demand Sylvia Bender Manager DEMAND ANALYSIS OFFICE Scott W. Matthews Chief Deputy Director B.B. Blevins Forecast Methods and Models ....................................................... 14 5. Demand-Side

359

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

2012-06-07T23:59:59.000Z

360

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on ...

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

Experiments with the ECMWF model are carried out to study the influence that a correct representation of the lower boundary conditions, the tropical atmosphere, and the Northern Hemisphere stratosphere would have on extended-range forecast skill ...

T. Jung; M. J. Miller; T. N. Palmer

2010-06-01T23:59:59.000Z

362

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

An improved Support Vector Machine (SVM) algorithm is used to forecast wind in Doubly Fed Induction Generator (DFIG) wind power system without aerodromometer. The ... Validation (CV) method. Finally, 3.6MW DFIG w...

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

363

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

In practical problem, number of samples is often limited, for complex issues such as power load forecasting, generally available historical data and information of impact factor are very ... support vector mechan...

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

364

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have ...

Glen S. Romine; Craig S. Schwartz; Judith Berner; Kathryn R. Fossell; Chris Snyder; Jeff L. Anderson; Morris L. Weisman

2014-12-01T23:59:59.000Z

365

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

366

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect

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

367

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good ...

Juan J. Flores; Roberto Loaeza; Héctor Rodríguez; Erasmo Cadenas

2009-11-01T23:59:59.000Z

368

E-Print Network 3.0 - actual results satellitenexperiment Sample...  

NLE Websites -- All DOE Office Websites (Extended Search)

The actual case here corresponds to the minor windows (U0.5) case in Table 6. Table A1: Load and energy... .96) 6343.77 (3316.14) 933.65 (901.44) Major windows (Actual) Diff. - -...

369

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

370

Radiation fog forecasting using a 1-dimensional model  

E-Print Network (OSTI)

measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing... measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing...

Peyraud, Lionel

2012-06-07T23:59:59.000Z

371

Annual Energy Outlook with Projections to 2025 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2005 Forecast Comparisons Table 32. Forecasts of annual average economic growth, 2003-2025 Printer Friendly Version Average annual percentage growth Forecast 2003-2009 2003-2014 2003-2025 AEO2004 3.5 3.2 3.0 AEO2005 Reference 3.4 3.3 3.1 Low growth 2.9 2.8 2.5 High growth 4.1 3.9 3.6 GII 3.4 3.2 3.1 OMB 3.6 NA NA CBO 3.5 3.1 NA OEF 3.5 3.5 NA Only one other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a time horizon similar to that of AEO2005. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as other forecasts that concentrate on economic growth, international oil prices, energy

372

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.

B. Kamranzad; A. Etemad-Shahidi; M.H. Kazeminezhad

2011-01-01T23:59:59.000Z

373

Table 19. Total Commercial Energy Consumption, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Commercial Energy Consumption, Projected vs. Actual Commercial Energy Consumption, Projected vs. Actual (quadrillion Btu) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 6.6 6.7 6.8 6.8 6.8 6.9 AEO 1983 6.4 6.6 6.8 6.9 7.0 7.1 7.2 AEO 1984 6.2 6.4 6.5 6.7 6.8 6.9 7.3 AEO 1985 5.9 6.1 6.2 6.3 6.4 6.5 6.7 AEO 1986 6.2 6.3 6.4 6.4 6.5 7.1 7.4 AEO 1987 6.1 6.1 6.3 6.4 6.6 6.7 6.8 6.9 6.9 7.3 AEO 1989* 6.6 6.7 6.9 7.0 7.0 7.1 7.2 7.3 7.3 7.4 7.5 7.6 7.7 AEO 1990 6.6 6.8 7.1 7.4 7.8 AEO 1991 6.7 6.9 7.0 7.1 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.6 8.7 AEO 1992 6.8 7.1 7.2 7.3 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 AEO 1993 7.2 7.3 7.4 7.4 7.5 7.6 7.7 7.7 7.8 7.9 7.9 8.0 8.0 8.1 8.1 8.1 8.2 8.2 AEO 1994 6.8 6.9 6.9 7.0 7.1 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.4 7.5 7.5 7.5 7.5 AEO 1995 6.94 6.9 7.0 7.0 7.0 7.1 7.1 7.1 7.1 7.1 7.2 7.2 7.2 7.2 7.3 7.3 AEO 1996 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.5 7.6 7.6 7.7 7.7 7.8 7.9 8.0

374

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

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

and 2007 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

375

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

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

and 2008 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

376

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

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

and 2003 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

377

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

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

and 2009 Base Year)" ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,,,,"Texas Power Grid","Western Power Grid",,,," " ,"Projected Year...

378

,"Table 2a. Noncoincident Summer Peak Load, Actual and Projected...  

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

Base Year)",,,," " ,"Summer Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

379

,"Table 2b. Noncoincident Winter Peak Load, Actual and Projected...  

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

and 2004 Base Year)" ,"Winter Noncoincident Peak Load",,"Contiguous U.S. ","Eastern Power Grid",,,"Texas Power Grid","Western Power Grid" ,"Projected Year...

380

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

E-Print Network (OSTI)

of the Department of Energy's Office of Industrial Technologies, EIA extracted energy use infonnation from the Annual Energy Outlook (AEO) - 2000 (8) for each of the seven # The Pacific Northwest National Laboratory is operated by Battelle Memorial Institute...-6, 2000 NEMS The NEMS industrial module is the official forecasting model for EIA and thus the Department of Energy. For this reason, the energy prices and output forecasts used to drive the ITEMS model were taken from EIA's AEO 2000. Understanding...

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

Note: This page contains sample records for the topic "actual base forecast" 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

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

SciTech Connect

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

382

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect

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

383

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect

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

384

FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2  

SciTech Connect

For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters. The range is primarily due to uncertainties associated with the Tank Waste Remediation System (TWRS) program, including uncertainties regarding retrieval of long-length equipment, scheduling, and tank retrieval technologies.

Templeton, K.J.

1996-05-23T23:59:59.000Z

385

Adaptive Forecast-Based Monitoring for Dynamic Systems  

E-Print Network (OSTI)

process that makes plastic parts. The transition involvesa color change. In a laboratory setting, a single-screw extruder is used to per- form the color transition experiments. An on-line visual data acquisition

Nembhard, Harriet Black

386

Weather-based yield forecasts developed for 12 California crops  

E-Print Network (OSTI)

Climate Change Center at the Scripps Institution of Oceanography (M. Tyree, staff scientist, personal communication).

Lobell, David; Cahill, Kimberly Nicholas; Field, Christopher

2006-01-01T23:59:59.000Z

387

Weather Forecasting using GPU-based Large-Eddy Simulations  

Science Journals Connector (OSTI)

Since the advent of computers midway through the 20th century, computational resources have increased exponentially. It is likely they will continue to do so, especially when accounting for recent trends in multi-core processors. History has shown that ...

Jerôme Schalkwijk; Harmen J.J. Jonker; A. Pier Siebesma; Erik van Meijgaard

388

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

SciTech Connect

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

389

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

SciTech Connect

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

390

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

SciTech Connect

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

391

A Measurement Method of Actual Thermal Performance of Detached Houses  

E-Print Network (OSTI)

of residential houses based on field measurement (In Japanese), AIJ Report on Environmental engineering Vol.3, 1981 2) Martin Sandberg, J?rgen Eriksson: Commissioning of residential buildings in Sweden, IEA ECBCS Annex40 meetings held in Quebec, 2001/9, Doc...

Iwamae, A.; Nagai, H.; Miura, H.

2004-01-01T23:59:59.000Z

392

Numerical Simulation of 2010 Pakistan Flood in the Kabul River Basin by Using Lagged Ensemble Rainfall Forecasting  

Science Journals Connector (OSTI)

Lagged ensemble forecasting of rainfall and rainfall–runoff–inundation (RRI) forecasting were applied to the devastating flood in the Kabul River basin, the first strike of the 2010 Pakistan flood. The forecasts were performed using the Global ...

Tomoki Ushiyama; Takahiro Sayama; Yuya Tatebe; Susumu Fujioka; Kazuhiko Fukami

2014-02-01T23:59:59.000Z

393

Modelling and forecasting Oman crude oil prices using Box-Jenkins techniques  

Science Journals Connector (OSTI)

The Box-Jenkins' Auto Regressive Integrated Moving Average (ARIMA) modelling approach has been applied for the time series analysis of monthly average prices of Oman crude oil taken over a period of 10 years. Several seasonal and non-seasonal ARIMA models were identified. These models were then estimated and compared for their adequacy using the significance of the parameter estimates, mean square errors and Modified Box-Pierce (Ljung-Box) Chi-Square statistic. Based on these criterion a multiplicative seasonal model of the form ARIMA (1,1,5)x(1,1,1) was recommended for short term forecasting.

M.I. Ahmad

2012-01-01T23:59:59.000Z

394

Linear Diagnostics to Assess the Performance of an Ensemble Forecast System  

E-Print Network (OSTI)

. The mathematical model we adopt to predict the evolution of uncertainty in a local state estimate (analysis or forecast), xe, is based on the assumption that the error in the state estimate, ? = xe ? xt, (2.1) *Portions of this chapter have been reprinted from... variable. In Equation (2.1) xt is the model representation of the, in practice unknown, true state of the atmosphere. The covariance between the different components of ? is described by the error covariance matrix P`. We employ a K-member ensemble...

Satterfield, Elizabeth A.

2011-10-21T23:59:59.000Z

395

Expert Panel: Forecast Future Demand for Medical Isotopes | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and stimulate biomedical research. The expert panel recommends that the U.S. government build this capability around either a reactor, an accelerator or a combination of both technologies as long as isotopes for clinical and research applications can be supplied reliably, with diversity in adequate

396

Forecasting correlated time series with exponential smoothing models  

Science Journals Connector (OSTI)

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples.

Ana Corberán-Vallet; José D. Bermúdez; Enriqueta Vercher

2011-01-01T23:59:59.000Z

397

Application of GIS on forecasting water disaster in coal mines  

SciTech Connect

In many coal mines of China, water disasters occur very frequently. It is the most important problem that water gets inrush into drifts and coal faces, locally known as water gush, during extraction and excavation. Its occurrence is controlled by many factors such as geological, hydrogeological and mining technical conditions, and very difficult to be predicted and prevented by traditional methods. By making use of overlay analysis of Geographic Information System, a multi-factor model can be built to forecast the potential of water gush. This paper introduced the method of establishment of the water disaster forecasting system and forecasting model and two practical successful cases of application in Jiaozuo and Yinzhuang coal mines. The GIS proved helpful for ensuring the safety of coal mines.

Sun Yajun; Jiang Dong; Ji Jingxian [China Univ. of Mining and Technology, Jiangshy (China)] [and others

1996-08-01T23:59:59.000Z

398

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

NLE Websites -- All DOE Office Websites (Extended Search)

Energy Forecasting and Modeling Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Geraly Amador Clayton Barrows Greg Brinkman Brian W Bush Stuart Cohen Carolyn Davidson Paul Denholm Victor Diakov Aron Dobos Easan Drury Kelly Eurek Janine Freeman Marissa Hummon Jennie Jorganson Jordan Macknick Trieu Mai David Mulcahy David Palchak Ben Sigrin Daniel Steinberg Patrick Sullivan Aaron Townsend Laura Vimmerstedt Andrew Weekley Owen Zinaman Photo of Clayton Barrows. Clayton Barrows Postdoctoral Researcher Areas of expertise Power system modeling Primary research interests Power and energy systems

399

CCPP-ARM Parameterization Testbed Model Forecast Data  

DOE Data Explorer (OSTI)

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

Klein, Stephen

400

Sales forecasting strategies for small businesses: an empirical investigation of statistical and judgemental methods  

Science Journals Connector (OSTI)

This study evolved from the mixed results shown in the reviewed forecasting literature and from the lack of sufficient forecasting research dealing with micro data. The main purpose of this study is to investigate and compare the accuracy of different quantitative and qualitative forecasting techniques, and to recommend a forecasting strategy for small businesses. Emphasis is placed on the testing of combining as a tool to improve forecasting accuracy. Of particular interest is whether combining time series and judgemental forecasts provides more accurate results than individual methods. A case study of a small business was used for this purpose to assess the accuracy and applicability of combining forecasts. The evidence indicates that combining qualitative and quantitative methods results in better and improved forecasts.

Imad J. Zbib

2006-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

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

E-Print Network (OSTI)

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

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

402

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

E-Print Network (OSTI)

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

Ganguly, Auroop Ratan

2002-01-01T23:59:59.000Z

403

A Comparison of Measures-Oriented and Distributions-Oriented Approaches to Forecast Verification  

Science Journals Connector (OSTI)

The authors have carried out verification of 590 12–24-h high-temperature forecasts from numerical guidance products and human forecasters for Oklahoma City, Oklahoma, using both a measures-oriented verification scheme and a distributions-...

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

404

Improving Seasonal Forecast Skill of North American Surface Air Temperature in Fall Using a Postprocessing Method  

Science Journals Connector (OSTI)

A statistical postprocessing approach is applied to seasonal forecasts of surface air temperatures (SAT) over North America in fall, when the original uncalibrated predictions have little skill. The data used are ensemble-mean seasonal forecasts ...

XiaoJing Jia; Hai Lin; Jacques Derome

2010-05-01T23:59:59.000Z

405

Computing electricity spot price prediction intervals using quantile regression and forecast averaging  

Science Journals Connector (OSTI)

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity

Jakub Nowotarski; Rafa? Weron

2014-08-01T23:59:59.000Z

406

Price Forecasting and Optimal Operation of Wholesale Customers in a Competitive Electricity Market.  

E-Print Network (OSTI)

??This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning… (more)

Zareipour, Hamidreza

2006-01-01T23:59:59.000Z

407

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

SciTech Connect

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

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

408

Combining Multi Wavelet and Multi NN for Power Systems Load Forecasting  

Science Journals Connector (OSTI)

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series ... introduced. In addition, multi neural network for load forecast including...

Zhigang Liu; Qi Wang; Yajun Zhang

2008-01-01T23:59:59.000Z

409

Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs  

E-Print Network (OSTI)

Production forecasting in shale (ultra-low permeability) gas reservoirs is of great interest due to the advent of multi-stage fracturing and horizontal drilling. The well renowned production forecasting model, Arps? Hyperbolic Decline Model...

Statton, James Cody

2012-07-16T23:59:59.000Z

410

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

NLE Websites -- All DOE Office Websites (Extended Search)

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

411

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height  

Science Journals Connector (OSTI)

The Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model simulations with ...

Adam J. Deppe; William A. Gallus Jr.; Eugene S. Takle

2013-02-01T23:59:59.000Z

412

E-Print Network 3.0 - actuales relacionadas con Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

for: actuales relacionadas con Page: << < 1 2 3 4 5 > >> 1 Departamento de Fsica (EPS) Universidad Carlos III de Madrid Summary: fsica relacionada con la implosin de los...

413

E-Print Network 3.0 - actuales clasificaciones del Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

Collection: Mathematics 30 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

414

E-Print Network 3.0 - actuales del sector Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

Collection: Engineering 60 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

415

Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance  

Science Journals Connector (OSTI)

Modeling the lead-time demand for the multiple slow-moving inventory items in the case when the available requests history is very short is a challenge for inventory management. The classical forecasting technique, which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity, is known to lead to very poor performance in many cases important for industrial applications. An alternative approach to the demand forecasting for the considered problem is based on the Bayesian paradigm, when the initially developed population-averaged demand probability distribution is modified for each item using its specific requests history. This paper follows this approach and presents a new model, which relies on the beta distribution as a prior for the request probability, and allows to account for disparity in variance of demand between different stock keeping units. To estimate the model parameters, a special computationally effective technique based on the generalized method of moments is developed. Simulation results indicate the superiority of the proposed model over the known ones, while the computational burden does not increase.

Alexandre Dolgui; Maksim Pashkevich

2008-01-01T23:59:59.000Z

416

Improving the forecasting function for a Credit Hire operator in the UK  

Science Journals Connector (OSTI)

This study aims to test on the predictability of Credit Hire services for the automobile and insurance industry. A relatively sophisticated time series forecasting procedure, which conducts a competition among exponential smoothing models, is employed to forecast demand for a leading UK Credit Hire operator (CHO). The generated forecasts are compared against the Naive method, resulting that demand for CHO services is indeed extremely hard to forecast, as the underlying variable is the number of road accidents – a truly stochastic variable.

Nicolas D. Savio; K. Nikolopoulos; Konstantinos Bozos

2009-01-01T23:59:59.000Z

417

Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities  

SciTech Connect

The report addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America.

Porter, K.; Rogers, J.

2009-12-01T23:59:59.000Z

418

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

E-Print Network (OSTI)

1 Next Generation Short-Term Forecasting of Wind Power ­ Overview of the ANEMOS Project. G outperform current state-of-the-art methods, for onshore and offshore wind power forecasting. Advanced forecasts for the power system management and market integration of wind power. Keywords: Wind power, short

Boyer, Edmond

419

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

E-Print Network (OSTI)

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

Abu-Mostafa, Yaser S.

420

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

Note: This page contains sample records for the topic "actual base forecast" 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

VALIDATION OF SHORT AND MEDIUM TERM OPERATIONAL SOLAR RADIATION FORECASTS IN THE US  

E-Print Network (OSTI)

, and medium term forecasts (up to seven days ahead) from numerical weather prediction models [1]. Forecasts radiation forecasting. One approach relies on numerical weather prediction (NWP) models which can be global modeling of the atmosphere. NWP models cannot, at this stage of their development, predict the exact

Perez, Richard R.

422

Products and Service of Center for Weather Forecast and Climate Studies  

E-Print Network (OSTI)

) Seasonal Climate Forecast (1-6 months) #12;Weather Forecast Weather Bulletin PCD SCD1 SCD2 SX6 SatelliteLOG O Products and Service of Center for Weather Forecast and Climate Studies Simone Sievert da AND DEVELOP. DIVISION SATELLITE DIVISION ENVIROM. SYSTEM OPERATIONAL DIVISION CPTEC/INPE Msc / PHD &TRAINING

423

Lessons from Deploying NLG Technology for Marine Weather Forecast Text Generation  

E-Print Network (OSTI)

model along with other sources of weather data such as satellite pictures and their own forecastingLessons from Deploying NLG Technology for Marine Weather Forecast Text Generation Somayajulu G Language Generation (NLG) system that produces textual weather forecasts for offshore oilrigs from

Sripada, Yaji

424

WasteStreamForecast2010.xls  

Office of Environmental Management (EM)

Reporting Reporting Site Disposition Facility Field Stream ID Actual Dispos 2009 Starting Inventory 2010 2010 2011 2012 2013 2014 2015 to 2019 2020 to 2024 2025 to 2029 2030 to 2034 2035 to 2039 2040-50 1 Ames Energy Solutions-Clive (formerly Envirocare) 8020-01 0.00 0.00 0.00 0.00 0.00 20.00 0.00 0.00 20.00 20.00 20.00 0.00 60.00 2 Argonne Area 5 LLW Disposal Unit (NTS) AEL105DOEa 55.12 50.45 72.36 29.22 29.22 29.22 29.22 29.22 0.00 0.00 0.00 0.00 0.00 3 Argonne Area 5 LLW Disposal Unit (NTS) AEL106DOEa 0.38 0.07 0.09 0.21 0.21 0.21 0.21 0.21 0.00 0.00 0.00 0.00 0.00 4 Argonne Area 5 LLW Disposal Unit (NTS) AE-L104DOE 0.19 10.85 11.19 0.42 0.42 0.42 0.42 0.42 0.00 0.00 0.00 0.00 0.00 5 Argonne Area 5 LLW Disposal Unit (NTS) AEL103DOE 74.13 87.37 110.16 30.39 30.39 30.39 30.39 30.39 0.00 0.00 0.00 0.00 0.00 6 Argonne Area 5 LLW Disposal Unit (NTS)

425

FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2  

SciTech Connect

For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

Valero, O.J.

1996-04-23T23:59:59.000Z

426

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network (OSTI)

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts Antonio that the inherent variability in wind power generation and the related difficulty in predicting future generation profiles, raise major challenges to wind power integration into the electricity grid. In this work we study

Giannitrapani, Antonello

427

Does Money Matter in Inflation Forecasting? JM Binner 1  

E-Print Network (OSTI)

1 Does Money Matter in Inflation Forecasting? JM Binner 1 P Tino 2 J Tepper 3 R Anderson4 B Jones 5 range of different definitions of money, including different methods of aggregation and different that there exists a long-run relationship between the growth rate of the money supply and the growth rate of prices

Tino, Peter

428

Detecting and Forecasting Economic Regimes in Automated Exchanges  

E-Print Network (OSTI)

, such as over- supply or scarcity, from historical data using computational methods to construct price density. The agent can use this information to make both tactical decisions such as pricing and strategic decisions historical data and identified from observable data. We outline how to identify regimes and forecast regime

Ketter, Wolfgang

429

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

430

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

431

A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE  

E-Print Network (OSTI)

A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE Wensheng Zhang1,* , Hongfu Chen1 and excessive fluctuation of agricultural and livestock products price is not only harmful to residents' living, but also affects CPI (Consumer Price Index) values, and even leads to social crisis, which influences

Boyer, Edmond

432

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network (OSTI)

) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model conditioning (HVAC) systems. In particular, if occupancy can be accurately pre- dicted, HVAC systems can potentially be controlled to op- erate more efficiently. For example, an HVAC system can pre-heat or pre

Hoff, William A.

433

Forecasting Hospital Bed Availability Using Simulation and Neural Networks  

E-Print Network (OSTI)

Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels is a critical factor for decision-making in hospitals. Bed availability (or alternatively the bed occupancy in emergency departments, and many other important hospital decisions. To better enable a hospital to make

Kuhl, Michael E.

434

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network (OSTI)

Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy Department of Computer Science University of Massachusetts Amherst Amherst, Massachusetts 01003 {nksharma,pranshus,irwin,shenoy}@cs.umass.edu Abstract--A key goal

Shenoy, Prashant

435

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect

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

436

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 in Porto) Power Systems Unit Porto, Portugal Industry Partners Horizon Wind Energy, LLC Midwest Independent

Kemner, Ken

437

Power load forecasting using data mining and knowledge discovery technology  

Science Journals Connector (OSTI)

Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.

Yongli Wang; Dongxiao Niu; Ling Ji

2011-01-01T23:59:59.000Z

438

What constrains spread growth in forecasts ini2alized from  

E-Print Network (OSTI)

1 What constrains spread growth in forecasts ini2alized from ensemble Kalman filters? Tom from manner in which ini2al condi2ons are generated, some due to the model (e.g., stochas2c physics as error; part of spread growth from manner in which ini2al condi2ons are generated, some due

Hamill, Tom

439

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

440

Introduction An important goal in operational weather forecasting  

E-Print Network (OSTI)

sensitive areas. To answer these questions simulation experiments with state-of-the-art numerical weather prediction (NWP) models have proved great value to test future meteorological observing systems a priori102 Introduction An important goal in operational weather forecasting is to reduce the number

Haak, Hein

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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.
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441

Operational Forecasts of Cloud Cover and Water Vapour  

E-Print Network (OSTI)

of the forecast programme, which involved the additional use of 10.7 µm GOES-8 satellite data and surface weather cirrus cloud cover 15 5. A satellite-derived extinction parameter 17 5.1 Background 17 5.2 Previous work 20 5.3 Continued development of a satellite-derived 22 extinction parameter 6. Suggestions

442

Increasing NOAA's computational capacity to improve global forecast modeling  

E-Print Network (OSTI)

competing numerical weather prediction centers such as the European Center for MediumRange Weather Forecasts (ECMWF). For most sensibleweather metrics, we lag 1 to 1.5 days (i.e., they make a 3.5day of NOAA's current investment in weather satellites. Without a modern data assimilation system

Hamill, Tom

443

Measuring forecast skill: is it real skill or  

E-Print Network (OSTI)

samples, then many verification metrics will credit a forecast with extra skill it doesn't deserve islands, zero meteorologists Imagine a planet with a global ocean and two isolated islands. Weather three metrics... (1) Brier Skill Score (2) Relative Operating Characteristic (3) Equitable Threat Score

Hamill, Tom

444

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA  

E-Print Network (OSTI)

Perceptron; Ozone concentration. 1. Introduction Tropospheric ozone is a major air pollution problem, both, Ajaccio, France, e-mail: balu@univ-corse.fr Abstract: Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air

Boyer, Edmond

445

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

446

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

447

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network (OSTI)

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study ON EXTREMES · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes (Germany, France) Conventional Methods (3) Extreme Value Theory (EVT) (4) Application of EVT to Verification (5) Frost

Katz, Richard

448

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network (OSTI)

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study on Extremes · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes "Il est impossible que l'improbable n'arrive jamais." #12;3 OUTLINE (1) Motivation (2) Conventional Methods (3) Extreme

Katz, Richard

449

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network (OSTI)

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study ON EXTREMES · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes "Il est impossible que l'improbable n'arrive jamais." #12;3 OUTLINE (1) Motivation (2) Conventional Methods (3) Extreme

Katz, Richard

450

Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe  

E-Print Network (OSTI)

Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe Matthew J. Swann;Abstract Flood and wind damage to property and livelihoods resulting from extreme precipitation events variability of these extreme events can be closely related to the large-scale atmospheric circulation

Feigon, Brooke

451

Use of wind power forecasting in operational decisions.  

SciTech Connect

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

452

An evaluation of forecasting methods for aircraft non-routine maintenance material demand  

Science Journals Connector (OSTI)

Aircraft maintenance can be divided into routine and non-routine activities. Material demand associated with non-routine maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-routine maintenance material demand. Non-routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-routine maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-routine maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate maintenance C-check.

Maarten Zorgdrager; Wim J.C. Verhagen; Richard Curran

2014-01-01T23:59:59.000Z

453

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

SciTech Connect

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

454

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

455

Economic evaluation of a residential photovoltaic system based on a probability model using actual meteorological data  

SciTech Connect

To design a photovoltaic (PV) generation system economically, it is necessary to use date of the total insolation on a horizontal surface. However, such data is only the total daily values and does not represent the power variation caused by the cloud cover. This paper presents the probability method which represents not only the average but also the variance of the PV generation power, and shows simulated results using this methodology. This study's results indicate that the distribution of the PV power divided by the estimated value of the total insolation on a tilted surface is similar to a normal distribution and that a residential (privately-owned) system without storage, whose PV capacity is more than 2 kWp, has little effect upon the reduction of the energy of an average Japanese household.

Sutoh, T.; Suzuki, H.; Sekine, Y.

1987-03-01T23:59:59.000Z

456

Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts  

SciTech Connect

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles. Using data collected from Volts enrolled in The EV Project, this paper compares the real-world UF of two groups of Chevrolet Volts to estimated UF's based on J2841. The actual observed fleet utility factors (FUF) for the MY2011/2012 and MY2013 Volt groups studied were observed to be 72% and 74%, respectively. Using the EPA CD ranges, the method prescribed by J2841 estimates a FUF of 65% and 68% for the MY2011/2012 and MY2013 Volt groups, respectively. Volt drivers achieved higher percentages of distance traveled in EV mode for two reasons. First, they had fewer long-distance travel days than drivers in the national travel survey referenced by J2841. Second, they charged more frequently than the J2841 assumption of once per day - drivers of Volts in this study averaged over 1.4 charging events per day. Although actual CD range varied widely as driving conditions varied, the average CD ranges for the two Volt groups studied matched the EPA CD range estimates, so CD range variation did not affect FUF results.

John Smart; Thomas Bradley; Stephen Schey

2014-04-01T23:59:59.000Z

457

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

SciTech Connect

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 (US). Dept. of Physics; Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. [Argonne National Lab., IL (US)

1993-05-01T23:59:59.000Z

458

Detiding DART buoy data for real-time extraction of source coefficients for operational tsunami forecasting  

E-Print Network (OSTI)

U.S. Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune source coefficients of tsunami forecast models. For accurate coefficients and therefore forecasts, tides at the buoys must be accounted for. In this study, five methods for coefficient estimation are compared, each of which accounts for tides differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 pre-existing harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 hrs of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate source coefficients after detiding. Method (5) estimates the coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from eleven...

Percival, Donald B; Eble, Marie C; Gica, Edison; Huang, Paul Y; Mofjeld, Harold O; Spillane, Michael C; Titov, Vasily V; Tolkova, Elena I

2014-01-01T23:59:59.000Z

459

Daily pollution forecast using optimal meteorological data at synoptic and local scales  

E-Print Network (OSTI)

We present a simple framework to easily pre-select the most essential data for accurately forecasting the concentration of the pollutant PM$_{10}$, based on pollutants observations for the years 2002 until 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several meteorological stations, we apply a forward stepwise regression procedure that enables us not only to identify the most important variables for forecasting the pollutant but also to rank them in order of importance. We argue the importance of this variable ranking, showing that the ranking is very sensitive to the urban spot where measurements are taken. Having this pre-selection, we then present the potential of linear and non-linear neural network models when applied to the concentration of pollutant PM$_{10}$. Similarly to previous studies for other pollutants, our validation results show that non-linear models in average perform as well or worse as linear models for PM$_{10}$. F...

Russo, Ana; Raischel, Frank; Trigo, Ricardo; Mendes, Manuel

2014-01-01T23:59:59.000Z

460

Forecasting sales and product evolution: The case of the hybrid/electric car  

Science Journals Connector (OSTI)

We present a model that forecasts sales and product evolution, based on data on market and industry, which can be collected before the product is introduced. Product evolution can be incremental but can also take place by releasing new generations. In our model adoption of a new product is motivated by attribute improvements (enabled by technology evolution), and firms' attribute improvements strategies are motivated by market growth and directed by market preferences. The interdependency between attributes' improvements and cumulative adoption level makes the problem inherently dynamic. The dependency of attribute levels on adoption levels is assessed using industry and technology analysis. Market preferences and purchase intention response to attribute levels changes are assessed based on a conjoint study. The option of collecting and interpreting data about both demand and supply aspects, before the new product is introduced, enables us to estimate sales and technology progress endogenously rather than to require them as inputs. We demonstrate the method on the hybrid car market.

Yair Orbach; Gila E. Fruchter

2011-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "actual base forecast" 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

Holt’s exponential smoothing and neural network models for forecasting interval-valued time series  

Science Journals Connector (OSTI)

Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively. In Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval-valued stock market time series.

André Luis Santiago Maia; Francisco de A.T. de Carvalho

2011-01-01T23:59:59.000Z

462

A Non Parametric Model for the Forecasting of the Venezuelan Oil Prices  

E-Print Network (OSTI)

A neural net model for forecasting the prices of Venezuelan crude oil is proposed. The inputs of the neural net are selected by reference to a dynamic system model of oil prices by Mashayekhi (1995, 2001) and its performance is evaluated using two criteria: the Excess Profitability test by Anatoliev and Gerko (2005) and the characteristics of the equity curve generated by a trading strategy based on the neural net predictions. ----- Se introduce aqui un modelo no parametrico para pronosticar los precios del petroleo Venezolano cuyos insumos son seleccionados en base a un sistema dinamico que explica los precios en terminos de dichos insumos. Se describe el proceso de recoleccion y pre-procesamiento de datos y la corrida de la red y se evaluan sus pronosticos a traves de un test estadistico de predictibilidad y de las caracteristicas del Equity Curve inducido por la estrategia de compraventa bursatil generada por dichos pronosticos.

Costanzo, Sabatino; Dehne, Wafaa; Prato, Hender

2007-01-01T23:59:59.000Z

463

BENCH-SCALE STEAM REFORMING OF ACTUAL TANK 48H WASTE  

SciTech Connect

Fluidized Bed Steam Reforming (FBSR) has been demonstrated to be a viable technology to remove >99% of the organics from Tank 48H simulant, to remove >99% of the nitrate/nitrite from Tank 48H simulant, and to form a solid product that is primarily carbonate based. The technology was demonstrated in October of 2006 in the Engineering Scale Test Demonstration Fluidized Bed Steam Reformer1 (ESTD FBSR) at the Hazen Research Inc. (HRI) facility in Golden, CO. The purpose of the Bench-scale Steam Reformer (BSR) testing was to demonstrate that the same reactions occur and the same product is formed when steam reforming actual radioactive Tank 48H waste. The approach used in the current study was to test the BSR with the same Tank 48H simulant and same Erwin coal as was used at the ESTD FBSR under the same operating conditions. This comparison would allow verification that the same chemical reactions occur in both the BSR and ESTD FBSR. Then, actual radioactive Tank 48H material would be steam reformed in the BSR to verify that the actual tank 48H sample reacts the same way chemically as the simulant Tank 48H material. The conclusions from the BSR study and comparison to the ESTD FBSR are the following: (1) A Bench-scale Steam Reforming (BSR) unit was successfully designed and built that: (a) Emulated the chemistry of the ESTD FBSR Denitration Mineralization Reformer (DMR) and Carbon Reduction Reformer (CRR) known collectively as the dual reformer flowsheet. (b) Measured and controlled the off-gas stream. (c) Processed real (radioactive) Tank 48H waste. (d) Met the standards and specifications for radiological testing in the Savannah River National Laboratory (SRNL) Shielded Cells Facility (SCF). (2) Three runs with radioactive Tank 48H material were performed. (3) The Tetraphenylborate (TPB) was destroyed to > 99% for all radioactive Bench-scale tests. (4) The feed nitrate/nitrite was destroyed to >99% for all radioactive BSR tests the same as the ESTD FBSR. (5) The radioactive Tank 48H DMR product was primarily made up of soluble carbonates. The three most abundant species were thermonatrite, [Na{sub 2}CO{sub 3} {center_dot} H{sub 2}O], sodium carbonate, [Na{sub 2}CO{sub 3}], and trona, [Na{sub 3}H(CO{sub 3}){sub 2} {center_dot} 2H{sub 2}O] the same as the ESTD FBSR. (6) Insoluble solids analyzed by X-Ray Diffraction (XRD) did not detect insoluble carbonate species. However, they still may be present at levels below 2 wt%, the sensitivity of the XRD methodology. Insoluble solids XRD characterization indicated that various Fe/Ni/Cr/Mn phases are present. These crystalline phases are associated with the insoluble sludge components of Tank 48H slurry and impurities in the Erwin coal ash. The percent insoluble solids, which mainly consist of un-burnt coal and coal ash, in the products were 4 to 11 wt% for the radioactive runs. (7) The Fe{sup +2}/Fe{sub total} REDOX measurements ranged from 0.58 to 1 for the three radioactive Bench-scale tests. REDOX measurements > 0.5 showed a reducing atmosphere was maintained in the DMR indicating that pyrolysis was occurring. (8) Greater than 90% of the radioactivity was captured in the product for all three runs. (9) The collective results from the FBSR simulant tests and the BSR simulant tests indicate that the same chemistry occurs in the two reactors. (10) The collective results from the BSR simulant runs and the BSR radioactive waste runs indicates that the same chemistry occurs in the simulant as in the real waste. The FBSR technology has been proven to destroy the organics and nitrates in the Tank 48H waste and form the anticipated solid carbonate phases as expected.

Burket, P; Gene Daniel, G; Charles Nash, C; Carol Jantzen, C; Michael Williams, M

2008-09-25T23:59:59.000Z

464

XAFS Study of Phase-Change Recording Material Using Actual Media  

Science Journals Connector (OSTI)

The influence of the interface layer to the local structure for atomic arrangement of a GeBiTe phase-change material was investigated by using XAFS on the actual rewritable HD DVD...

Nakai, Tsukasa; Yoshiki, Masahiko; Satoh, Yasuhiro

465

E-Print Network 3.0 - actual del ultrasonido Sample Search Results  

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Summary: : evolucin histrica y situacin actual. 8 l) Evaluacin de la capacidad de carga del Parque para los... Proyectos A lo largo del ao 2010 han estado vigentes 85...

466

E-Print Network 3.0 - anciano consideraciones actuales Sample...  

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mitigacin de los efectos del cambio climtico y con... polticas De proseguir las emisiones de GEI a una tasa igual o superior a la actual, el calentamiento Source: Binette,...

467

E-Print Network 3.0 - actual terrestrial rabies Sample Search...  

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and Information Sciences 56 innovati nNREL Advances a Unique Crystalline Silicon Solar Cell Summary: actually begins at another of the U.S. Department of Energy (DOE)...

468

E-Print Network 3.0 - actual del huemul Sample Search Results  

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and Information Sciences 88 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

469

E-Print Network 3.0 - actual del franciscanismo Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 75 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

470

E-Print Network 3.0 - actual del control Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 30 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

471

E-Print Network 3.0 - actual del tabaquismo Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 91 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

472

E-Print Network 3.0 - actual del no-acceso Sample Search Results  

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and Information Sciences 73 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

473

E-Print Network 3.0 - actual del rabdomiosarcoma Sample Search...  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 74 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

474

E-Print Network 3.0 - actual del estreptococo Sample Search Results  

NLE Websites -- All DOE Office Websites (Extended Search)

and Information Sciences 80 MTODO DE CENSO Y ESTIMA DE POBLACIN DEL PINZN AZUL DE GRAN CANARIA Summary: distribucin actual de la especie en Inagua, Ojeda y Pajonales. El...

475

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

Energy.gov (U.S. Department of Energy (DOE))

The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored research project whose overarching goals are to improve the accuracy of short-term wind energy forecasts, and to demonstrate the economic value of these improvements.

476

Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir  

E-Print Network (OSTI)

CONTINUOUS MODEL UPDATING AND FORECASTING FOR A NATURALLY FRACTURED RESERVOIR A Thesis by HISHAM HASSAN S. ALMOHAMMADI Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... guidance and support throughout my time here in Texas A&M University. I also would like to thank my committee members, Dr. Eduardo Gildin and Dr. Michael Sherman, for providing valued insight and help during the course of this research. I am indebted...

Almohammadi, Hisham

2013-07-26T23:59:59.000Z

477

NOAA National Weather Service I'm a weather forecaster.  

E-Print Network (OSTI)

.S.D EPARTMENT OF COM M ERCE How Do You Make a Weather Satellite? How Do You Make a Weather Satellite? #12;Well you put a truck in orbit? So it can carry all the things needed to make a working weather satelliteNOAA National Weather Service I'm a weather forecaster. I need to see clouds and storms from way up

Waliser, Duane E.

478

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

SciTech Connect

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

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

2011-08-15T23:59:59.000Z

479

A Sensitivity Study of Building Performance Using 30-Year Actual Weather  

NLE Websites -- All DOE Office Websites (Extended Search)

Sensitivity Study of Building Performance Using 30-Year Actual Weather Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Title A Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Publication Type Conference Paper Year of Publication 2013 Authors Hong, Tianzhen, Wen-Kuei Chang, and Hung-Wen Lin Date Published 05/2013 Keywords Actual meteorological year, Building simulation, Energy use, Peak electricity demand, Typical meteorological year, Weather data Abstract Traditional energy performance calculated using building simulation with the typical meteorological year (TMY) weather data represents the energy performance in a typical year but not necessarily the average or typical energy performance of a building in long term. Furthermore, the simulated results do not provide the range of variations due to the change of weather, which is important in building energy management and risk assessment of energy efficiency investment. This study analyzes the weather impact on peak electric demand and energy use by building simulation using 30-year actual meteorological year (AMY) weather data for three types of office buildings at two design efficiency levels across all 17 climate zones. The simulated results from the AMY are compared to those from TMY3 to determine and analyze the differences. It was found that yearly weather variation has significant impact on building performance especially peak electric demand. Energy savings of building technologies should be evaluated using simulations with multi-decade actual weather data to fully consider investment risk and the long term performance.

480

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

G: Key Assumptions for the IEO2005 Kyoto Protocol Case G: Key Assumptions for the IEO2005 Kyoto Protocol Case Energy-Related Emissions of Greenhouse Gases The System for the Analysis of Global energy Markets (SAGE)—the model used by the Energy Information Administration (EIA) to prepare the International Energy Outlook 2005 (IEO2005) mid-term projections—does not include non-energy-related emissions of greenhouse gases, which are estimated at about 15 to 20 percent of total greenhouse gas emissions, based on inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). SAGE models global energy supply and demand and, therefore, does not address agricultural and other non-energy-related emissions. EIA implicitly assumes that percentage reductions of non-energy-related emissions and their associated abatement costs will be similar to those for energy-related emissions. Non-energy-related greenhouse gas emissions are likely to grow faster than energy-related emissions; however, the marginal abatement costs for non-energy-related greenhouse gas emissions are not known and cannot be estimated reliably. In SAGE, each region’s emissions reduction goal under the Kyoto Protocol is based only on the corresponding estimate of that region’s energy-related carbon dioxide emissions, as determined by EIA data. It is assumed that the required reductions will also be proportionately less than if all gases were included.

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481

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

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. Minh Le Minh Le Program Manager, Solar Program What Do These Projects Do? The Energy Department is investing $8 million in two cutting-edge projects to increase the accuracy of solar forecasting at sub-hourly,

482

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach  

Science Journals Connector (OSTI)

Abstract A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both least squares support vector machine (LSSVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. The proposed hybrid model showed improved forecasting accuracy compared to a forecasting model using a single LSSVM.

Xing Yan; Nurul A. Chowdhury

2013-01-01T23:59:59.000Z

483

Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition  

SciTech Connect

The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

Rogers, J.; Porter, K.

2011-03-01T23:59:59.000Z

484

Comparison of Airbus, Boeing, Rolls-Royce and AVITAS market forecasts  

Science Journals Connector (OSTI)

Forecasts of future world demand for commercial aircraft are published fairly regularly by Airbus and Boeing. Other players in the aviation business, Rolls Royce and AVITAS, have also published forecasts in the past year. This article analyses and compares the methods used and assumptions made by the several forecasters. It concludes that there are wide areas of similarity in the approaches used and highlights the most significant area of divergence.

Ralph Anker

2000-01-01T23:59:59.000Z

485

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

E-Print Network (OSTI)

to measure various aspects of the California redwood lumber industry. The first sought to explain the economic struc- ture of the short-run market for redwood lumber by preparing short-range forecasts of price, new orders, shipments, produc- tion, stocks... regression coefficients (20) . The second study was directed at developing a short-run forecast of new orders for redwood lumber (21) . Several forecasting techniques were developed, but econometrics, i. e. , multiple regression analysis, provided...

Jackson, Ben Douglas

2012-06-07T23:59:59.000Z

486

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

I: System for the Analysis of Global Energy Markets (SAGE) I: System for the Analysis of Global Energy Markets (SAGE) The projections of world energy consumption appearing in this yearÂ’s International Energy Outlook (IEO) are based on the Energy Information AdministrationÂ’s (EIAÂ’s) international energy modeling tool, System for the Analysis of Global Energy markets (SAGE). SAGE is an integrated set of regional models that provide a technology-rich basis for estimating regional energy consumption. For each region, reference case estimates of 42 end-use energy service demands (e.g., car, commercial truck, and heavy truck road travel; residential lighting; steam heat requirements in the paper industry) are developed on the basis of economic and demographic projections. Projections of energy consumption to meet the energy demands are estimated on the basis of each regionÂ’s existing energy use patterns, the existing stock of energy-using equipment, and the characteristics of available new technologies, as well as new sources of primary energy supply.

487

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Preface Preface This report presents international energy projections through 2025, prepared by the Energy Information Administration, including outlooks for major energy fuels and associated carbon dioxide emissions. The International Energy Outlook 2005 (IEO2005) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets through 2025. U.S. projections appearing in IEO2005 are consistent with those published in EIAÂ’s Annual Energy Outlook 2005 (AEO2005), which was prepared using the National Energy Modeling System (NEMS). Although the IEO typically uses the same reference case as the AEO, IEO2005 has adopted the October futures case from AEO2005 as its reference case for the United States. The October futures case, which has an assumption of higher world oil prices than the AEO2005 reference case, now appears to be a more likely projection. The reference case prices will be reconsidered for the next AEO. Based on information available as of July 2005, the AEO2006 reference case will likely reflect world oil prices higher than those in the IEO2005 reference case.

488

Experimental evaluation of actual delivered dose using mega-voltage cone-beam CT and direct point dose measurement  

SciTech Connect

Radiation therapy in patients is planned by using computed tomography (CT) images acquired before start of the treatment course. Here, tumor shrinkage or weight loss or both, which are common during the treatment course for patients with head-and-neck (H and N) cancer, causes unexpected differences from the plan, as well as dose uncertainty with the daily positional error of patients. For accurate clinical evaluation, it is essential to identify these anatomical changes and daily positional errors, as well as consequent dosimetric changes. To evaluate the actual delivered dose, the authors proposed direct dose measurement and dose calculation with mega-voltage cone-beam CT (MVCBCT). The purpose of the present study was to experimentally evaluate dose calculation by MVCBCT. Furthermore, actual delivered dose was evaluated directly with accurate phantom setup. Because MVCBCT has CT-number variation, even when the analyzed object has a uniform density, a specific and simple CT-number correction method was developed and applied for the H and N site of a RANDO phantom. Dose distributions were calculated with the corrected MVCBCT images of a cylindrical polymethyl methacrylate phantom. Treatment processes from planning to beam delivery were performed for the H and N site of the RANDO phantom. The image-guided radiation therapy procedure was utilized for the phantom setup to improve measurement reliability. The calculated dose in the RANDO phantom was compared to the measured dose obtained by metal-oxide-semiconductor field-effect transistor detectors. In the polymethyl methacrylate phantom, the calculated and measured doses agreed within about +3%. In the RANDO phantom, the dose difference was less than +5%. The calculated dose based on simulation-CT agreed with the measured dose within±3%, even in the region with a high dose gradient. The actual delivered dose was successfully determined by dose calculation with MVCBCT, and the point dose measurement with the image-guided radiation therapy procedure.

Matsubara, Kana, E-mail: matsubara-kana@hs.tmu.ac.jp [Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa-ku Tokyo (Japan); Kohno, Ryosuke [National Cancer Center Hospital East, Chiba (Japan); National Cancer Center Research Institute, Chiba (Japan); Nishioka, Shie; Shibuya, Toshiyuki; Ariji, Takaki; Akimoto, Tetsuo [National Cancer Center Hospital East, Chiba (Japan); Saitoh, Hidetoshi [Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa-ku Tokyo (Japan)

2013-07-01T23:59:59.000Z

489

Energy Department Announces $2.5 Million to Improve Wind Forecasting...  

Energy Savers (EERE)

better forecasts, wind energy plant operators and industry professionals can ensure wind turbines operate closer to maximum capacity, leading to lower energy costs for consumers....

490

Intra-hour wind power variability assessment using the conditional range metric : quantification, forecasting and applications.  

E-Print Network (OSTI)

??The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the… (more)

Boutsika, Thekla

2013-01-01T23:59:59.000Z

491

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

E-Print Network (OSTI)

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

Grimstad, Dan

2007-01-01T23:59:59.000Z

492

Study and implementation of mesoscale weather forecasting models in the wind industry.  

E-Print Network (OSTI)

?? As the wind industry is developing, it is asking for more reliable short-term wind forecasts to better manage the wind farms’ operations and electricity… (more)

Jourdier, Bénédicte

2012-01-01T23:59:59.000Z

493

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

SciTech Connect

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

494

A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting  

E-Print Network (OSTI)

is critical for coastal California solar forecasting.   affecting solar irradiance in southern California.   solar  photovoltaic generation (the southern California 

Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

2013-01-01T23:59:59.000Z

495

Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain  

Energy.gov (U.S. Department of Energy (DOE))

The DOE Wind Program has issued a Notice of Intent for a funding opportunity, tentatively titled Wind Forecasting Improvement Project in Complex Terrain.

496

Forecasting future oil production in Norway and the UK: a general improved methodology  

E-Print Network (OSTI)

We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 +/- 10 million barrels for Norway and 98 +/- 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

Fievet, Lucas; Cauwels, Peter; Sornette, Didier

2014-01-01T23:59:59.000Z

497

Using Google Flu Trends data in forecasting influenza-like–illness related ED visits in Omaha, Nebraska  

Science Journals Connector (OSTI)

AbstractIntroduction Emergency department (ED) visits increase during the influenza seasons. It is essential to identify statistically significant correlates in order to develop an accurate forecasting model for ED visits. Forecasting influenza-like–illness (ILI)-related ED visits can significantly help in developing robust resource management strategies at the EDs. Methods We first performed correlation analyses to understand temporal correlations between several predictors of ILI-related ED visits. We used the data available for Douglas County, the biggest county in Nebraska, for Omaha, the biggest city in the state, and for a major hospital in Omaha. The data set included total and positive influenza test results from the hospital (ie, Antigen rapid (Ag) and Respiratory Syncytial Virus Infection (RSV) tests); an Internet-based influenza surveillance system data, that is, Google Flu Trends, for both Nebraska and Omaha; total ED visits in Douglas County attributable to ILI; and ILI surveillance network data for Douglas County and Nebraska as the predictors and data for the hospital's ILI-related ED visits as the dependent variable. We used Seasonal Autoregressive Integrated Moving Average and Holt Winters methods with3 linear regression models to forecast ILI-related ED visits at the hospital and evaluated model performances by comparing the root means square errors (RMSEs). Results Because of strong positive correlations with ILI-related ED visits between 2008 and 2012, we validated the use of Google Flu Trends data as a predictor in an ED influenza surveillance tool. Of the 5 forecasting models we have tested, linear regression models performed significantly better when Google Flu Trends data were included as a predictor. Regression models including Google Flu Trends data as a predictor variable have lower RMSE, and the lowest is achieved when all other variables are also included in the model in our forecasting experiments for the first 5 weeks of 2013 (with RMSE = 57.61). Conclusions Google Flu Trends data statistically improve the performance of predicting ILI-related ED visits in Douglas County, and this result can be generalized to other communities. Timely and accurate estimates of ED volume during the influenza season, as well as during pandemic outbreaks, can help hospitals plan their ED resources accordingly and lower their costs by optimizing supplies and staffing and can improve service quality by decreasing ED wait times and overcrowding.

Ozgur M. Araz; Dan Bentley; Robert L. Muelleman

2014-01-01T23:59:59.000Z

498

Forecast of Contracting and Subcontracting Opportunities, Fiscal year 1995  

SciTech Connect

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

499

Gasoline direct injection: Actual trends and future strategies for injection and combustion systems  

SciTech Connect

Recent developments have raised increased interest on the concept of gasoline direct injection as the most promising future strategy for fuel economy improvement of SI engines. The general requirements for mixture preparation and combustion systems in a GDI engine are presented in view of known and actual systems regarding fuel economy and emission potential. The characteristics of the actually favored injection systems are discussed and guidelines for the development of appropriate combustion systems are derived. The differences between such mixture preparation strategies as air distributed fuel and fuel wall impingement are discussed, leading to the alternative approach to the problem of mixture preparation with the fully air distributing concept of direct mixture injection.

Fraidl, G.K.; Piock, W.F.; Wirth, M.

1996-09-01T23:59:59.000Z

500

The outlook for Operations Research: will business education supply enough management science new entrants to meet forecast demand  

Science Journals Connector (OSTI)

Can Management Science in Business Education become sufficiently popular to fill forecast demands for new entrants to its Operations Research (OR) subset? Based upon papers by numerous authors, this paper identifies an interesting phenomenon â?? an increasingly applicable field of Management Science plagued by students avoiding entry. This paper discusses the results of an examination of this phenomenon's background, provides data collected concerning current supply of and projected demand for new entrants in a subset of Management Science; examines the continuing call for new approaches to teaching Management Science as a means of attracting new entrants; and presents continued research suggestions.

Richard A. McMahon; Peter D. DeVries

2012-01-01T23:59:59.000Z