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1

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

2

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

3

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

4

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

5

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

6

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.

7

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

8

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.

9

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

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

10

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....

11

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

12

Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market  

SciTech Connect

For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

2008-11-15T23:59:59.000Z

13

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 90min) 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

14

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

15

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

16

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.

17

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

18

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

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

19

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

E-Print Network (OSTI)

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

Abdel-Aal, Radwan E.

20

Development of short-term forecast quality for new offshore wind farms  

Science Journals Connector (OSTI)

As the rapid wind power build-out continues, a large number of new wind farms will come online but forecasters and forecasting algorithms have little experience with them. This is a problem for statistical short term forecasts, which must be trained on a long record of historical power production exactly what is missing for a new farm. Focus of the study was to analyse development of the offshore wind power forecast (WPF) quality from beginning of operation up to one year of operational experience. This paper represents a case study using data of the first German offshore wind farm "alpha ventus" and first German commercial offshore wind farm "Baltic1". The work was carried out with measured data from meteorological measurement mast FINO1, measured power from wind farms and numerical weather prediction (NWP) from the German Weather Service (DWD). This study facilitates to decide the length of needed time series and selection of forecast method to get a reliable WPF on a weekly time axis. Weekly development of WPF quality for day-ahead WPF via different models is presented. The models are physical model; physical model extended with a statistical correction (MOS) and artificial neural network (ANN) as a pure statistical model. Selforganizing map (SOM) is investigated for a better understanding of uncertainties of forecast error.

M Kurt; B Lange

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts million short" 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

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 24h temperature (?18%) and moisture (?10%) forecast in comparison to control run. The 24h 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

22

An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street  

Science Journals Connector (OSTI)

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

Zhi-Peng LI; Hong YU; Yun-Cai LIU; Fu-Qiang LIU

2008-01-01T23:59:59.000Z

23

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

24

Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns  

Science Journals Connector (OSTI)

This research uses a Self-Organizing Map neural network model (SOM) as a short-term forecasting method. The objective is to obtain the demand curve of certain hours of the next day. In order to validate the model...

C. Senabre; S. Valero; J. Aparicio

2010-01-01T23:59:59.000Z

25

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

SciTech Connect

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

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

2010-02-21T23:59:59.000Z

26

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

Reports and Publications (EIA)

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

1998-01-01T23:59:59.000Z

27

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

28

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

29

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

30

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

31

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

E-Print Network (OSTI)

Cloud tracking with optical flow for short-term solar forecasting Philip Wood-Bradley, José Zapata: John Pye ­ john.pye@anu.edu.au 1. Abstract A method for tracking and predicting cloud movement using apart with a size of 640 by 480 pixels, were processed to determine the time taken for clouds to reach

32

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.

33

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 Takens 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 LevenbergMarquardt (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

34

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

SciTech Connect

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

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

2014-04-30T23:59:59.000Z

35

Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts  

Science Journals Connector (OSTI)

The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over ...

David J. Stensrud; Jidong Gao

2010-04-01T23:59:59.000Z

36

Short-term solar irradiance forecasting using exponential smoothing state space model  

Science Journals Connector (OSTI)

Abstract We forecast high-resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models using residual analysis and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test. Using the optimized Fourier trend, an ESSS model is implemented to forecast the stationary residual series of datasets from Singapore and Colorado, USA. To compare the performance with other time series models, autoregressive integrated moving average (ARIMA), linear exponential smoothing (LES), simple exponential smoothing (SES) and random walk (RW) models are tested using the same data. The simulation results show that the ESSS model has generally better performance than other time series forecasting models. To assess the reliability of the forecasting model in real-time applications, a complementary study of the forecasting 95% confidence interval and forecasting horizon of the ESSS model has been conducted.

Zibo Dong; Dazhi Yang; Thomas Reindl; Wilfred M. Walsh

2013-01-01T23:59:59.000Z

37

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

38

DOE Announces More than $5 Million to Support Wind Energy Development |  

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

More than $5 Million to Support Wind Energy More than $5 Million to Support Wind Energy Development DOE Announces More than $5 Million to Support Wind Energy Development September 13, 2010 - 12:00am Addthis Washington, DC - U.S. Energy Secretary Steven Chu announced today that the Department of Energy is awarding more than $5 million to support U.S. wind energy development. Two projects receiving a total of $3.4 million over two years will improve short-term wind forecasting, which will accelerate the use of wind power in electricity transmission networks by allowing utilities and grid operators to more accurately forecast when and where electricity will be generated from wind power. Three additional projects are receiving a total of more than $1.8 million to boost the speed and scale of midsize wind turbine technology development and deployment.

39

Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column cloud liquid water amounts  

SciTech Connect

These observing system simulation experiments investigate the assimilation of satellite-observed water vapor and cloud liquid water data in the initialization of a limited-area primitive equations model with the goal of improving short-range precipitation forecasts. The assimilation procedure presented includes two aspects: specification of an initial cloud liquid water vertical distribution and diabatic initialization. The satellite data is simulated for the next generation of polar-orbiting satellite instruments, the Advanced Microwave Sounding Unit (AMSU) and the High-Resolution Infrared Sounder (HIRS), which are scheduled to be launched on the NOAA-K satellite in the mid-1990s. Based on cloud-top height and total column cloud liquid water amounts simulated for satellite data a diagnostic method is used to specify an initial cloud water vertical distribution and to modify the initial moisture distribution in cloudy areas. Using a diabatic initialization procedure, the associated latent heating profiles are directly assimilated into the numerical model. The initial heating is estimated by time averaging the latent heat release from convective and large-scale condensation during the early forecast stage after insertion of satellite-observed temperature, water vapor, and cloud water formation.

Wu, X.; Diak, G.R.; Hayden, C.M.; Young, J.A. [Univ. of Wisconsin, Madison, WI (United States)] [Univ. of Wisconsin, Madison, WI (United States)

1995-02-01T23:59:59.000Z

40

Very short range local area weather forecasting using measurements from geosynchronous meteorological satellites  

Science Journals Connector (OSTI)

Quantitative radiance measurements from NASA's ATS-3 geosynchronous satellite have been used to develop and test ... a statistical forecst method to predict air terminal weather over the very short range (06 ......

Gerald J. Sikula; Dr. Thomas H. Vonder Haar

1973-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts million short" 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

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...

42

Advanced chemistry-transport modeling and observing systems allow daily air quality observations, short-term forecasts, and real-time analyses of air quality at the global and  

E-Print Network (OSTI)

Advanced chemistry-transport modeling and observing systems allow daily air quality observations, short-term forecasts, and real-time analyses of air quality at the global and European scales control measures that could be taken for managing such episodes, European-scale air quality forecasting

Paris-Sud XI, Université de

43

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

E-Print Network (OSTI)

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

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

2008-08-20T23:59:59.000Z

44

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

45

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

SciTech Connect

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

Eisenberg, Joel Fred [ORNL

2008-01-01T23:59:59.000Z

46

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...

47

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...

48

Evaluation of Mixed-Phase Cloud Parameterizations in Short-Range Weather Forecasts with CAM3 and AM2 for Mixed-Phase Arctic Cloud Experiment  

SciTech Connect

By making use of the in-situ data collected from the recent Atmospheric Radiation Measurement Mixed-Phase Arctic Cloud Experiment, we have tested the mixed-phase cloud parameterizations used in the two major U.S. climate models, the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory climate model (AM2), under both the single-column modeling framework and the U.S. Department of Energy Climate Change Prediction Program-Atmospheric Radiation Measurement Parameterization Testbed. An improved and more physically based cloud microphysical scheme for CAM3 has been also tested. The single-column modeling tests were summarized in the second quarter 2007 Atmospheric Radiation Measurement metric report. In the current report, we document the performance of these microphysical schemes in short-range weather forecasts using the Climate Chagne Prediction Program Atmospheric Radiation Measurement Parameterizaiton Testbest strategy, in which we initialize CAM3 and AM2 with realistic atmospheric states from numerical weather prediction analyses for the period when Mixed-Phase Arctic Cloud Experiment was conducted.

Xie, S; Boyle, J; Klein, S; Liu, X; Ghan, S

2007-06-01T23:59:59.000Z

49

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...

50

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...

51

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 23-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

52

Relative Short-Range Forecast Impact from Aircraft, Profiler, Radiosonde, VAD, GPS-PW, METAR, and Mesonet Observations via the RUC Hourly Assimilation Cycle  

Science Journals Connector (OSTI)

An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived ...

Stanley G. Benjamin; Brian D. Jamison; William R. Moninger; Susan R. Sahm; Barry E. Schwartz; Thomas W. Schlatter

2010-04-01T23:59:59.000Z

53

A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts  

Science Journals Connector (OSTI)

The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, ...

Altu? Aksoy; David C. Dowell; Chris Snyder

2010-04-01T23:59:59.000Z

54

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

E-Print Network (OSTI)

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

55

" Million Housing Units, Final...  

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

3 Household Demographics of U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to...

56

" Million Housing Units, Final...  

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

3 Space Heating in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to...

57

" Million Housing Units, Final...  

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

5 Fuels Used and End Uses in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2"...

58

" Million Housing Units, Final...  

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

7 Space Heating in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Space...

59

" Million Housing Units, Final...  

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

5 Space Heating in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2" ,,"Less than...

60

" Million Housing Units, Final...  

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

4 Space Heating in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More...

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final...  

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

6 Space Heating in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold","Mixed- Humid","Mixed-Dry"...

62

Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model.  

E-Print Network (OSTI)

?? The short-term (1 to 48 hours) predictability of wind power production from wind power plants in a power system is critical to the value (more)

Boone, Andrew

2005-01-01T23:59:59.000Z

63

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

64

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

65

" Million Housing Units, Final...  

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

1 Space Heating in U.S. Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,"Pacific...

66

" Million Housing Units, Final...  

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

0 Household Demographics of Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census...

67

" Million Housing Units, Final...  

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

0 Fuels Used and End Uses in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census...

68

" Million Housing Units, Final...  

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

1 Household Demographics of Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,"Pacific...

69

" Million Housing Units, Final...  

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

HC.1.11 Fuels Used and End Uses in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census...

70

" Million Housing Units, Final...  

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

0 Space Heating in U.S. Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East...

71

" Million Housing Units, Final...  

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

8 Space Heating in U.S. Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census...

72

" Million Housing Units, Final...  

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

9 Household Demographics of Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census...

73

" Million Housing Units, Final...  

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

9 Space Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" " ",,,"East North Central Census...

74

" Million Housing Units, Final...  

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

9 Fuels Used and End Uses in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census...

75

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

76

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

77

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

78

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

79

Million Cu. Feet  

Gasoline and Diesel Fuel Update (EIA)

0 0 Alaska - Natural Gas 2010 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table 29. Summary Statistics for Natural Gas - Alaska, 2006-2010 Number of Producing Gas Wells at End of Year................................................... 231 239 261 261 269 Production (million cubic feet) Gross Withdrawals From Gas Wells .............................................. 193,654 165,624 150,483 137,639 127,417 From Oil Wells ................................................ 3,012,097 3,313,666 3,265,401

80

U.S. crude oil production expected to top 9 million barrels per...  

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

half of this year, drilling is expected to increase and U.S. production is forecast to rise to an average of 9.5 million barrels per day in 2016. That would be the...

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final...  

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

2 Fuels Used and End Uses in U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings...

82

" Million Housing Units, Final...  

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

2 Household Demographics of U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings...

83

millionImaging research infrastructure  

E-Print Network (OSTI)

Centre for Imaging Technology Commercialization, led by Aaron Fenster $34 million Hybrid imaging infrastructureimaging #12;IMAGING Investment $100 millionImaging research infrastructure Formation

Denham, Graham

84

Short-term energy outlook annual supplement, 1993  

SciTech Connect

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

NONE

1993-08-06T23:59:59.000Z

85

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

86

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

87

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, Bndicte

2012-01-01T23:59:59.000Z

88

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...

89

Sea-floor spreading during the past 10 million years on the East Pacific Rise between 35 p0 sS and 53 p0 sS, and the identification of short period pole reversal events  

E-Print Network (OSTI)

the East Pacific Rise between 35 S 0 and 53 S, and the Identification of Short Period Pole Reversal Events (Nay 1974) Robert Joseph Woodward, B. S. Florida State University Chairman of Advisory Committee: Dr. James N. Shapiro Twelve magnetic anomaly..., and activation of the East Pacific Rise 0 north of 35 S. 63 Figure 31. A model showing the resultant anomaly pattern that would occur if a fracture zone is assumed between the EL 28 and FL 20N profiles 65 Figure 32. EL 21 profile compared with a ridge Jump...

Woodward, Robert Joseph

1974-01-01T23:59:59.000Z

90

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

91

Short-term energy outlook quarterly projections. First quarter 1994  

SciTech Connect

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

Not Available

1994-02-07T23:59:59.000Z

92

Application of Artificial Neural Network Forecasts to Predict Fog at Canberra International Airport  

Science Journals Connector (OSTI)

The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million (Australian dollars) ...

Dustin Fabbian; Richard de Dear; Stephen Lellyett

2007-04-01T23:59:59.000Z

93

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

94

projects are valued at approximately $67 million (including $15 million  

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

projects are valued at approximately $67 million (including $15 million projects are valued at approximately $67 million (including $15 million in non-Federal cost sharing) over four years. The overall goal of the research is to develop carbon dioxide (CO 2 ) capture and separation technologies that can achieve at least 90 percent CO 2 removal at no more than a 35 percent increase in the cost of electricity. The projects, managed by FE's National Energy Technology Laboratory (NETL), include: (1) Linde, LLC, which will use a post-combustion capture technology incorporating BASF's novel amine-based process at a 1-megawatt electric (MWe) equivalent slipstream pilot plant at the National Carbon Capture Center (NCCC) (DOE contribution: $15 million); (2) Neumann Systems Group, Inc., which will design, construct, and test a patented NeuStreamTM absorber at the Colorado

95

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...

96

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

97

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

98

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

99

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

100

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

Note: This page contains sample records for the topic "forecasts million short" 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

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...

102

Arizona - Natural Gas 2012 Million  

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

4 4 Arizona - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S3. Summary statistics for natural gas - Arizona, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6 6 5 5 5 Production (million cubic feet) Gross Withdrawals From Gas Wells 523 711 183 168 117 From Oil Wells * * 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

103

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

104

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

105

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

106

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

107

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,

108

" Million U.S. Housing Units"  

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

0 Home Appliances Usage Indicators by Type of Housing Unit, 2005" " Million U.S. Housing Units" ,,"Type of Housing Unit" ," Housing Units (millions) ","Single-Family...

109

" Million U.S. Housing Units"  

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

0 Home Appliances Usage Indicators by Number of Household Members, 2005" " Million U.S. Housing Units" ,,"Number of Households With --" ,"Housing Units (millions)" ,,"1 Member","2...

110

" Million U.S. Housing Units"  

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

0 Home Appliances Usage Indicators by Year of Construction, 2005" " Million U.S. Housing Units" ,,"Year of Construction" ,"Housing Units (millions)" ,,"Before 1940","1940 to...

111

Lab contractor awards LANL Foundation $3 million  

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

Contractor awards LANL Foundation 3 million Lab contractor awards LANL Foundation 3 million To provide educational enrichment and educational outreach funding for a wide variety...

112

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...

113

Earth: 15 Million Years Ago  

E-Print Network (OSTI)

In Einstein's general relativity theory the metric component gxx in the direction of motion (x-direction) of the sun deviates from unity due to a tensor potential caused by the black hole existing around the center of the galaxy. Because the solar system is orbiting around the galactic center at 200 km/s, the theory shows that the Newtonian gravitational potential due to the sun is not quite radial. At the present time, the ecliptic plane is almost perpendicular to the galactic plane, consistent with this modification of the Newtonian gravitational force. The ecliptic plane is assumed to maintain this orientation in the galactic space as it orbits around the galactic center, but the rotational angular momentum of the earth around its own axis can be assumed to be conserved. The earth is between the sun and the galactic center at the summer solstice all the time. As a consequence, the rotational axis of the earth would be parallel to the axis of the orbital rotation of the earth 15 million years ago, if the solar system has been orbiting around the galactic center at 200 km/s. The present theory concludes that the earth did not have seasons 15 million years ago. Therefore, the water on the earth was accumulated near the poles as ice and the sea level was very low. Geological evidence exists that confirms this effect. The resulting global ice-melting started 15 million years ago and is ending now.

Masataka Mizushima

2008-10-13T23:59:59.000Z

114

Short-term improvements in public health from global-climate policies on fossil-fuel combustion: an interim report  

Science Journals Connector (OSTI)

SummaryBackground Most public-health assessments of climate-control policies have focused on long-term impacts of global change. Our interdisciplinary working group assesses likely short-term impacts on public health. Methods We combined models of energy consumption, carbon emissions, and associated atmospheric particulate-matter (PM) concentration under two different forecasts: business-as-usual (BAU); and a hypothetical climate-policy scenario, where developed and developing countries undertake significant reductions in carbon emissions. Findings We predict that by 2020, 700?000 avoidable deaths (90% CI 3850001034000) will occur annually as a result of additional PM exposure under the BAU forecasts when compared with the climate-policy scenario. From 2000 to 2020, the cumulative impact on public health related to the difference in PM exposure could total 8 million deaths globally (90% CI 4.411.9 million). In the USA alone, the avoidable number of annual deaths from PM exposure in 2020 (without climate-change-control policy) would equal in magnitude deaths associated with human immunodeficiency diseases or all liver diseases in 1995. Interpretation The mortality estimates are indicative of the magnitude of the likely health benefits of the climate-policy scenario examined and are not precise predictions of avoidable death. While characterised by considerable uncertainty, the short-term public-health impacts of reduced PM exposures associated with greenhouse-gas reductions are likely to be substantial even under the most conservative set of assumptions.

Devra Lee Davis

1997-01-01T23:59:59.000Z

115

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,

116

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

117

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.

118

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.

119

A $5 Million Boost for Midsize Wind Turbines and Grid Connectivity |  

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

A $5 Million Boost for Midsize Wind Turbines and Grid Connectivity A $5 Million Boost for Midsize Wind Turbines and Grid Connectivity A $5 Million Boost for Midsize Wind Turbines and Grid Connectivity September 14, 2010 - 10:52am Addthis Niketa Kumar Niketa Kumar Public Affairs Specialist, Office of Public Affairs What does this mean for me? With better forecasting, utilities can more reliably connect variable power sources such as wind energy with electricity grids, and can decrease their need for back-up energy sources such as natural gas and hydropower. Last week's Geek-Up talked about the Energy Department's Wind for Schools program and how it is helping schools use wind turbines to power their classrooms. Yesterday, U.S. Energy Secretary Steven Chu announced more than $5 million in funding to help bring wind-generated power to not only more

120

Wind Speeds at Heights Crucial for Wind Energy: Measurements and Verification of Forecasts  

Science Journals Connector (OSTI)

Wind speed measurements from one year from meteorological towers and wind turbines at heights between 20 and 250 m for various European sites are analyzed and are compared with operational short-term forecasts of the global ECMWF model. The ...

Susanne Drechsel; Georg J. Mayr; Jakob W. Messner; Reto Stauffer

2012-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts million short" 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

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,

122

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

123

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.

124

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

125

Price volatility forecasting using artificial neural networks in emerging electricity markets  

Science Journals Connector (OSTI)

In the adaptive short-term electricity price forecasting, it may be premature to rely solely on the hourly price forecast. The volatility of electricity price should also be analysed to provide additional insight on price forecasting. This paper proposes a price volatility module to analyse electricity price spikes and study the probability distribution of electricity price. Two methods are used to study the probability distribution of electricity price: the analytical method and the ANN method. Furthermore, ANN method is used to study the impact of line limits, line outages, generator outages, load pattern and bidding strategy on short term price forecasting, in addition to sensitivity analysis to determine the extent to which these factors impact price forecasting. Data used in this study are spot electricity prices from California market in the period which includes the crisis months where extreme volatility was observed.

Ahmad F. Al-Ajlouni; Hatim Y. Yamin; Ali Eyadeh

2012-01-01T23:59:59.000Z

126

California Natural Gas International Deliveries (Million Cubic...  

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

Deliveries (Million Cubic Feet) California Natural Gas International Deliveries (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

127

California Natural Gas International Receipts (Million Cubic...  

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

Receipts (Million Cubic Feet) California Natural Gas International Receipts (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's...

128

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 24h 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 3h, 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 6h. 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

129

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

130

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

131

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

SciTech Connect

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

Chiswell, S

2009-01-11T23:59:59.000Z

132

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

133

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

134

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...

135

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.

136

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

137

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...

138

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

139

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

140

1 million gallons of grout.doc  

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

Historic Milestone Achieved as 1 Million Gallons of Grout Historic Milestone Achieved as 1 Million Gallons of Grout Is Poured into SRS Waste Tanks 18 and 19 AIKEN, S.C. (May 9, 2012) - Operational closure of the next two radioactive waste tanks at the Savannah River Site (SRS) has achieved a historic milestone with the placement of over 1 million gallons of grout inside the massive underground tanks. Filling Tanks 18 and 19 began on April 2, 2012. As of today, over 1 million gallons of

Note: This page contains sample records for the topic "forecasts million short" 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

Energy Department Announces $66 Million for Transformational...  

Office of Environmental Management (EM)

(REMOTE), provides 34 million to find advanced biocatalyst technologies that can convert natural gas to liquid fuel for transportation. Deputy Director Martin made the project...

142

Million U.S. Housing Units Total...............................  

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

CDD or More and Less than 4,000 HDD Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

143

Heating a Plasma to 100 Million Kelvin.  

E-Print Network (OSTI)

?? In this work techniques for heating the fusion reactor ITER to thermonuclear temperatures, over 100 million kelvin, is investigated. The temperature is numerically computed (more)

Frberg, Gunnar

2014-01-01T23:59:59.000Z

144

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

SciTech Connect

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

Not Available

1994-08-02T23:59:59.000Z

145

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

146

Univariate forecasting of day-ahead hourly electricity demand in the northern grid of India  

Science Journals Connector (OSTI)

Short-term electricity demand forecasts (minutes to several hours ahead) have become increasingly important since the rise of the competitive energy markets. The issue is particularly important for India as it has recently set up a power exchange (PX), which has been operating on day-ahead hourly basis. In this study, an attempt has been made to forecast day-ahead hourly demand of electricity in the northern grid of India using univariate time-series forecasting techniques namely multiplicative seasonal ARIMA and Holt-Winters multiplicative exponential smoothing (ES). In-sample forecasts reveal that ARIMA models, except in one case, outperform ES models in terms of lower RMSE, MAE and MAPE criteria. We may conclude that linear time-series models works well to explain day-ahead hourly demand forecasts in the northern grid of India. The findings of the study will immensely help the players in the upcoming power market in India.

Sajal Ghosh

2009-01-01T23:59:59.000Z

147

A Framework of Incorporating Spatio-temporal Forecast in Look-ahead Grid Dispatch with Photovoltaic Generation  

E-Print Network (OSTI)

at multiple sites. Given the unique spatial and temporal correlation of PV generation, an optimal data-driven forecast model for short-term PV power is proposed. This model leverages both spatial and temporal correlations among neighboring solar sites...

Yang, Chen

2013-05-02T23:59:59.000Z

148

Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting  

Science Journals Connector (OSTI)

Data from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been ...

Robert J. Kuligowski; Yaping Li; Yu Zhang

2013-06-01T23:59:59.000Z

149

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

150

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

151

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

SciTech Connect

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

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

2013-05-01T23:59:59.000Z

152

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":""}]}

153

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

154

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

155

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

156

Million Cu. Feet Percent of National Total  

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

6 6 Tennessee - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S44. Summary statistics for natural gas - Tennessee, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 285 310 230 210 212 Production (million cubic feet) Gross Withdrawals From Gas Wells 4,700 5,478 5,144 4,851 5,825 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

157

Million Cu. Feet Percent of National Total  

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

38 38 Nevada - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S30. Summary statistics for natural gas - Nevada, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 4 4 4 3 4 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 4 4 4 3 4

158

Million Cu. Feet Percent of National Total  

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

2 2 Connecticut - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S7. Summary statistics for natural gas - Connecticut, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

159

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Oregon - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S39. Summary statistics for natural gas - Oregon, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 18 21 24 26 24 Production (million cubic feet) Gross Withdrawals From Gas Wells 409 778 821 1,407 1,344 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

160

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Idaho - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S14. Summary statistics for natural gas - Idaho, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

Note: This page contains sample records for the topic "forecasts million short" 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

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Washington - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S49. Summary statistics for natural gas - Washington, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

162

Million Cu. Feet Percent of National Total  

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

0 0 Maine - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S21. Summary statistics for natural gas - Maine, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

163

Million Cu. Feet Percent of National Total  

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

8 8 Minnesota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

164

Million Cu. Feet Percent of National Total  

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

2 2 South Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

165

Million Cu. Feet Percent of National Total  

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

6 6 District of Columbia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S9. Summary statistics for natural gas - District of Columbia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

166

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 North Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

167

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Iowa - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S17. Summary statistics for natural gas - Iowa, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

168

Million Cu. Feet Percent of National Total  

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

4 4 Massachusetts - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

169

Million Cu. Feet Percent of National Total  

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

6 6 Oregon - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S39. Summary statistics for natural gas - Oregon, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 21 24 26 24 27 Production (million cubic feet) Gross Withdrawals From Gas Wells 778 821 1,407 1,344 770 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

170

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Georgia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S11. Summary statistics for natural gas - Georgia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

171

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Minnesota - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

172

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Delaware - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S8. Summary statistics for natural gas - Delaware, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

173

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 District of Columbia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S9. Summary statistics for natural gas - District of Columbia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

174

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 New Jersey - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

175

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Tennessee - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S44. Summary statistics for natural gas - Tennessee, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 305 285 310 230 210 Production (million cubic feet) Gross Withdrawals From Gas Wells NA 4,700 5,478 5,144 4,851 From Oil Wells 3,942 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

176

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Nebraska - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S29. Summary statistics for natural gas - Nebraska, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 186 322 285 276 322 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,331 2,862 2,734 2,092 1,854 From Oil Wells 228 221 182 163 126 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

177

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Vermont - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S47. Summary statistics for natural gas - Vermont, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

178

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Wisconsin - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S51. Summary statistics for natural gas - Wisconsin, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

179

Million Cu. Feet Percent of National Total  

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

8 8 North Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

180

Million Cu. Feet Percent of National Total  

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

2 2 New Jersey - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

Note: This page contains sample records for the topic "forecasts million short" 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

Million Cu. Feet Percent of National Total  

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

0 0 Georgia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S11. Summary statistics for natural gas - Georgia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

182

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Connecticut - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S7. Summary statistics for natural gas - Connecticut, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

183

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Maryland - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 7 7 7 7 8 Production (million cubic feet) Gross Withdrawals From Gas Wells 35 28 43 43 34 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 35

184

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Florida - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S10. Summary statistics for natural gas - Florida, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 2,000 2,742 290 13,938 17,129 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

185

Million Cu. Feet Percent of National Total  

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

0 0 New Hampshire - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S31. Summary statistics for natural gas - New Hampshire, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

186

Million Cu. Feet Percent of National Total  

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

2 2 Maryland - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 7 7 7 8 9 Production (million cubic feet) Gross Withdrawals From Gas Wells 28 43 43 34 44 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 28

187

Million Cu. Feet Percent of National Total  

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

2 2 Missouri - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S27. Summary statistics for natural gas - Missouri, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 53 100 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

188

Million Cu. Feet Percent of National Total  

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

4 4 Delaware - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S8. Summary statistics for natural gas - Delaware, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0

189

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Massachusetts - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

190

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 South Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

191

Million Cu. Feet Percent of National Total  

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

0 0 Rhode Island - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S41. Summary statistics for natural gas - Rhode Island, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

192

Million Cu. Feet Percent of National Total  

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

0 0 Indiana - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S16. Summary statistics for natural gas - Indiana, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 525 563 620 914 819 Production (million cubic feet) Gross Withdrawals From Gas Wells 4,701 4,927 6,802 9,075 8,814 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

193

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

194

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...

195

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

196

A Million Meter Milestone | Department of Energy  

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

A Million Meter Milestone A Million Meter Milestone A Million Meter Milestone March 4, 2011 - 2:36pm Addthis To see what installing the 1 millionth meter looked like, check out this video. Don Macdonald Program Manager, Smart Grid Investment Grant Program What does this mean for me? Smart meters allow consumers to take personal control and ownership of her energy usage in a way not possible before. As program manager for the Department of Energy's Recovery Act funded Smart Grid Investment Grant (SGIG) program, I've had the pleasure of seeing SGIG reach several important milestones recently. Among the most notable has been the recent achievement of three million smart meters installed by SGIG recipients as of December 31, 2010. On February 23, 2011, along with my colleague Chris Irwin, I was in Houston, Texas where SGIG

197

Million U.S. Housing Units Total...............................  

Gasoline and Diesel Fuel Update (EIA)

... 13.2 10.2 0.6 0.3 1.1 1.1 Table HC2.10 Home Appliances Usage Indicators by Type of Housing Unit, 2005 Housing Units (millions) Single-Family Units...

198

storage of several million tonnes of carbon  

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

of several million tonnes of carbon dioxide (CO of several million tonnes of carbon dioxide (CO 2 ). The three recipients of the award are: the In Salah CO 2 Storage Project in Algeria; the Sleipner CO 2 Project in the North Sea; and the Weyburn-Midale CO 2 Project in Canada. In addition to providing scientific research opportunities, the projects are also being recognized as exemplary global models for their willingness to share their experiences in

199

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"

200

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

Note: This page contains sample records for the topic "forecasts million short" 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

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

202

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 ANN??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

203

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

E-Print Network (OSTI)

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

Johnson, Eric E.

204

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

205

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

206

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.

207

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

208

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.

209

Million Cu. Feet Percent of National Total  

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

8 8 Illinois - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S15. Summary statistics for natural gas - Illinois, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 45 51 50 40 40 Production (million cubic feet) Gross Withdrawals From Gas Wells E 1,188 E 1,438 E 1,697 2,114 2,125 From Oil Wells E 5 E 5 E 5 7 0 From Coalbed Wells E 0 E 0 0 0 0 From Shale Gas Wells 0

210

Million Cu. Feet Percent of National Total  

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

50 50 North Dakota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S36. Summary statistics for natural gas - North Dakota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 194 196 188 239 211 Production (million cubic feet) Gross Withdrawals From Gas Wells 13,738 11,263 10,501 14,287 22,261 From Oil Wells 54,896 45,776 38,306 27,739 17,434 From Coalbed Wells 0

211

Million Cu. Feet Percent of National Total  

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

0 0 Mississippi - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S26. Summary statistics for natural gas - Mississippi, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 2,343 2,320 1,979 5,732 1,669 Production (million cubic feet) Gross Withdrawals From Gas Wells 331,673 337,168 387,026 429,829 404,457 From Oil Wells 7,542 8,934 8,714 8,159 43,421 From Coalbed Wells 7,250

212

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Virginia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S48. Summary statistics for natural gas - Virginia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 5,735 6,426 7,303 7,470 7,903 Production (million cubic feet) Gross Withdrawals From Gas Wells R 6,681 R 7,419 R 16,046 R 23,086 20,375 From Oil Wells 0 0 0 0 0 From Coalbed Wells R 86,275 R 101,567

213

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Michigan - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S24. Summary statistics for natural gas - Michigan, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 9,712 9,995 10,600 10,100 11,100 Production (million cubic feet) Gross Withdrawals From Gas Wells R 80,090 R 16,959 R 20,867 R 7,345 18,470 From Oil Wells 54,114 10,716 12,919 9,453 11,620 From Coalbed Wells 0

214

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Montana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S28. Summary statistics for natural gas - Montana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 6,925 7,095 7,031 6,059 6,477 Production (million cubic feet) Gross Withdrawals From Gas Wells R 69,741 R 67,399 R 57,396 R 51,117 37,937 From Oil Wells 23,092 22,995 21,522 19,292 21,777 From Coalbed Wells

215

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Mississippi - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S26. Summary statistics for natural gas - Mississippi, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 2,315 2,343 2,320 1,979 5,732 Production (million cubic feet) Gross Withdrawals From Gas Wells R 259,001 R 331,673 R 337,168 R 387,026 429,829 From Oil Wells 6,203 7,542 8,934 8,714 8,159 From Coalbed Wells

216

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Indiana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S16. Summary statistics for natural gas - Indiana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 2,350 525 563 620 914 Production (million cubic feet) Gross Withdrawals From Gas Wells 3,606 4,701 4,927 6,802 9,075 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0

217

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 New York - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S34. Summary statistics for natural gas - New York, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 6,680 6,675 6,628 6,736 6,157 Production (million cubic feet) Gross Withdrawals From Gas Wells 54,232 49,607 44,273 35,163 30,495 From Oil Wells 710 714 576 650 629 From Coalbed Wells 0

218

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Texas - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S45. Summary statistics for natural gas - Texas, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 76,436 87,556 93,507 95,014 100,966 Production (million cubic feet) Gross Withdrawals From Gas Wells R 4,992,042 R 5,285,458 R 4,860,377 R 4,441,188 3,794,952 From Oil Wells 704,092 745,587 774,821 849,560 1,073,301

219

Million Cu. Feet Percent of National Total  

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

2 2 Ohio - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S37. Summary statistics for natural gas - Ohio, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 34,416 34,963 34,931 46,717 35,104 Production (million cubic feet) Gross Withdrawals From Gas Wells 79,769 83,511 73,459 30,655 65,025 From Oil Wells 5,072 5,301 4,651 45,663 6,684 From Coalbed Wells 0

220

Million Cu. Feet Percent of National Total  

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

0 0 Colorado - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S6. Summary statistics for natural gas - Colorado, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 25,716 27,021 28,813 30,101 32,000 Production (million cubic feet) Gross Withdrawals From Gas Wells 496,374 459,509 526,077 563,750 1,036,572 From Oil Wells 199,725 327,619 338,565

Note: This page contains sample records for the topic "forecasts million short" 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

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 South Dakota - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S43. Summary statistics for natural gas - South Dakota, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 71 71 89 102 100 Production (million cubic feet) Gross Withdrawals From Gas Wells 422 R 1,098 R 1,561 1,300 933 From Oil Wells 11,458 10,909 11,366 11,240 11,516 From Coalbed Wells 0 0

222

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Illinois - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S15. Summary statistics for natural gas - Illinois, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 43 45 51 50 40 Production (million cubic feet) Gross Withdrawals From Gas Wells RE 1,389 RE 1,188 RE 1,438 RE 1,697 2,114 From Oil Wells E 5 E 5 E 5 E 5 7 From Coalbed Wells RE 0 RE

223

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Colorado - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S6. Summary statistics for natural gas - Colorado, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 22,949 25,716 27,021 28,813 30,101 Production (million cubic feet) Gross Withdrawals From Gas Wells R 436,330 R 496,374 R 459,509 R 526,077 563,750 From Oil Wells 160,833 199,725 327,619

224

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Alaska - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S2. Summary statistics for natural gas - Alaska, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 239 261 261 269 277 Production (million cubic feet) Gross Withdrawals From Gas Wells 165,624 150,483 137,639 127,417 112,268 From Oil Wells 3,313,666 3,265,401 3,174,747 3,069,683 3,050,654

225

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Ohio - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S37. Summary statistics for natural gas - Ohio, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 34,416 34,416 34,963 34,931 46,717 Production (million cubic feet) Gross Withdrawals From Gas Wells R 82,812 R 79,769 R 83,511 R 73,459 30,655 From Oil Wells 5,268 5,072 5,301 4,651 45,663 From Coalbed Wells

226

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Kentucky - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S19. Summary statistics for natural gas - Kentucky, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 16,563 16,290 17,152 17,670 14,632 Production (million cubic feet) Gross Withdrawals From Gas Wells 95,437 R 112,587 R 111,782 133,521 122,578 From Oil Wells 0 1,529 1,518 1,809 1,665 From Coalbed Wells 0

227

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

8 8 Utah - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S46. Summary statistics for natural gas - Utah, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 5,197 5,578 5,774 6,075 6,469 Production (million cubic feet) Gross Withdrawals From Gas Wells R 271,890 R 331,143 R 340,224 R 328,135 351,168 From Oil Wells 35,104 36,056 36,795 42,526 49,947 From Coalbed Wells

228

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 California - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S5. Summary statistics for natural gas - California, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 1,540 1,645 1,643 1,580 1,308 Production (million cubic feet) Gross Withdrawals From Gas Wells 93,249 91,460 82,288 73,017 63,902 From Oil Wells R 116,652 R 122,345 R 121,949 R 151,369 120,880

229

Million Cu. Feet Percent of National Total  

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

0 0 Utah - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S46. Summary statistics for natural gas - Utah, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 5,578 5,774 6,075 6,469 6,900 Production (million cubic feet) Gross Withdrawals From Gas Wells 331,143 340,224 328,135 351,168 402,899 From Oil Wells 36,056 36,795 42,526 49,947 31,440 From Coalbed Wells 74,399

230

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Louisiana - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S20. Summary statistics for natural gas - Louisiana, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 18,145 19,213 18,860 19,137 21,235 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,261,539 R 1,288,559 R 1,100,007 R 911,967 883,712 From Oil Wells 106,303 61,663 58,037 63,638 68,505

231

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Oklahoma - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S38. Summary statistics for natural gas - Oklahoma, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 38,364 41,921 43,600 44,000 41,238 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,583,356 R 1,452,148 R 1,413,759 R 1,140,111 1,281,794 From Oil Wells 35,186 153,227 92,467 210,492 104,703

232

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 New Mexico - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S33. Summary statistics for natural gas - New Mexico, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 42,644 44,241 44,784 44,748 32,302 Production (million cubic feet) Gross Withdrawals From Gas Wells R 657,593 R 732,483 R 682,334 R 616,134 556,024 From Oil Wells 227,352 211,496 223,493 238,580 252,326

233

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 West Virginia - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S50. Summary statistics for natural gas - West Virginia, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 48,215 49,364 50,602 52,498 56,813 Production (million cubic feet) Gross Withdrawals From Gas Wells R 189,968 R 191,444 R 192,896 R 151,401 167,113 From Oil Wells 701 0 0 0 0 From Coalbed Wells

234

Million Cu. Feet Percent of National Total  

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

6 6 Michigan - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S24. Summary statistics for natural gas - Michigan, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 9,995 10,600 10,100 11,100 10,900 Production (million cubic feet) Gross Withdrawals From Gas Wells 16,959 20,867 7,345 18,470 17,041 From Oil Wells 10,716 12,919 9,453 11,620 4,470 From Coalbed Wells 0

235

Million U.S. Housing Units Total............................................................................  

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

Attached Attached 2 to 4 Units Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or More Units Mobile Homes Type of Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Home Electronics Usage Indicators Detached Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing Units Attached 2 to 4 Units Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or More Units Mobile Homes Type of Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Home Electronics Usage Indicators Detached Status of PC When Not in Use Left On..............................................................

236

Million Cu. Feet Percent of National Total  

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

8 8 West Virginia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S50. Summary statistics for natural gas - West Virginia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 49,364 50,602 52,498 56,813 50,700 Production (million cubic feet) Gross Withdrawals From Gas Wells 191,444 192,896 151,401 167,113 397,313 From Oil Wells 0 0 0 0 1,477 From Coalbed Wells 0

237

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

80 80 Wyoming - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S52. Summary statistics for natural gas - Wyoming, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 27,350 28,969 25,710 26,124 26,180 Production (million cubic feet) Gross Withdrawals From Gas Wells R 1,649,284 R 1,764,084 R 1,806,807 R 1,787,599 1,709,218 From Oil Wells 159,039 156,133 135,269 151,871 152,589

238

Million Cu. Feet Percent of National Total  

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

6 6 New York - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S34. Summary statistics for natural gas - New York, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,675 6,628 6,736 6,157 7,176 Production (million cubic feet) Gross Withdrawals From Gas Wells 49,607 44,273 35,163 30,495 25,985 From Oil Wells 714 576 650 629 439 From Coalbed Wells 0

239

Million Cu. Feet Percent of National Total  

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

2 2 Wyoming - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S52. Summary statistics for natural gas - Wyoming, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 28,969 25,710 26,124 26,180 22,171 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,764,084 1,806,807 1,787,599 1,709,218 1,762,095 From Oil Wells 156,133 135,269 151,871 152,589 24,544

240

Million Cu. Feet Percent of National Total  

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

4 4 Virginia - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S48. Summary statistics for natural gas - Virginia, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,426 7,303 7,470 7,903 7,843 Production (million cubic feet) Gross Withdrawals From Gas Wells 7,419 16,046 23,086 20,375 21,802 From Oil Wells 0 0 0 0 9 From Coalbed Wells 101,567 106,408

Note: This page contains sample records for the topic "forecasts million short" 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

Million Cu. Feet Percent of National Total  

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

6 6 Kentucky - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S19. Summary statistics for natural gas - Kentucky, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 16,290 17,152 17,670 14,632 17,936 Production (million cubic feet) Gross Withdrawals From Gas Wells 112,587 111,782 133,521 122,578 106,122 From Oil Wells 1,529 1,518 1,809 1,665 0 From Coalbed Wells 0

242

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Pennsylvania - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S40. Summary statistics for natural gas - Pennsylvania, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 52,700 55,631 57,356 44,500 54,347 Production (million cubic feet) Gross Withdrawals From Gas Wells 182,277 R 188,538 R 184,795 R 173,450 242,305 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0

243

Million Cu. Feet Percent of National Total  

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

8 8 Texas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S45. Summary statistics for natural gas - Texas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 87,556 93,507 95,014 100,966 96,617 Production (million cubic feet) Gross Withdrawals From Gas Wells 5,285,458 4,860,377 4,441,188 3,794,952 3,619,901 From Oil Wells 745,587 774,821 849,560 1,073,301 860,675

244

Million Cu. Feet Percent of National Total  

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

0 0 Alabama - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S1. Summary statistics for natural gas - Alabama, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 6,860 6,913 7,026 7,063 6,327 Production (million cubic feet) Gross Withdrawals From Gas Wells 158,964 142,509 131,448 116,872 114,407 From Oil Wells 6,368 5,758 6,195 5,975 10,978

245

Million Cu. Feet Percent of National Total  

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

8 8 Louisiana - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S20. Summary statistics for natural gas - Louisiana, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 19,213 18,860 19,137 21,235 19,792 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,288,559 1,100,007 911,967 883,712 775,506 From Oil Wells 61,663 58,037 63,638 68,505 49,380

246

Million Cu. Feet Percent of National Total  

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

4 4 South Dakota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S43. Summary statistics for natural gas - South Dakota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 71 89 102 100 95 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,098 1,561 1,300 933 14,396 From Oil Wells 10,909 11,366 11,240 11,516 689 From Coalbed Wells 0 0 0 0 0

247

Million Cu. Feet Percent of National Total  

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

4 4 Kansas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S18. Summary statistics for natural gas - Kansas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 17,862 21,243 22,145 25,758 24,697 Production (million cubic feet) Gross Withdrawals From Gas Wells 286,210 269,086 247,651 236,834 264,610 From Oil Wells 45,038 42,647 39,071 37,194 0 From Coalbed Wells 44,066

248

Million Cu. Feet Percent of National Total  

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

6 6 Arkansas - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S4. Summary statistics for natural gas - Arkansas, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 5,592 6,314 7,397 8,388 8,538 Production (million cubic feet) Gross Withdrawals From Gas Wells 173,975 164,316 152,108 132,230 121,684 From Oil Wells 7,378 5,743 5,691 9,291 3,000

249

Million Cu. Feet Percent of National Total  

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

8 8 California - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S5. Summary statistics for natural gas - California, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 1,645 1,643 1,580 1,308 1,423 Production (million cubic feet) Gross Withdrawals From Gas Wells 91,460 82,288 73,017 63,902 120,579 From Oil Wells 122,345 121,949 151,369 120,880 70,900

250

Million Cu. Feet Percent of National Total  

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

4 4 Oklahoma - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S38. Summary statistics for natural gas - Oklahoma, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 41,921 43,600 44,000 41,238 40,000 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,452,148 1,413,759 1,140,111 1,281,794 1,394,859 From Oil Wells 153,227 92,467 210,492 104,703 53,720

251

Million Cu. Feet Percent of National Total  

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

2 2 Alaska - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S2. Summary statistics for natural gas - Alaska, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 261 261 269 277 185 Production (million cubic feet) Gross Withdrawals From Gas Wells 150,483 137,639 127,417 112,268 107,873 From Oil Wells 3,265,401 3,174,747 3,069,683 3,050,654 3,056,918

252

" Million Housing Units, Final"  

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

2 Household Demographics of U.S. Homes, by Owner/Renter Status, 2009" 2 Household Demographics of U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,"Total U.S.1 (millions)",,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" "Household Demographics",,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Number of Household Members"

253

" Million Housing Units, Final"  

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

0 Structural and Geographic Characteristics of Homes in South Region, Divisions, and States, 2009" 0 Structural and Geographic Characteristics of Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" "Structural and Geographic Characteristics",,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" ,,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX"

254

" Million Housing Units, Final"  

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

2 Televisions in U.S. Homes, by Owner/Renter Status, 2009" 2 Televisions in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" ,,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Televisions" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Televisions" "Number of Televisions"

255

" Million Housing Units, Final"  

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

2 Air Conditioning in U.S. Homes, by Owner/Renter Status, 2009" 2 Air Conditioning in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" "Air Conditioning",,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Air Conditioning Equipment"

256

" Million Housing Units, Final"  

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

0 Computers and Other Electronics in Homes in South Region, Divisions, and States, 2009" 0 Computers and Other Electronics in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Computers and Other Electronics",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX"

257

" Million Housing Units, Final"  

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

2 Space Heating in U.S. Homes, by Owner/Renter Status, 2009" 2 Space Heating in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" ,,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Space Heating" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Space Heating Equipment"

258

" Million Housing Units, Final"  

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

1 Televisions in Homes in West Region, Divisions, and States, 2009" 1 Televisions in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Televisions",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

259

" Million Housing Units, Final"  

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

9 Appliances in Homes in Midwest Region, Divisions, and States, 2009" 9 Appliances in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Appliances",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)"

260

" Million Housing Units, Final"  

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

2 Appliances in U.S. Homes, by Owner/Renter Status, 2009" 2 Appliances in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,"Total U.S.1 (millions)",,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" "Appliances",,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Cooking Appliances" "Stoves (Units With Both"

Note: This page contains sample records for the topic "forecasts million short" 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

Ethylene capacity tops 77 million mty  

SciTech Connect

World ethylene production capacity is 77.8 million metric tons/year (mty). This total represents an increase of more than 6 million mty, or almost 9%, over last year`s survey. The biggest reason for the large change is more information about plants in the CIS. Also responsible for the increase in capacity is the start-up of several large ethylene plants during the past year. The paper discusses construction of ethylene plants, feedstocks, prices, new capacity, price outlook, and problems in Europe`s ethylene market.

Rhodes, A.K.; Knott, D.

1995-04-17T23:59:59.000Z

262

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

263

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

264

U.S. monthly oil production tops 8 million barrels per day for the first time since 1988  

Gasoline and Diesel Fuel Update (EIA)

U.S. crude oil production expected to hit four-decade high during 2015 U.S. crude oil production expected to hit four-decade high during 2015 U.S. crude oil production over the next two years is expected to grow to its highest level since the early 1970s. Oil output increased by 1 million barrels per day in 2013...and is expected to repeat that growth rate during 2014....according to the new forecast from the U.S. Energy Information Administration. U.S. crude oil production is forecast to average 8.5 million barrels per day this year and then rise to 9.3 million barrels per day in 2015. That would be the highest yearly oil output since 1972, and just 300,000 barrels per day below the all-time production high of 9.6 million barrels per day set in 1970. Most of the oil production growth will come from increased drilling in the shale formations in

265

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.

266

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

267

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

268

Essays on macroeconomics and forecasting  

E-Print Network (OSTI)

explanatory variables. Compared to Stock and Watson (2002)â??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

269

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

270

Requested Amount: $7 million annually PROGRAM DESCRIPTION  

E-Print Network (OSTI)

Requested Amount: $7 million annually PROGRAM DESCRIPTION Goals are to increase the level of fire protection to the citizens of Texas and to decrease the level of losses (in lives and property) from wildfires, thereby enhancing the quality of life for Texans. The Texas Wildfire Protection Plan (TWPP

271

Colorado Crude Oil Reserves in Nonproducing Reservoirs (Million...  

Annual Energy Outlook 2012 (EIA)

Reserves in Nonproducing Reservoirs (Million Barrels) Colorado Crude Oil Reserves in Nonproducing Reservoirs (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

272

Colorado Working Natural Gas Underground Storage Capacity (Million...  

Annual Energy Outlook 2012 (EIA)

Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Colorado Working Natural Gas Underground Storage Capacity (Million Cubic Feet) Year Jan Feb Mar Apr May Jun...

273

Colorado Nonhydrocarbon Gases Removed from Natural Gas (Million...  

Annual Energy Outlook 2012 (EIA)

Nonhydrocarbon Gases Removed from Natural Gas (Million Cubic Feet) Colorado Nonhydrocarbon Gases Removed from Natural Gas (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3...

274

DOE Awards $3 Million Contract to Oak Ridge Associated Universities...  

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

Million Contract to Oak Ridge Associated Universities for Expert Review of Yucca Mountain Work DOE Awards 3 Million Contract to Oak Ridge Associated Universities for Expert...

275

President Obama Announces Over $467 Million in Recovery Act Funding...  

Office of Environmental Management (EM)

Over 467 Million in Recovery Act Funding for Geothermal and Solar Energy Projects President Obama Announces Over 467 Million in Recovery Act Funding for Geothermal and Solar...

276

President Obama Announces Over $467 Million in Recovery Act Funding...  

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

Announces Over 467 Million in Recovery Act Funding for Geothermal and Solar Energy Projects President Obama Announces Over 467 Million in Recovery Act Funding for Geothermal and...

277

DOE Announces Nearly $170 Million in Available Funding to Advance...  

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

70 Million in Available Funding to Advance Solar Energy Technologies DOE Announces Nearly 170 Million in Available Funding to Advance Solar Energy Technologies April 8, 2011 -...

278

Energy Department Announces $4 Million for University Consortium...  

Energy Savers (EERE)

4 Million for University Consortium to Advance America's Water Power Industry Energy Department Announces 4 Million for University Consortium to Advance America's Water Power...

279

DOE Announces Over $30 Million to Help Universities Train the...  

Office of Environmental Management (EM)

30 Million to Help Universities Train the Next Generation of Industrial Energy Efficiency Experts DOE Announces Over 30 Million to Help Universities Train the Next Generation of...

280

Secretary Chu Announces $256 Million Investment to Improve the...  

Office of Environmental Management (EM)

56 Million Investment to Improve the Energy Efficiency of the American Economy Secretary Chu Announces 256 Million Investment to Improve the Energy Efficiency of the American...

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


281

Energy Department Announces $5 Million for Residential Building...  

Office of Environmental Management (EM)

Announces 5 Million for Residential Building Energy Efficiency Research and University-Industry Partnerships Energy Department Announces 5 Million for Residential Building Energy...

282

Secretary Chu Announces Nearly $50 Million of Recovery Act Funding...  

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

Nearly 50 Million of Recovery Act Funding to Accelerate Deployment of Geothermal Heat Pumps Secretary Chu Announces Nearly 50 Million of Recovery Act Funding to...

283

Secretary Chu Announces Nearly $50 Million of Recovery Act Funding...  

Office of Environmental Management (EM)

50 Million of Recovery Act Funding to Accelerate Deployment of Geothermal Heat Pumps Secretary Chu Announces Nearly 50 Million of Recovery Act Funding to Accelerate Deployment of...

284

Energy Secretary Chu Announces $148 million in Recovery Act Funding...  

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

48 million in Recovery Act Funding for Environmental Cleanup in New York Energy Secretary Chu Announces 148 million in Recovery Act Funding for Environmental Cleanup in New York...

285

Energy Department Invests Over $7 Million to Deploy Tribal Clean...  

Energy Savers (EERE)

Energy Department Invests Over 7 Million to Deploy Tribal Clean Energy Projects Energy Department Invests Over 7 Million to Deploy Tribal Clean Energy Projects November 14, 2013...

286

Energy Secretary Chu Announces $384 Million in Recovery Act Funding...  

Energy Savers (EERE)

384 Million in Recovery Act Funding for Environmental Cleanup in New Mexico Energy Secretary Chu Announces 384 Million in Recovery Act Funding for Environmental Cleanup in New...

287

Energy Department Announces $11 Million to Advance Renewable...  

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

1 Million to Advance Renewable Carbon Fiber Production from Biomass Energy Department Announces 11 Million to Advance Renewable Carbon Fiber Production from Biomass July 30, 2014...

288

Combined Heat and Power System Achieves Millions in Cost Savings...  

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

Achieves Millions in Cost Savings at Large University - Case Study, 2013 Combined Heat and Power System Achieves Millions in Cost Savings at Large University - Case Study, 2013...

289

Department of Energy Awards $9 Million in Grants for Science...  

Office of Environmental Management (EM)

Awards 9 Million in Grants for Science and Technical Research to Historically Black Colleges and Universities in South Carolina and Georgia Department of Energy Awards 9 Million...

290

Energy Department Announces $53 Million to Drive Innovation,...  

Energy Savers (EERE)

Energy Department Announces 53 Million to Drive Innovation, Cut Cost of Solar Power Energy Department Announces 53 Million to Drive Innovation, Cut Cost of Solar Power October...

291

Energy Department Announces $3 Million to Support Clean Energy...  

Energy Savers (EERE)

Energy Department Announces 3 Million to Support Clean Energy Businesses and Entrepreneurs Energy Department Announces 3 Million to Support Clean Energy Businesses and...

292

Energy Department Announces $10 Million to Speed Enhanced Geothermal...  

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

Energy Department Announces 10 Million to Speed Enhanced Geothermal Systems into the Market Energy Department Announces 10 Million to Speed Enhanced Geothermal Systems into the...

293

Department of Energy Awards $15 Million for Nuclear Fuel Cycle...  

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

Million for Nuclear Fuel Cycle Technology Research and Development Department of Energy Awards 15 Million for Nuclear Fuel Cycle Technology Research and Development August 1,...

294

Department of Energy Announces $17 Million to Bolster University...  

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

7 Million to Bolster University-Led Nuclear Energy Research and Development Department of Energy Announces 17 Million to Bolster University-Led Nuclear Energy Research and...

295

Department Of Energy Offers $60 Million to Spur Industry Engagement...  

Office of Environmental Management (EM)

Of Energy Offers 60 Million to Spur Industry Engagement in Global Nuclear Energy Partnership Department Of Energy Offers 60 Million to Spur Industry Engagement in Global Nuclear...

296

Department of Energy Announces $39 Million to Strengthen University...  

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

39 Million to Strengthen University-Led Nuclear Energy Research and Development Department of Energy Announces 39 Million to Strengthen University-Led Nuclear Energy Research and...

297

Energy Department Announces $2 Million to Develop Supply Chain...  

Energy Savers (EERE)

Announces 2 Million to Develop Supply Chain, Manufacturing Competitiveness Analysis for Hydrogen and Fuel Cell Technologies Energy Department Announces 2 Million to Develop...

298

Department of Energy Awards Nearly $7 Million to Advance Fuel...  

Energy Savers (EERE)

Million to Advance Fuel Cell and Hydrogen Storage Systems Research Department of Energy Awards Nearly 7 Million to Advance Fuel Cell and Hydrogen Storage Systems Research August...

299

Energy Secretary Bodman Announces $119 Million in Funding and...  

Energy Savers (EERE)

119 Million in Funding and Roadmap to Advance Hydrogen Fuel Cell Vehicles Energy Secretary Bodman Announces 119 Million in Funding and Roadmap to Advance Hydrogen Fuel Cell...

300

Energy Department Invests Over $7 Million to Commercialize Cost...  

Energy Savers (EERE)

Energy Department Invests Over 7 Million to Commercialize Cost-Effective Hydrogen and Fuel Cell Technologies Energy Department Invests Over 7 Million to Commercialize...

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


301

Obama Administration Announces $12 Million i6 Green Investment...  

Office of Environmental Management (EM)

Obama Administration Announces 12 Million i6 Green Investment to Promote Clean Energy Innovation and Job Creation Obama Administration Announces 12 Million i6 Green Investment to...

302

Energy Department to Award $6 Million to State Partnerships to...  

Energy Savers (EERE)

to Award 6 Million to State Partnerships to Increase Energy Efficiency Energy Department to Award 6 Million to State Partnerships to Increase Energy Efficiency September 19, 2006...

303

Energy Department Invests $60 Million to Train Next Generation...  

Office of Environmental Management (EM)

60 Million to Train Next Generation Nuclear Energy Leaders, Pioneer Advanced Nuclear Technology Energy Department Invests 60 Million to Train Next Generation Nuclear Energy...

304

Energy Department Invests $67 Million to Advanced Nuclear Technology...  

Office of Environmental Management (EM)

Energy Department Invests 67 Million to Advanced Nuclear Technology Energy Department Invests 67 Million to Advanced Nuclear Technology August 20, 2014 - 12:00pm Addthis News...

305

Energy Department Awards $5 Million to Spur Local Clean Energy...  

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

5 Million to Spur Local Clean Energy Development, Energy Savings Energy Department Awards 5 Million to Spur Local Clean Energy Development, Energy Savings October 14, 2014 -...

306

Energy Department Announces Up to $14 Million for Applying Landscape...  

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

Up to 14 Million for Applying Landscape Design to Cellulosic Bioenergy Energy Department Announces Up to 14 Million for Applying Landscape Design to Cellulosic Bioenergy October...

307

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 (SyntetosBoylan 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

308

" Million Housing Units, Final"  

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

Air Conditioning in U.S. Homes, by Housing Unit Type, 2009" Air Conditioning in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Air Conditioning" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,61.1,5.6,6.3,15.2,5.8 "Have Air Conditioning Equipment But" "Do Not Use It",4.9,2.6,0.2,0.7,0.9,0.4 "Do Not Have Air Conditioning Equipment",14.7,8.1,0.9,2.1,3,0.7 "Type of Air Conditioning Equipment "

309

" Million Housing Units, Final"  

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

8 Fuels Used and End Uses in Homes in Northeast Region, Divisions, and States, 2009" 8 Fuels Used and End Uses in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Fuels Used and End Uses",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Fuels Used for Any Use" "Electricity",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Natural Gas",69.2,13.8,2.9,1.7,1.1,10.9,5.7,2.3,2.8

310

" Million Housing Units, Final"  

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

1 Computers and Other Electronics in Homes in West Region, Divisions, and States, 2009" 1 Computers and Other Electronics in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Computers and Other Electronics",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

311

" Million Housing Units, Final"  

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

Household Demographics of U.S. Homes, by Housing Unit Type, 2009" Household Demographics of U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Household Demographics" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Number of Household Members" "1 Person",31.3,14.4,2.1,3.4,9.6,1.9 "2 Persons",35.8,24.2,1.9,2.5,5,2.1 "3 Persons",18.1,12.1,1.2,1.3,2.2,1.2 "4 Persons",15.7,11.5,1,1,1.5,0.8 "5 Persons",7.7,5.8,0.3,0.5,0.6,0.5

312

" Million Housing Units, Final"  

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

8 Computers and Other Electronics in Homes in Northeast Region, Divisions, and States, 2009" 8 Computers and Other Electronics in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Computers and Other Electronics",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Computers" "Number of Computers" 0,27.4,4.7,1,0.5,0.5,3.7,1.7,1.4,0.5 1,46.9,8.7,2.3,1,1.3,6.4,3.2,2,1.2 2,24.3,4.3,1.2,0.5,0.7,3.1,1.4,0.9,0.8

313

" Million Housing Units, Final"  

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

1 Space Heating in U.S. Homes in West Region, Divisions, and States, 2009" 1 Space Heating in U.S. Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Space Heating",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

314

" Million Housing Units, Final"  

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

7 Space Heating in U.S. Homes, by Census Region, 2009" 7 Space Heating in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Space Heating" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Space Heating Equipment" "Use Space Heating Equipment",110.1,20.8,25.8,41.1,22.4 "Have Space Heating Equipment But Do " "Not Use It",2.4,"Q","Q",0.7,1.6 "Do Not Have Space Heating Equipment",1.2,"N","Q",0.3,0.8 "Main Heating Fuel and Equipment2" "Natural Gas",55.6,10.8,17.9,13.3,13.6 "Central Warm-Air Furnace",44.3,6.1,15.9,11.3,11

315

" Million Housing Units, Final"  

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

7 Water Heating in U.S. Homes, by Census Region, 2009" 7 Water Heating in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Water Heating" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Number of Storage Tank Water Heaters" 0,2.9,1.3,0.4,0.7,0.5 1,108.1,19.3,25,40.2,23.6 "2 or More",2.7,0.2,0.5,1.2,0.7 "Number of Tankless Water Heaters2" 0,110.4,19.4,25.6,41.2,24.2 1,3.1,1.4,0.3,0.8,0.6 "2 or More",0.1,"Q","N","Q","Q" "Main Water Heater" "Main Water Heater Type" "Storage Tank",110.6,19.4,25.5,41.3,24.3

316

" Million Housing Units, Final"  

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

4 Space Heating in U.S. Homes, by Number of Household Members, 2009" 4 Space Heating in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Space Heating",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Space Heating Equipment" "Use Space Heating Equipment",110.1,30.3,35,17.6,15.2,12 "Have Space Heating Equipment But Do " "Not Use It",2.4,0.6,0.6,0.3,0.4,0.4 "Do Not Have Space Heating Equipment",1.2,0.3,0.3,0.2,0.1,0.3 "Main Heating Fuel and Equipment2" "Natural Gas",55.6,14.1,17.9,9.4,7.9,6.3

317

" Million Housing Units, Final"  

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

5 Space Heating in U.S. Homes, by Household Income, 2009" 5 Space Heating in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Space Heating" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Space Heating Equipment" "Use Space Heating Equipment",110.1,22.8,26.5,20.5,13.8,9.1,5.6,11.8,16.1 "Have Space Heating Equipment But Do " "Not Use It",2.4,0.6,0.7,0.5,0.2,0.2,0.1,0.1,0.5

318

" Million Housing Units, Final"  

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

8 Televisions in Homes in Northeast Region, Divisions, and States, 2009" 8 Televisions in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Televisions",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Televisions" "Number of Televisions" 0,1.5,0.4,0.1,0.1,"Q",0.2,"Q","Q","Q" 1,24.2,4.6,1.2,0.6,0.6,3.5,2,1,0.4

319

" Million Housing Units, Final"  

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

0 Air Conditioning in Homes in South Region, Divisions, and States, 2009" 0 Air Conditioning in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Air Conditioning",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

320

" Million Housing Units, Final"  

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

9 Computers and Other Electronics in Homes in Midwest Region, Divisions, and States, 2009" 9 Computers and Other Electronics in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Computers and Other Electronics",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Computers" "Number of Computers" 0,27.4,6.7,4.7,1.1,1.1,0.6,2,2,0.6,1,0.5

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final"  

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

0 Water Heating in U.S. Homes in South Region, Divisions, and States, 2009" 0 Water Heating in U.S. Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Water Heating",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

322

" Million Housing Units, Final"  

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

8 Space Heating in U.S. Homes in Northeast Region, Divisions, and States, 2009" 8 Space Heating in U.S. Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Space Heating",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Space Heating Equipment" "Use Space Heating Equipment",110.1,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Have Space Heating Equipment But Do "

323

" Million Housing Units, Final"  

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

6 Household Demographics of U.S. Homes, by Climate Region, 2009" 6 Household Demographics of U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Household Demographics",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Number of Household Members" "1 Person",31.3,11,9.7,3.3,5.4,1.9 "2 Persons",35.8,12.4,11.2,4.4,5.9,1.8 "3 Persons",18.1,6,5.7,2.2,3.1,1.1 "4 Persons",15.7,5.3,4.9,2,2.6,0.9 "5 Persons",7.7,2.6,2.4,1.1,1.2,0.4 "6 or More Persons",5,1.5,1.5,1,0.8,0.2

324

" Million Housing Units, Final"  

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

9 Fuels Used and End Uses in Homes in Midwest Region, Divisions, and States, 2009" 9 Fuels Used and End Uses in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Fuels Used and End Uses",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Fuels Used for Any Use" "Electricity",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8

325

" Million Housing Units, Final"  

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

HC4.9 Televisions in Homes in Midwest Region, Divisions, and States, 2009" HC4.9 Televisions in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Televisions",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Televisions" "Number of Televisions" 0,1.5,0.3,0.2,"Q","Q","Q","Q",0.1,"Q","Q","Q"

326

" Million Housing Units, Final"  

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

8 Household Demographics of Homes in Northeast Region, Divisions, and States, 2009" 8 Household Demographics of Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Household Demographics",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Number of Household Members" "1 Person",31.3,6,1.5,0.7,0.8,4.5,2.1,1.6,0.8 "2 Persons",35.8,6.3,1.8,0.8,1,4.5,2,1.5,0.9

327

" Million Housing Units, Final"  

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

9 Water Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" 9 Water Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,,,,,"IA, MN, ND, SD" "Water Heating",,,,"IL","MI","WI","IN, OH",,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Number of Storage Tank Water Heaters" 0,2.9,0.4,0.3,"Q","Q","Q","Q",0.1,"Q","Q","Q"

328

" Million Housing Units, Final"  

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

9 Air Conditioning in Homes in Midwest Region, Divisions, and States, 2009" 9 Air Conditioning in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Air Conditioning",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,22.4,15,4.3,3.1,1.8,5.9,7.4,2.3,3.4,1.7

329

" Million Housing Units, Final"  

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

6 Space Heating in U.S. Homes, by Climate Region, 2009" 6 Space Heating in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Space Heating",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Space Heating Equipment" "Use Space Heating Equipment",110.1,38.7,35.4,12.5,17.6,6 "Have Space Heating Equipment But Do " "Not Use It",2.4,"Q","N",1.3,0.7,0.3 "Do Not Have Space Heating Equipment",1.2,"Q","Q",0.3,0.8,"Q" "Main Heating Fuel and Equipment3"

330

" Million Housing Units, Final"  

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

0 Space Heating in U.S. Homes in South Region, Divisions, and States, 2009" 0 Space Heating in U.S. Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Space Heating",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

331

" Million Housing Units, Final"  

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

2 Water Heating in U.S. Homes, by Owner/Renter Status, 2009" 2 Water Heating in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,"Total U.S.1 (millions)",,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Water Heating" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Number of Storage Tank Water Heaters"

332

" Million Housing Units, Final"  

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

9 Space Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" 9 Space Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" " ",,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Space Heating",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Space Heating Equipment" "Use Space Heating Equipment",110.1,25.8,17.8,4.7,3.8,2.3,7,8.1,2.3,3.9,1.8

333

" Million Housing Units, Final"  

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

Fuels Used and End Uses in U.S. Homes, by Housing Unit Type, 2009" Fuels Used and End Uses in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Fuels Used and End Uses" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Fuels Used for Any Use" "Electricity",113.6,71.8,6.7,9,19.1,6.9 "Natural Gas",69.2,45.6,4.7,6.1,11,1.8 "Propane/LPG",48.9,39.6,2.4,1.7,2,3.2 "Wood",13.1,11.4,0.3,0.2,0.5,0.7 "Fuel Oil",7.7,5.1,0.4,0.7,1.3,0.1

334

" Million Housing Units, Final"  

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

8 Structural and Geographic Characteristics of Homes in Northeast Region, Divisions, and States, 2009" 8 Structural and Geographic Characteristics of Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" "Structural and Geographic Characteristics",,"Total Northeast",,,"CT, ME, NH, RI, VT" ,,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Urban and Rural2" "Urban",88.1,18,4.4,2.2,2.2,13.6,6.6,3.9,3.1 "Rural",25.5,2.8,1.1,0.3,0.8,1.7,0.6,1,"Q"

335

" Million Housing Units, Final"  

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

2 Structural and Geographic Characteristics of U.S. Homes, by Owner/Renter Status, 2009" 2 Structural and Geographic Characteristics of U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4

336

" Million Housing Units, Final"  

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

4 Appliances in U.S. Homes, by Number of Household Members, 2009" 4 Appliances in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Appliances",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,28.8,31.7,16.3,14,11.5 "1.",100.8,28.5,31.2,16,13.9,11.2 "2 or More",1.5,0.3,0.5,0.2,0.2,0.3 "Do Not Use a Stove",11.3,2.5,4.1,1.8,1.7,1.2 "Most-Used Stove Fuel" "Electric",61.9,18.3,19.7,9.7,8.2,6.1

337

" Million Housing Units, Final"  

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

2 Computers and Other Electronics in U.S. Homes, by Owner/Renter Status, 2009" 2 Computers and Other Electronics in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" ,,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Computers and Other Electronics" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4

338

" Million Housing Units, Final"  

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

11 Water Heating in U.S. Homes in West Region, Divisions, and States, 2009" 11 Water Heating in U.S. Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Water Heating",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

339

" Million Housing Units, Final"  

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

7 Air Conditioning in U.S. Homes, by Census Region, 2009" 7 Air Conditioning in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Air Conditioning" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,16.5,22.4,40.5,14.6 "Have Air Conditioning Equipment But" "Do Not Use It",4.9,1.4,1.2,0.9,1.4 "Do Not Have Air Conditioning Equipment",14.7,2.8,2.3,0.7,8.9 "Type of Air Conditioning Equipment " "Used (more than one may apply)" "Use Central Air Conditioning Equipment",69.7,7.2,17.1,34.6,10.8

340

" Million Housing Units, Final"  

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

0 Fuels Used and End Uses in Homes in South Region, Divisions, and States, 2009" 0 Fuels Used and End Uses in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Fuels Used and End Uses",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

Note: This page contains sample records for the topic "forecasts million short" from the National Library of EnergyBeta (NLEBeta).
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341

" Million Housing Units, Final"  

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

8 Air Conditioning in Homes in Northeast Region, Divisions, and States, 2009" 8 Air Conditioning in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Air Conditioning",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,16.5,3.9,1.9,2,12.6,5.3,4.4,2.9 "Have Air Conditioning Equipment But"

342

" Million Housing Units, Final"  

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

0 Appliances in Homes in South Region, Divisions, and States, 2009" 0 Appliances in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Appliances",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

343

" Million Housing Units, Final"  

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

4 Air Conditioning in U.S. Homes, by Number of Household Members, 2009" 4 Air Conditioning in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Air Conditioning",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,24.6,30.2,15.1,13.5,10.6 "Have Air Conditioning Equipment But" "Do Not Use It",4.9,1.7,1.5,0.7,0.6,0.5 "Do Not Have Air Conditioning Equipment",14.7,5,4.1,2.3,1.7,1.7 "Type of Air Conditioning Equipment "

344

" Million Housing Units, Final"  

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

9 Structural and Geographic Characteristics of Homes in Midwest Region, Divisions, and States, 2009" 9 Structural and Geographic Characteristics of Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" ,,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Urban and Rural2" "Urban",88.1,19.9,14.6,4.1,2.9,1.8,5.8,5.3,1.6,2.4,1.4

345

" Million Housing Units, Final"  

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

5 Air Conditioning in U.S. Homes, by Household Income, 2009" 5 Air Conditioning in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Air Conditioning" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,18.3,22.3,17.9,11.9,8.1,5.1,10.4,12.8 "Have Air Conditioning Equipment But" "Do Not Use It",4.9,1.5,1.3,0.9,0.5,0.2,0.1,0.3,1

346

" Million Housing Units, Final"  

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

HC.1.11 Fuels Used and End Uses in Homes in West Region, Divisions, and States, 2009" HC.1.11 Fuels Used and End Uses in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Fuels Used and End Uses",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

347

" Million Housing Units, Final"  

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

5 Appliances in U.S. Homes, by Household Income, 2009" 5 Appliances in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Appliances" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,22.2,25.8,19.4,13,8.1,4.9,8.8,15.9 "1.",100.8,22,25.6,19.2,12.8,8,4.7,8.5,15.8

348

" Million Housing Units, Final"  

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

0 Televisions in Homes in South Region, Divisions, and States, 2009" 0 Televisions in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Televisions",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

349

" Million Housing Units, Final"  

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

0 Household Demographics of Homes in South Region, Divisions, and States, 2009" 0 Household Demographics of Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East South Central Census Division",,,"West South Central Census Division" ,,,,,,,,,"Total East South Central",,,"Total West South Central" ,"Total U.S.1 (millions)",,"Total South Atlantic" ,,"Total South",,,,,"DC, DE, MD, WV",,,,"AL, KY, MS",,,"AR, LA, OK" "Household Demographics",,,,"VA","GA","FL",,"NC, SC",,"TN",,,"TX" "Total Homes",113.6,42.1,22.2,3,3.5,7,3.4,5.4,7.1,2.4,4.6,12.8,8.5,4.2

350

" Million Housing Units, Final"  

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

8 Home Appliances in Homes in Northeast Region, Divisions, and States, 2009" 8 Home Appliances in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census Division",,,"Middle Atlantic Census Division" ,"Total U.S.1 (millions)",,"Total New England",,,"Total Middle Atlantic" ,,"Total Northeast",,,"CT, ME, NH, RI, VT" "Home Appliances",,,,"MA",,,"NY","PA","NJ" "Total Homes",113.6,20.8,5.5,2.5,3,15.3,7.2,4.9,3.2 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,19.2,5.2,2.3,2.8,14.1,6.8,4.6,2.7

351

" Million Housing Units, Final"  

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

7 Structural and Geographic Characteristics of U.S. Homes, by Census Region, 2009" 7 Structural and Geographic Characteristics of U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,"Northeast","Midwest","South","West" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Urban and Rural2" "Urban",88.1,18,19.9,28.6,21.5 "Rural",25.5,2.8,6,13.4,3.3 "Metropolitan and Micropolitan" "Statistical Area" "In metropolitan statistical area",94,18.6,19.4,33.4,22.7 "In micropolitan statistical area",12.4,1.5,4.7,4.7,1.5 "Not in metropolitan or micropolitan"

352

" Million Housing Units, Final"  

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

1 Appliances in Homes in West Region, Divisions, and States, 2009" 1 Appliances in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Appliances",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7 "Cooking Appliances"

353

" Million Housing Units, Final"  

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

1 Structural and Geographic Characteristics of Homes in West Region, Divisions, and States, 2009" 1 Structural and Geographic Characteristics of Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" "Structural and Geographic Characteristics",,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" ,,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

354

" Million Housing Units, Final"  

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

6 Appliances in U.S. Homes, by Climate Region, 2009" 6 Appliances in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Appliances",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,35.8,32.4,11.6,17.3,5.2 "1.",100.8,35.1,31.9,11.5,17.1,5.1 "2 or More",1.5,0.7,0.5,0.1,0.2,"Q" "Do Not Use a Stove",11.3,3,3,2.5,1.8,1.1 "Most-Used Stove Fuel"

355

" Million Housing Units, Final"  

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

Appliances in U.S. Homes, by Housing Unit Type, 2009" Appliances in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,,,,,"5 or More Units","Mobile Homes" "Appliances",,"Detached","Attached","2 to 4 Units" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,62.3,6.4,8.7,18.3,6.5 "1.",100.8,61,6.4,8.6,18.3,6.5 "2 or More",1.5,1.3,0.1,"Q","Q","Q" "Do Not Use a Stove",11.3,9.5,0.3,0.3,0.8,0.4

356

" Million Housing Units, Final"  

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

3 Appliances in U.S. Homes, by Year of Construction, 2009" 3 Appliances in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Appliances" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Cooking Appliances" "Stoves (Units With Both" "an Oven and a Cooktop)" "Use a Stove",102.3,13.2,4.9,12.3,11.2,16.5,15.6,14.9,13.7 "1.",100.8,13,4.8,12.2,10.9,16.3,15.4,14.7,13.5 "2 or More",1.5,0.2,0.1,0.2,0.2,0.2,0.2,0.1,0.2

357

" Million Housing Units, Final"  

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

1 Household Demographics of Homes in West Region, Divisions, and States, 2009" 1 Household Demographics of Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,,,,,"Pacific Census Division" ,,,,"Mountain North Sub-Division",,,"Mountain South Sub-Division" ,"Total U.S.1 (millions)",,,"Total Mountain North",,,"Total Mountain South" ,,"Total West","Total Mountain",,,"ID, MT, UT, WY",,,,"Total Pacific",,"AK, HI, OR, WA" "Household Demographics",,,,,"CO",,,"AZ","NM, NV",,"CA" "Total Homes",113.6,24.8,7.9,3.9,1.9,2,4,2.3,1.7,16.9,12.2,4.7

358

" Million Housing Units, Final"  

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

4 Structural and Geographic Characteristics of U.S. Homes, by Number of Household Members, 2009" 4 Structural and Geographic Characteristics of U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,,,,,"5 or More Members" ,,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Census Region and Division" "Northeast",20.8,6,6.3,3.3,3.1,2.1 "New England",5.5,1.5,1.8,1,0.7,0.5 "Middle Atlantic",15.3,4.5,4.5,2.3,2.4,1.6 "Midwest",25.9,7.4,8.5,3.9,3.5,2.6 "East North Central",17.9,5.1,5.6,2.7,2.5,1.9

359

" Million Housing Units, Final"  

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

9 Household Demographics of Homes in Midwest Region, Divisions, and States, 2009" 9 Household Demographics of Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census Division",,,,,"West North Central Census Division" ,,,"Total East North Central",,,,,"Total West North Central" ,"Total U.S.1 (millions)" ,,"Total Midwest",,,,," IN, OH",,,"IA, MN, ND, SD" "Household Demographics",,,,"IL","MI","WI",,,"MO",,"KS, NE" "Total Homes",113.6,25.9,17.9,4.8,3.8,2.3,7,8.1,2.3,3.9,1.8 "Number of Household Members" "1 Person",31.3,7.4,5.1,1.4,1,0.6,2.1,2.3,0.6,1.1,0.6

360

" Million Housing Units, Final"  

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

Water Heating in U.S. Homes, by Housing Unit Type, 2009" Water Heating in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Water Heating" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Number of Storage Tank Water Heaters" 0,2.9,1.8,0.1,0.2,0.6,0.1 1,108.1,67.5,6.5,8.8,18.5,6.8 "2 or More",2.7,2.5,0.1,"Q","Q","Q" "Number of Tankless Water Heaters2" 0,110.4,69.5,6.5,8.9,18.6,6.8 1,3.1,2.2,0.2,0.2,0.5,"Q"

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final"  

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

2 Fuels Used and End Uses in U.S. Homes, by Owner/Renter Status, 2009" 2 Fuels Used and End Uses in U.S. Homes, by Owner/Renter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With" ,,,,"Detached",,"Attached",,"2 to 4 Units",,"5 or More Units",,"Mobile Homes" ,"Total U.S.1 (millions)" "Fuels Used and End Uses",,"Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent","Own","Rent" "Total Homes",113.6,76.5,37.1,63.2,8.6,3.9,2.8,1.5,7.6,2.3,16.8,5.5,1.4 "Fuels Used for Any Use"

362

" Million Housing Units, Final"  

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

Space Heating in U.S. Homes, by Housing Unit Type, 2009" Space Heating in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Space Heating" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Space Heating Equipment" "Use Space Heating Equipment",110.1,70.5,6.5,8.7,17.7,6.7 "Have Space Heating Equipment But Do " "Not Use It",2.4,0.8,0.2,0.2,1,0.1 "Do Not Have Space Heating Equipment",1.2,0.6,"Q",0.1,0.4,"Q"

363

Chemical Hazards: Jury awards $85 million to soldiers exposed to hexavalent chromium in Iraq  

Science Journals Connector (OSTI)

Chemical Hazards: Jury awards $85 million to soldiers exposed to hexavalent chromium in Iraq ... A jury in Portland, Ore., has ordered military contractor Kellogg Brown & Root (KBR) to pay 12 U.S. soldiers a total of $85 million in damages after finding that the firm failed to protect the troops from exposure to hexavalent chromium, a known carcinogen, when they served in Iraq. ... The Army Corps of Engineers contracted KBR to reconstruct a water treatment facility near Basra, in southern Iraq, shortly after the U.S.-led invasion in March 2003. ...

GLENN HESS

2012-11-12T23:59:59.000Z

364

Utah Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Utah Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 1980's 68,211 95,670 93,934 98,598 99,233 241,904 274,470 286,592 286,929 1990's 334,067 333,591 319,017 348,010 368,585 308,174 265,546 249,930 242,070 211,514 2000's 169,553 166,505 136,843 161,275 193,093 187,524 193,836 195,701 202,380 412,639 2010's 454,832 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Utah Natural Gas Plant Processing

365

Michigan Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Michigan Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 171,531 156,996 143,802 1970's 139,571 141,784 94,738 37,384 45,106 79,154 151,318 172,578 199,347 1980's 155,984 151,560 137,364 148,076 151,393 142,255 137,687 125,183 123,578 1990's 134,550 170,574 186,144 201,985 196,000 179,678 117,119 86,564 83,052 67,514 2000's 58,482 50,734 47,292 41,619 37,977 34,545 33,213 29,436 30,008 23,819 2010's 22,405 21,518 21,243 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014

366

Arkansas Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Arkansas Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 93,452 88,011 56,190 1970's 37,816 31,387 17,946 26,135 19,784 17,918 20,370 18,630 18,480 1980's 29,003 31,530 33,753 34,572 258,648 174,872 197,781 213,558 228,157 1990's 272,278 224,625 156,573 198,074 218,710 100,720 219,477 185,244 198,148 179,524 2000's 207,045 207,352 12,635 13,725 10,139 16,756 13,702 11,532 6,531 2,352 2010's 9,599 5,611 6,872 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014

367

Louisiana Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Processed (Million Cubic Feet) Natural Gas Processed (Million Cubic Feet) Louisiana Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 3,383,334 3,728,717 4,465,379 1970's 5,237,519 5,994,431 6,337,328 6,524,729 6,273,136 5,831,487 5,749,783 5,709,535 5,561,040 1980's 5,197,429 4,770,095 4,190,105 4,439,430 3,811,852 3,794,464 3,880,364 3,918,236 4,002,843 1990's 4,220,068 4,340,531 4,466,425 4,315,312 4,200,126 4,604,292 4,652,677 4,767,965 4,610,969 4,687,261 2000's 4,316,127 4,206,470 3,771,001 3,391,870 3,244,850 2,527,636 2,511,802 2,857,443 2,208,920 2,175,026 2010's 2,207,760 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

368

California Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) California Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 505,063 476,596 455,692 1970's 444,700 431,605 386,664 359,841 252,402 213,079 216,667 206,981 204,693 1980's 169,812 261,725 263,475 276,209 281,389 263,823 276,969 270,191 254,286 1990's 263,667 246,335 243,692 246,283 228,346 226,548 240,566 243,054 235,558 259,518 2000's 260,049 258,271 249,671 238,743 236,465 226,230 223,580 206,239 195,272 198,213 2010's 204,327 180,648 169,203 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 1/7/2014

369

Alabama Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Alabama Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 57,208 1970's 0 0 0 0 0 0 25,517 31,610 32,806 1980's 38,572 41,914 38,810 42,181 45,662 48,382 49,341 52,511 55,939 1990's 58,136 76,739 126,910 132,222 136,195 118,688 112,868 114,411 107,334 309,492 2000's 372,136 285,953 290,164 237,377 263,426 255,157 287,278 257,443 253,028 248,232 2010's 242,444 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Alabama Natural Gas Plant Processing

370

Wyoming Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Wyoming Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 261,478 259,227 269,921 1970's 276,926 292,434 298,439 303,519 263,684 215,104 251,846 262,801 255,760 1980's 366,530 393,027 432,313 579,479 624,619 506,241 512,579 560,603 591,472 1990's 635,922 681,266 728,113 750,853 821,689 895,129 845,253 863,052 870,518 902,889 2000's 993,702 988,595 1,083,860 1,101,425 1,249,309 1,278,087 1,288,124 1,399,570 1,278,439 1,507,142 2010's 1,642,190 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 1/7/2014

371

Pennsylvania Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Pennsylvania Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 2,247 2,390 1,708 1970's 1,418 1,112 1,711 0 0 0 0 0 0 1980's 2,001 2,393 5,432 6,115 5,407 6,356 6,459 6,126 6,518 1990's 6,613 10,244 11,540 10,263 7,133 10,106 10,341 11,661 11,366 11,261 2000's 7,758 9,928 7,033 9,441 9,423 11,462 12,386 13,367 18,046 22,364 2010's 56,162 131,959 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Pennsylvania Natural Gas Plant Processing

372

Illinois Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Processed (Million Cubic Feet) Natural Gas Processed (Million Cubic Feet) Illinois Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 483,902 483,336 478,291 1970's 429,691 341,750 376,310 358,142 342,046 322,393 305,441 275,060 327,451 1980's 150,214 152,645 166,568 156,791 153,419 146,463 106,547 757 509 1990's 607 951 942 809 685 727 578 500 468 358 2000's 271 233 299 306 328 280 242 235 233 164 2010's 5,393 15,727 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Illinois Natural Gas Plant Processing

373

Texas Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Texas Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 7,018,237 7,239,621 7,613,234 1970's 7,808,476 7,938,550 8,139,408 7,683,830 7,194,453 6,509,132 6,253,159 6,030,131 5,621,419 1980's 4,563,931 4,507,771 4,258,852 4,377,799 4,164,382 4,199,501 3,997,226 3,813,727 3,842,395 1990's 3,860,388 4,874,718 4,231,145 4,301,504 4,160,551 4,132,491 4,180,062 4,171,967 4,073,739 3,903,351 2000's 4,096,535 3,876,399 3,861,114 3,658,929 3,748,670 3,781,565 3,990,862 4,187,358 4,431,574 4,478,331 2010's 4,534,403 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

374

Alaska Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Alaska Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 1970's 0 0 0 0 0 0 149,865 151,669 147,954 1980's 111,512 115,394 42,115 62,144 66,062 58,732 134,945 76,805 75,703 1990's 1,571,438 1,873,279 2,121,838 2,295,499 2,667,254 2,980,557 2,987,364 2,964,734 2,966,461 2,950,502 2000's 3,123,599 2,984,807 2,997,824 2,447,017 2,680,859 3,089,229 2,665,742 2,965,956 2,901,760 2,830,034 2010's 2,731,803 2,721,396 2,788,997 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 1/7/2014 Next Release Date: 1/31/2014

375

Montana Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Montana Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 60,500 59,058 57,793 1970's 59,193 57,105 61,757 56,960 146,907 156,203 0 0 0 1980's 11,825 13,169 15,093 16,349 19,793 16,212 14,177 15,230 15,475 1990's 14,629 14,864 12,697 11,010 10,418 9,413 10,141 8,859 8,715 5,211 2000's 5,495 5,691 6,030 6,263 6,720 10,057 12,685 13,646 13,137 12,415 2010's 12,391 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Montana Natural Gas Plant Processing

376

Colorado Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Colorado Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 112,440 96,397 85,171 1970's 82,736 97,420 104,116 110,662 118,686 136,090 175,624 171,233 167,959 1980's 201,637 220,108 173,894 181,150 191,625 163,614 180,290 178,048 196,682 1990's 208,069 234,851 256,019 307,250 353,855 345,441 493,963 374,728 425,083 444,978 2000's 494,581 497,385 534,295 555,544 703,804 730,948 751,036 888,705 1,029,641 1,233,260 2010's 1,434,003 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014

377

Oklahoma Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Oklahoma Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 1,038,103 1,122,692 1,167,150 1970's 1,183,273 1,123,614 1,116,872 1,175,548 1,092,487 1,033,003 1,072,992 1,057,326 1,069,293 1980's 1,063,256 1,112,740 1,023,057 1,118,403 1,137,463 1,103,062 1,127,780 1,301,673 1,145,688 1990's 1,102,301 1,100,812 1,071,426 1,082,452 1,092,734 1,015,965 1,054,123 1,014,008 947,177 892,396 2000's 963,464 957,665 854,220 804,029 839,366 865,411 908,055 964,709 1,047,643 1,112,510 2010's 1,110,236 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

378

Florida Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Processed (Million Cubic Feet) Natural Gas Processed (Million Cubic Feet) Florida Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 375,090 409,248 765,597 854,064 886,147 859,996 1980's 279,690 272,239 270,004 265,840 247,870 218,288 228,721 226,028 260,627 1990's 258,984 222,893 226,254 207,975 10,265 9,061 8,514 8,364 8,174 8,439 2000's 7,844 7,186 6,063 5,771 4,805 3,584 3,972 2,422 300 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 1/7/2014 Next Release Date: 1/31/2014 Referring Pages: Natural Gas Processed Florida Natural Gas Plant Processing

379

Mississippi Natural Gas Processed (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Processed (Million Cubic Feet) Processed (Million Cubic Feet) Mississippi Natural Gas Processed (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 46,068 44,510 0 1970's 50,509 44,732 29,538 29,081 24,568 29,694 0 0 0 1980's 34,337 38,315 29,416 29,705 23,428 21,955 12,131 9,565 8,353 1990's 7,887 7,649 4,822 4,892 5,052 4,869 4,521 4,372 3,668 135,773 2000's 205,106 239,830 263,456 283,675 283,763 292,023 278,436 224,596 174,573 215,951 2010's 218,840 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Natural Gas Processed Mississippi Natural Gas Plant Processing

380

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

Note: This page contains sample records for the topic "forecasts million short" 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

Energy Department Announces $15 Million to Integrate Affordable...  

Office of Environmental Management (EM)

and energy storage through: Advanced operation in conjunction with smart loads and demand response, Incorporation of solar and load forecasting, Innovative uses of smart components...

382

U.S. gasoline prices at its lowest since February 2011 (short version)  

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

U.S. crude oil output exceeds crude oil imports U.S. crude oil output exceeds crude oil imports Estimated U.S. crude oil production in October surpassed U.S. crude oil imports on a monthly basis for the first time since February 1995. Domestic crude oil output averaged 7.7 million barrels per day in October... the highest production for any October in 25 years while ... U.S. crude oil imports were 7.6 million barrels per day last month according to the new monthly forecast from the U.S. Energy Information Administration. . Crude oil production is forecast to average 7.5 million barrels per day this year and then jump to 8.5 million barrels per day in 2014. That would be the highest annual output since 1986. In response to higher oil production, EIA expects U.S. net petroleum imports to meet 28% of

383

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 EIA’s view on energy prices, demand, and production.

384

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

385

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 17-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 ...

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

386

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

387

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.

388

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...

389

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...

390

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

391

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

392

Short-Term Energy Outlook- May 2003  

Gasoline and Diesel Fuel Update (EIA)

3 3 1 Short-Term Energy Outlook May 2003 Overview World Oil Markets. The April 24 meeting of the Organization of Petroleum Exporting Countries (OPEC) raised official quotas for members (excluding Iraq) by 0.9 million barrels per day from the previous (suspended) quota to 25.4 million barrels per day. OPEC members also sought tighter compliance with quotas, calling for production cuts of 2 million barrels per day from April levels. We expect these measures to result in an average total OPEC (including Iraq) crude oil production rate of about 26.7 million barrels per day in the second and third quarters. This production level is not significantly different from our base case assumptions in last month's report. Individual OPEC country shares of these output levels will depend upon the speed with which

393

" Million Housing Units, Final"  

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

3 Televisions in U.S. Homes, by Year of Construction, 2009" 3 Televisions in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Televisions" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Televisions" "Number of Televisions" 0,1.5,0.4,"Q",0.2,0.1,0.3,0.2,0.1,0.1 1,24.2,4.2,1.2,3.1,3,3.8,3.7,2.9,2.3 2,37.5,4.7,1.9,4.3,4.5,6.4,6.2,5,4.4 3,26.6,2.9,1,3.1,3,4.4,3.7,4.1,4.4 4,14.2,1.4,0.5,1.6,1.7,2.1,1.9,2.3,2.7 "5 or More",9.7,0.9,0.4,1.2,1,1.3,1.3,1.9,1.7

394

" Million Housing Units, Final"  

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

4 Fuels Used and End Uses in U.S. Homes, by Number of Household Members, 2009" 4 Fuels Used and End Uses in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Fuels Used and End Uses",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Fuels Used for Any Use" "Electricity",113.6,31.3,35.8,18.1,15.7,12.7 "Natural Gas",69.2,18,21.6,11.5,9.8,8.2 "Propane/LPG",48.9,8.3,17.6,8.4,8.3,6.3 "Wood",13.1,2.3,4.8,2.3,2,1.7 "Fuel Oil",7.7,2.2,2.4,1.1,1.1,0.9 "Kerosene",1.7,0.4,0.5,0.3,0.2,0.3

395

" Million Housing Units, Final"  

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

3 Air Conditioning in U.S. Homes, by Year of Construction, 2009" 3 Air Conditioning in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Air Conditioning" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Air Conditioning Equipment" "Use Air Conditioning Equipment",94,10.5,4,10.6,10.5,15.1,14.1,14.7,14.4 "Have Air Conditioning Equipment But" "Do Not Use It",4.9,0.9,0.2,0.8,0.6,0.8,0.9,0.3,0.4 "Do Not Have Air Conditioning Equipment",14.7,3,0.9,2.2,2.2,2.4,2,1.3,0.8

396

" Million Housing Units, Final"  

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

5 Televisions in U.S. Homes, by Household Income, 2009" 5 Televisions in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Televisions" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Televisions" "Number of Televisions" 0,1.5,0.5,0.4,0.2,0.2,"Q","Q",0.1,0.3 1,24.2,8,6.4,4.2,2.5,1.1,0.7,1.2,4.8 2,37.5,8.7,10.3,6.7,4.6,2.7,1.4,3.1,5.9 3,26.6,4.2,6.3,5.4,3.6,2.6,1.6,2.9,3.5

397

" Million Housing Units, Final"  

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

3 Water Heating in U.S. Homes, by Year of Construction, 2009" 3 Water Heating in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Water Heating" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Number of Storage Tank Water Heaters" 0,2.9,0.5,0.2,0.3,0.3,0.5,0.3,0.3,0.5 1,108.1,13.7,4.9,13,12.8,17.5,16.3,15.6,14.4 "2 or More",2.7,0.3,"Q",0.3,0.2,0.3,0.4,0.5,0.6 "Number of Tankless Water Heaters2" 0,110.4,14,5,13.2,13,17.9,16.6,16,14.9

398

" Million Housing Units, Preliminary"  

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

Computers and Other Electronics in U.S. Homes, By Number of Household Members, 2009" Computers and Other Electronics in U.S. Homes, By Number of Household Members, 2009" " Million Housing Units, Preliminary" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Computers and Other Electronics",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Computers" "Number of Computers" 0,27.4,12.9,7.6,3,1.8,2.1 1,46.9,14.5,15.5,6.7,5.7,4.5 2,24.3,3.1,9.3,4.6,4.2,3.1 3,9.5,0.5,2.6,2.5,2.3,1.7 4,3.6,0.2,0.6,0.9,1.2,0.7 "5 or More",2,0.1,0.3,0.4,0.5,0.7 "Most-Used Computer" "Computer Type" "Desktop",48.3,10.5,16.2,7.9,7.4,6.3

399

" Million Housing Units, Final"  

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

5 Household Demographics of U.S. Homes, by Household Income, 2009" 5 Household Demographics of U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Household Demographics" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Number of Household Members" "1 Person",31.3,11.3,9.4,5,2.8,1.2,0.5,1,5 "2 Persons",35.8,5.7,8.3,7,5.3,3.1,2,4.3,3.9 "3 Persons",18.1,2.9,3.9,3.5,2.3,1.9,1.1,2.4,2.9

400

Nebraska Natural Gas Repressuring (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Repressuring (Million Cubic Feet) Repressuring (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1991 0 0 0 0 0 0 0 0 0 0 0 0 1992 0 0 0 0 0 0 0 0 0 0 0 0 1993 0 0 0 0 0 0 0 0 0 0 0 0 1994 0 0 0 0 0 0 0 0 0 0 0 0 1995 0 0 0 0 0 0 0 0 0 0 0 0 1996 0 0 0 0 0 0 0 0 0 0 0 0 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 0 0 0 0 0 0 0 0 0 0 0 0 1999 0 0 0 0 0 0 0 0 0 0 0 0 2000 0 0 0 0 0 0 0 0 0 0 0 0 2001 0 0 0 0 0 0 0 0 0 0 0 0 2002 0 0 0 0 0 0 0 0 0 0 0 0 2003 0 0 0 0 0 0 0 0 0 0 0 0 2004 0 0 0 0 0 0 0 0 0 0 0 0 2005 0 0 0 0 0 0 0 0 0 0 0 0 2006 0 0 0 0 0 0 0 0 0 0 0 0 2007 0 0 0 0 0 0 0 0 0 0 0 0

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final"  

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

7 Household Demographics of U.S. Homes, by Census Region, 2009" 7 Household Demographics of U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Number of Household Members" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Number of Household Members" "1 Person",31.3,6,7.4,11.5,6.3 "2 Persons",35.8,6.3,8.5,13.4,7.6 "3 Persons",18.1,3.3,3.9,6.8,4.1 "4 Persons",15.7,3.1,3.5,5.8,3.3 "5 Persons",7.7,1.3,1.7,2.8,2 "6 or More Persons",5,0.8,0.9,1.8,1.5 "2009 Annual Household Income" "Less than $20,000",23.7,4,5.5,10,4.3 "$20,000 to $39,999",27.5,4.3,6.5,10.7,6

402

" Million Housing Units, Final"  

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

7 Fuels Used and End Uses in U.S. Homes, by Census Region, 2009" 7 Fuels Used and End Uses in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Fuels Used and End Uses" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Fuels Used for Any Use" "Electricity",113.6,20.8,25.9,42.1,24.8 "Natural Gas",69.2,13.8,19.4,17.7,18.3 "Propane/LPG",48.9,9.4,12.1,16.5,11 "Wood",13.1,2.5,2.9,4,3.7 "Fuel Oil",7.7,6.3,0.5,0.7,0.2 "Kerosene",1.7,0.5,0.4,0.6,0.2 "Solar",1.2,0.2,0.2,0.3,0.5 "Electricity End Uses2" "(more than one may apply)"

403

" Million Housing Units, Final"  

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

3 Fuels Used and End Uses in U.S. Homes, by Year of Construction, 2009" 3 Fuels Used and End Uses in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Fuels Used and End Uses" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Fuels Used for Any Use" "Electricity",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Natural Gas",69.2,10.9,3.8,10,9.1,10.1,8.2,8.6,8.4 "Propane/LPG",48.9,5.9,1.9,5.7,4.9,7.6,6.9,8.1,7.9 "Wood",13.1,1.4,0.5,1.5,1.5,2.5,2.7,1.9,1.1

404

" Million Housing Units, Final"  

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

3 Structural and Geographic Characteristics of U.S. Homes, by Year of Construction, 2009" 3 Structural and Geographic Characteristics of U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Census Region and Division" "Northeast",20.8,5.6,1.3,3.4,2.6,2.6,2.2,1.5,1.6 "New England",5.5,1.8,0.3,0.7,0.6,0.7,0.8,0.3,0.4 "Middle Atlantic",15.3,3.8,1.1,2.7,2,1.9,1.4,1.1,1.2

405

" Million Housing Units, Final"  

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

4 Water Heating in U.S. Homes, by Number of Household Members, 2009" 4 Water Heating in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Water Heating",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Number of Storage Tank Water Heaters" 0,2.9,0.9,0.8,0.4,0.4,0.3 1,108.1,30.2,33.9,17.3,14.9,11.9 "2 or More",2.7,0.2,1.1,0.4,0.4,0.5 "Number of Tankless Water Heaters2" 0,110.4,30.6,34.8,17.5,15.2,12.3 1,3.1,0.7,1,0.5,0.5,0.4 "2 or More",0.1,"Q","Q","Q","Q","Q"

406

" Million Housing Units, Final"  

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

5 Computers and Other Electronics in U.S. Homes, by Household Income, 2009" 5 Computers and Other Electronics in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Computers and Other Electronics" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Computers" "Number of Computers" 0,27.4,12.4,9,3.6,1.5,0.4,0.1,0.3,8.4 1,46.9,8.2,13,10.6,6.7,3.7,1.8,2.8,5.8 2,24.3,2.2,4.1,4.6,3.7,2.9,2.2,4.6,1.8 3,9.5,0.6,1,1.6,1.5,1.6,1,2.3,0.5

407

Ohio Natural Gas Repressuring (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Repressuring (Million Cubic Feet) Repressuring (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1991 0 0 0 0 0 0 0 0 0 0 0 0 1992 0 0 0 0 0 0 0 0 0 0 0 0 1993 0 0 0 0 0 0 0 0 0 0 0 0 1994 0 0 0 0 0 0 0 0 0 0 0 0 1995 0 0 0 0 0 0 0 0 0 0 0 0 1996 0 0 0 0 0 0 0 0 0 0 0 0 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 0 0 0 0 0 0 0 0 0 0 0 0 1999 0 0 0 0 0 0 0 0 0 0 0 0 2000 0 0 0 0 0 0 0 0 0 0 0 0 2001 0 0 0 0 0 0 0 0 0 0 0 0 2002 0 0 0 0 0 0 0 0 0 0 0 0 2003 0 0 0 0 0 0 0 0 0 0 0 0 2004 0 0 0 0 0 0 0 0 0 0 0 0 2005 0 0 0 0 0 0 0 0 0 0 0 0 2006 0 0 0 0 0 0 0 0 0 0 0 0 2007 0 0 0 0 0 0 0 0 0 0 0 0

408

Oklahoma Natural Gas Repressuring (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Repressuring (Million Cubic Feet) Repressuring (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1996 - - - - - - - - - - - - 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 0 0 0 0 0 0 0 0 0 0 0 0 1999 0 0 0 0 0 0 0 0 0 0 0 0 2000 0 0 0 0 0 0 0 0 0 0 0 0 2001 0 0 0 0 0 0 0 0 0 0 0 0 2002 0 0 0 0 0 0 0 0 0 0 0 0 2003 0 0 0 0 0 0 0 0 0 0 0 0 2004 0 0 0 0 0 0 0 0 0 0 0 0 2005 0 0 0 0 0 0 0 0 0 0 0 0 2006 0 0 0 0 0 0 0 0 0 0 0 0 2007 0 0 0 0 0 0 0 0 0 0 0 0 2008 0 0 0 0 0 0 0 0 0 0 0 0 2009 0 0 0 0 0 0 0 0 0 0 0 0 2010 0 0 0 0 0 0 0 0 0 0 0 0 2011 0 0 0 0 0 0 0 0 0 0 0 0 2012 0 0 0 0 0 0 0 0 0 0 0 0

409

" Million Housing Units, Final"  

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

6 Televisions in U.S. Homes, by Climate Region, 2009" 6 Televisions in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Televisions",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Televisions" "Number of Televisions" 0,1.5,0.6,0.4,0.2,0.2,0.2 1,24.2,8.5,6.9,2.7,4.1,2 2,37.5,12.4,11.6,5.1,6.2,2.1 3,26.6,8.9,8.5,3.2,4.7,1.2 4,14.2,4.8,4.7,1.8,2.4,0.6 "5 or More",9.7,3.6,3.3,1,1.5,0.3 "Most-Used Television" "Display Size" "Less than 21 Inches",12.5,4.4,4,1.3,2.1,0.6

410

" Million Housing Units, Final"  

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

7 Computers and Other Electronics in U.S. Homes, by Census Region, 2009" 7 Computers and Other Electronics in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Computers and Other Electronics" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Computers" "Number of Computers" 0,27.4,4.7,6.7,11.1,4.8 1,46.9,8.7,10.6,17.2,10.3 2,24.3,4.3,5.5,8.7,5.8 3,9.5,2.1,1.9,3.1,2.4 4,3.6,0.7,0.8,1.4,0.8 "5 or More",2,0.4,0.3,0.6,0.7 "Most-Used Computer" "Computer Type" "Desktop",48.3,9.1,11,16.8,11.4 "Flat-Panel LCD Monitor",38.3,7.3,8.4,13.4,9.3 "Standard Monitor",10,1.9,2.6,3.4,2.1

411

" Million Housing Units, Final"  

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

4 Televisions in U.S. Homes, by Number of Household Members, 2009" 4 Televisions in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Televisions",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Televisions" "Number of Televisions" 0,1.5,1,0.3,"Q","Q",0.1 1,24.2,12.9,6.7,2.2,1.3,1.1 2,37.5,11.6,13.6,5.4,4,2.8 3,26.6,4,9.1,5.7,4.5,3.3 4,14.2,1.2,3.9,2.9,3.3,2.8 "5 or More",9.7,0.6,2.1,1.7,2.6,2.7 "Most-Used Television" "Display Size" "Less than 21 Inches",12.5,4.9,3.9,1.4,1.1,1.2

412

" Million Housing Units, Final"  

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

4 Household Demographics of U.S. Homes, by Number of Household Members, 2009" 4 Household Demographics of U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More Members" "Number of Household Members",,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "2009 Annual Household Income" "Less than $20,000",23.7,11.3,5.7,2.9,2,1.9 "$20,000 to $39,999",27.5,9.4,8.3,3.9,3,2.9 "$40,000 to $59,000",21.2,5,7,3.5,3,2.6 "$60,000 to $79,999",14.2,2.8,5.3,2.3,2.2,1.6 "$80,000 to $99,999",9.3,1.2,3.1,1.9,1.7,1.3 "$100,000 to $119,999",5.7,0.5,2,1.1,1.4,0.7

413

" Million Housing Units, Final"  

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

6 Water Heating in U.S. Homes, by Climate Region, 2009" 6 Water Heating in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Water Heating",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Number of Storage Tank Water Heaters" 0,2.9,1.3,0.8,0.4,0.4,0.1 1,108.1,36.8,33.9,13.3,18,6 "2 or More",2.7,0.7,0.8,0.4,0.7,0.1 "Number of Tankless Water Heaters3" 0,110.4,37.4,34.6,13.7,18.5,6.1 1,3.1,1.3,0.8,0.4,0.5,0.1 "2 or More",0.1,"Q","Q","Q","Q","N"

414

" Million Housing Units, Final"  

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

3 Computers and Other Electronics in U.S. Homes, by Year of Construction, 2009" 3 Computers and Other Electronics in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Computers and Other Electronics" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Computers" "Number of Computers" 0,27.4,3.8,1.7,3.8,3.9,4.6,4.4,3.1,2.1 1,46.9,6,2.1,5.3,5.4,7.8,7,6.5,6.7 2,24.3,3,0.9,2.8,2.5,3.7,3.3,4.1,4 3,9.5,1.1,0.3,1.1,1,1.4,1.4,1.7,1.6 4,3.6,0.4,0.1,0.4,0.3,0.6,0.5,0.6,0.8

415

" Million Housing Units, Final"  

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

3 Space Heating in U.S. Homes, by Year of Construction, 2009" 3 Space Heating in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Space Heating" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Space Heating Equipment" "Use Space Heating Equipment",110.1,14.2,5,13.1,12.7,17.5,16.4,16,15.2 "Have Space Heating Equipment But Do " "Not Use It",2.4,0.2,"Q",0.3,0.3,0.5,0.4,0.3,0.3 "Do Not Have Space Heating Equipment",1.2,0.1,0.1,0.1,0.2,0.3,0.1,0.1,"Q"

416

" Million Housing Units, Final"  

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

7 Televisions in U.S. Homes, by Census Region, 2009" 7 Televisions in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Televisions" "Total Homes",113.6,20.8,25.9,42.1,24.8 "Televisions" "Number of Televisions" 0,1.5,0.4,0.3,0.5,0.4 1,24.2,4.6,5.4,8.1,6.1 2,37.5,7,8,13.8,8.5 3,26.6,4.5,6.1,10.5,5.3 4,14.2,2.2,3.4,5.7,2.9 "5 or More",9.7,1.9,2.7,3.4,1.5 "Most-Used Television" "Display Size" "Less than 21 Inches",12.5,2.3,3,4.7,2.5 "21 to 36 Inches",53.6,10.5,12.8,19.5,10.9 "37 Inches or More",46,7.6,9.8,17.4,11.1

417

Arizona Natural Gas Repressuring (Million Cubic Feet)  

Gasoline and Diesel Fuel Update (EIA)

Repressuring (Million Cubic Feet) Repressuring (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1996 - - - - - - - - - - - - 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 0 0 0 0 0 0 0 0 0 0 0 0 1999 0 0 0 0 0 0 0 0 0 0 0 0 2000 0 0 0 0 0 0 0 0 0 0 0 0 2001 0 0 0 0 0 0 0 0 0 0 0 0 2002 0 0 0 0 0 0 0 0 0 0 0 0 2003 0 0 0 0 0 0 0 0 0 0 0 0 2004 0 0 0 0 0 0 0 0 0 0 0 0 2005 0 0 0 0 0 0 0 0 0 0 0 0 2006 0 0 0 0 0 0 0 0 0 0 0 0 2007 0 0 0 0 0 0 0 0 0 0 0 0 2008 0 0 0 0 0 0 0 0 0 0 0 0 2009 0 0 0 0 0 0 0 0 0 0 0 0 2010 0 0 0 0 0 0 0 0 0 0 0 0 2011 0 0 0 0 0 0 0 0 0 0 0 0 2012 0 0 0 0 0 0 0 0 0 0 0 0

418

" Million Housing Units, Final"  

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

3 Household Demographics of U.S. Homes, by Year of Construction, 2009" 3 Household Demographics of U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999","2000 to 2009" "Household Demographics" "Total Homes",113.6,14.4,5.2,13.5,13.3,18.3,17,16.4,15.6 "Number of Household Members" "1 Person",31.3,4.8,1.5,3.9,3.9,5,5,3.9,3.3 "2 Persons",35.8,4.5,1.6,4.1,4.3,6.2,5.2,5.2,4.6 "3 Persons",18.1,2,0.9,2.3,2.1,2.8,2.9,2.7,2.4 "4 Persons",15.7,1.8,0.6,1.8,1.6,2.2,2.1,2.6,3.1

419

" Million Housing Units, Final"  

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

5 Water Heating in U.S. Homes, by Household Income, 2009" 5 Water Heating in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,,,,,,"Below Poverty Line2" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 to $99,999","$100,000 to $119,999","$120,000 or More" "Water Heating" "Total Homes",113.6,23.7,27.5,21.2,14.2,9.3,5.7,12,16.9 "Number of Storage Tank Water Heaters" 0,2.9,0.7,0.5,0.3,0.4,0.3,0.2,0.5,0.5 1,108.1,22.9,26.7,20.4,13.4,8.7,5.3,10.6,16.3 "2 or More",2.7,0.1,0.3,0.4,0.4,0.3,0.2,0.9,0.1 "Number of Tankless Water Heaters3"

420

" Million Housing Units, Final"  

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

Televisions in U.S. Homes, by Housing Unit Type, 2009" Televisions in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Televisions" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Televisions" "Number of Televisions" 0,1.5,0.5,0.1,0.2,0.6,"Q" 1,24.2,11,1.2,3,7.3,1.7 2,37.5,21.4,2.4,3.3,7.7,2.7 3,26.6,18.4,2,1.8,2.8,1.6 4,14.2,11.6,0.7,0.6,0.5,0.7 "5 or More",9.7,8.8,0.4,0.2,"Q",0.2 "Most-Used Television" "Display Size"

Note: This page contains sample records for the topic "forecasts million short" 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

" Million Housing Units, Final"  

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

Computers and Other Electronics in U.S. Homes, by Housing Unit Type, 2009" Computers and Other Electronics in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1 (millions)" ,," Detached"," Attached"," 2 to 4 Units","5 or More Units","Mobile Homes" "Computers and Other Electronics" "Total Homes",113.6,71.8,6.7,9,19.1,6.9 "Computers" "Number of Computers" 0,27.4,13.3,1.6,3.1,6.2,3.2 1,46.9,29,3,3.9,8.4,2.6 2,24.3,17.4,1.2,1.5,3.4,0.8 3,9.5,7.5,0.6,0.4,0.8,0.2 4,3.6,3,0.2,0.1,0.2,"Q" "5 or More",2,1.7,0.1,"Q",0.1,"Q"

422

" Million Housing Units, Final"  

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

6 Fuels Used and End Uses in U.S. Homes, by Climate Region, 2009" 6 Fuels Used and End Uses in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold/","Mixed- Humid","Mixed-Dry/" "Fuels Used and End Uses",,"Cold",,"Hot-Dry","Hot-Humid","Marine" "Total Homes",113.6,38.8,35.4,14.1,19.1,6.3 "Fuels Used for Any Use" "Electricity",113.6,38.8,35.4,14.1,19.1,6.3 "Natural Gas",69.2,27.4,19.8,11.3,6.7,4 "Propane/LPG",48.9,19.6,14.6,5.5,6.8,2.5 "Wood",13.1,5,4,1.5,1.4,1.2 "Fuel Oil",7.7,4.6,2.9,"Q","Q","Q" "Kerosene",1.7,0.8,0.7,0.1,"Q","Q"

423

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

424

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

425

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

426

Sabine Pass, LA Exports to Brazil Liquefied Natural Gas (Million...  

Gasoline and Diesel Fuel Update (EIA)

Brazil Liquefied Natural Gas (Million Cubic Feet) Sabine Pass, LA Exports to Brazil Liquefied Natural Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec...

427

Freeport, TX Liquefied Natural Gas Exports to Brazil (Million...  

Gasoline and Diesel Fuel Update (EIA)

to Brazil (Million Cubic Feet) Freeport, TX Liquefied Natural Gas Exports to Brazil (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2011 2,581 2014 2,664...

428

Energy Department Invests More Than $7 Million to Deploy Tribal...  

Office of Environmental Management (EM)

Invests More Than 7 Million to Deploy Tribal Clean Energy Energy Department Invests More Than 7 Million to Deploy Tribal Clean Energy November 20, 2013 - 12:00am Addthis The...

429

Cameron, LA Liquefied Natural Gas Imports from Egypt (Million...  

Annual Energy Outlook 2012 (EIA)

Egypt (Million Cubic Feet) Cameron, LA Liquefied Natural Gas Imports from Egypt (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2011 2,971 - No Data...

430

South Carolina Natural Gas LNG Storage Withdrawals (Million Cubic...  

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

Withdrawals (Million Cubic Feet) South Carolina Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

431

South Dakota Natural Gas LNG Storage Withdrawals (Million Cubic...  

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

Withdrawals (Million Cubic Feet) South Dakota Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

432

Gulf LNG, Mississippi Liquefied Natural Gas Imports (Million...  

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

Liquefied Natural Gas Imports (Million Cubic Feet) Gulf LNG, Mississippi Liquefied Natural Gas Imports (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

433

Arkansas Natural Gas LNG Storage Additions (Million Cubic Feet...  

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

Additions (Million Cubic Feet) Arkansas Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

434

Alaska Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

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

Net Withdrawals (Million Cubic Feet) Alaska Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

435

Alabama Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Alabama Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

436

New Jersey Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) New Jersey Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

437

California Natural Gas LNG Storage Additions (Million Cubic Feet...  

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

Additions (Million Cubic Feet) California Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

438

Louisiana Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Louisiana Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

439

Rhode Island Natural Gas LNG Storage Net Withdrawals (Million...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Rhode Island Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

440

Colorado Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Colorado Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

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


441

Oregon Natural Gas LNG Storage Additions (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Oregon Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 251...

442

Tennessee Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Tennessee Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

443

Louisiana Natural Gas LNG Storage Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Withdrawals (Million Cubic Feet) Louisiana Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

444

Washington Natural Gas LNG Storage Additions (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Washington Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

445

Delaware Natural Gas LNG Storage Additions (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Delaware Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

446

Alabama Natural Gas LNG Storage Additions (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Alabama Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

447

Maine Natural Gas LNG Storage Additions (Million Cubic Feet)  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Maine Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 0...

448

Minnesota Natural Gas LNG Storage Additions (Million Cubic Feet...  

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

Additions (Million Cubic Feet) Minnesota Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

449

Maryland Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Maryland Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

450

Nevada Natural Gas LNG Storage Additions (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Additions (Million Cubic Feet) Nevada Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 294...

451

Idaho Natural Gas LNG Storage Additions (Million Cubic Feet)  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Idaho Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 110...

452

Georgia Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Georgia Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

453

Nebraska Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Nebraska Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

454

New York Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) New York Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

455

Minnesota Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Minnesota Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

456

Illinois Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Illinois Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

457

Washington Natural Gas LNG Storage Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Washington Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

458

New Hampshire Natural Gas LNG Storage Additions (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Additions (Million Cubic Feet) New Hampshire Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

459

Connecticut Natural Gas LNG Storage Net Withdrawals (Million...  

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

Net Withdrawals (Million Cubic Feet) Connecticut Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

460

Louisiana Natural Gas LNG Storage Additions (Million Cubic Feet...  

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

Additions (Million Cubic Feet) Louisiana Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

Note: This page contains sample records for the topic "forecasts million short" 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

Connecticut Natural Gas LNG Storage Additions (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Connecticut Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

462

Maryland Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Maryland Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

463

Maine Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Withdrawals (Million Cubic Feet) Maine Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

464

Illinois Natural Gas LNG Storage Additions (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Additions (Million Cubic Feet) Illinois Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

465

Delaware Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Delaware Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

466

Massachusetts Natural Gas LNG Storage Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Withdrawals (Million Cubic Feet) Massachusetts Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

467

Missouri Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Missouri Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

468

Colorado Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Withdrawals (Million Cubic Feet) Colorado Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

469

Iowa Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Iowa Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

470

Minnesota Natural Gas LNG Storage Withdrawals (Million Cubic...  

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

Withdrawals (Million Cubic Feet) Minnesota Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

471

Washington Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Washington Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

472

New Hampshire Natural Gas LNG Storage Net Withdrawals (Million...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) New Hampshire Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

473

Arkansas Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Arkansas Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

474

Indiana Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

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

Net Withdrawals (Million Cubic Feet) Indiana Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

475

North Carolina Natural Gas LNG Storage Net Withdrawals (Million...  

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

Net Withdrawals (Million Cubic Feet) North Carolina Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

476

New York Natural Gas LNG Storage Additions (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Additions (Million Cubic Feet) New York Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

477

Missouri Natural Gas LNG Storage Additions (Million Cubic Feet...  

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

Additions (Million Cubic Feet) Missouri Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 0...

478

Nebraska Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Nebraska Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

479

Delaware Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Delaware Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

480

Nevada Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Nevada Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

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


481

California Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) California Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

482

California Natural Gas LNG Storage Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) California Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

483

Iowa Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet...  

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

Net Withdrawals (Million Cubic Feet) Iowa Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

484

Georgia Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Georgia Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

485

Maryland Natural Gas LNG Storage Additions (Million Cubic Feet...  

Annual Energy Outlook 2012 (EIA)

Additions (Million Cubic Feet) Maryland Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

486

Pennsylvania Natural Gas LNG Storage Net Withdrawals (Million...  

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

Net Withdrawals (Million Cubic Feet) Pennsylvania Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

487

Idaho Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Idaho Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

488

Oregon Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Oregon Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

489

Arkansas Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Arkansas Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

490

South Dakota Natural Gas LNG Storage Net Withdrawals (Million...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) South Dakota Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

491

Virginia Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Virginia Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

492

Idaho Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Withdrawals (Million Cubic Feet) Idaho Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's...

493

Maine Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

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

Net Withdrawals (Million Cubic Feet) Maine Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

494

Virginia Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Net Withdrawals (Million Cubic Feet) Virginia Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

495

Nevada Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Nevada Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

496

Indiana Natural Gas LNG Storage Withdrawals (Million Cubic Feet...  

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

Withdrawals (Million Cubic Feet) Indiana Natural Gas LNG Storage Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

497

Missouri Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

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

Net Withdrawals (Million Cubic Feet) Missouri Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

498

Illinois Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Illinois Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

499

Alaska Natural Gas LNG Storage Additions (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Additions (Million Cubic Feet) Alaska Natural Gas LNG Storage Additions (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's...

500

Alabama Natural Gas LNG Storage Net Withdrawals (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Net Withdrawals (Million Cubic Feet) Alabama Natural Gas LNG Storage Net Withdrawals (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...