National Library of Energy BETA

Sample records for base forecast natural

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

  2. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

    This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

  3. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

    On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO

  4. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

    On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

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

  6. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    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.

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e

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

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark A; Bolinger, Mark; Wiser, Ryan

    2008-01-07

    On December 12, 2007, the reference-case projections from Annual Energy Outlook 2008 (AEO 2008) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof) or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers (though its appeal has diminished somewhat as prices have increased); and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal and

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

    On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired or nuclear generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers; and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal, uranium, and

  11. How regulators should use natural gas price forecasts

    SciTech Connect (OSTI)

    Costello, Ken

    2010-08-15

    Natural gas prices are critical to a range of regulatory decisions covering both electric and gas utilities. Natural gas prices are often a crucial variable in electric generation capacity planning and in the benefit-cost relationship for energy-efficiency programs. High natural gas prices can make coal generation the most economical new source, while low prices can make natural gas generation the most economical. (author)

  12. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect (OSTI)

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

    2009-03-01

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

  13. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect (OSTI)

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

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

  14. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect (OSTI)

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  15. Base Oil Market Segment Forecasts up to 2020,Research Reports...

    Open Energy Info (EERE)

    Market Research Home > Groups > Future of Condition Monitoring for Wind Turbines Wayne31jan's picture Submitted by Wayne31jan(150) Contributor 11 June, 2015 - 03:19 Base Oil...

  16. Assessment and Forecasting Natural Gas Reserve Appreciation in the Gulf Coast Basin

    SciTech Connect (OSTI)

    Kim, E.M.; Fisher, W.L.

    1997-10-01

    Reserve appreciation, also called reserve growth, is the increase in the estimated ultimate recovery (the sum of year end reserves and cumulative production) from fields subsequent to discovery from extensions, infield drilling, improved recovery of in-place resources, new pools, and intrapool completions. In recent years, reserve appreciation has become a major component of total U.S. annual natural gas reserve additions. Over the past 15 years, reserve appreciation has accounted for more than 80 percent of all annual natural gas reserve additions in the U.S. lower 48 states (Figure 1). The rise of natural gas reserve appreciation basically came with the judgment that reservoirs were much more geologically complex than generally thought, and they hold substantial quantities of natural gas in conventionally movable states that are not recovered by typical well spacing and vertical completion practices. Considerable evidence indicates that many reservoirs show significant geological variations and compartmentalization, and that uniform spacing, unless very dense, does not efficiently tap and drain a sizable volume of the reservoir (Figure 2). Further, by adding reserves within existing infrastructure and commonly by inexpensive recompletion technology in existing wells, reserve appreciation has become the dominant factor in ample, low-cost natural gas supply. Although there is a wide range in natural gas reserve appreciation potential by play and that potential is a function of drilling and technology applied, current natural gas reserve appreciation studies are gross, averaging wide ranges, disaggregated by broad natural gas provinces, and calculated mainly as a function of time. A much more detailed analysis of natural gas reserve appreciation aimed at assessing long-term sustainability, technological amenability, and economic factors, however, is necessary. The key to such analysis is a disaggregation to the play level. Plays are the geologically homogeneous

  17. New Mexico Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) New Mexico Natural Gas in Underground Storage (Base Gas) ... Underground Base Natural Gas in Storage - All Operators New Mexico Underground Natural Gas ...

  18. New York Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) New York Natural Gas in Underground Storage (Base Gas) ... Underground Base Natural Gas in Storage - All Operators New York Underground Natural Gas ...

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

    SciTech Connect (OSTI)

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

    2014-05-01

    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.

  20. Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

    SciTech Connect (OSTI)

    Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar; Marianno, Fernando J.; Shao, Xiaoyan; Zhang, Jie; Hodge, Bri-Mathias; Hamann, Hendrik F.

    2015-07-15

    With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

  1. Community-Based Forest (Natural) Resource Management: A Path...

    Open Energy Info (EERE)

    Based Forest (Natural) Resource Management: A Path to Sustainable Environment and Development Jump to: navigation, search Name Community-Based Forest (Natural) Resource Management:...

  2. Solar Forecasting

    Broader source: Energy.gov [DOE]

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

  3. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect (OSTI)

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

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  4. Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  5. New Mexico Natural Gas Liquids Lease Condensate, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) New Mexico Natural Gas Liquids Lease ... Referring Pages: Lease Condensate Estimated Production New Mexico Lease Condensate Proved ...

  6. Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  7. New Mexico Natural Gas Plant Liquids, Reserves Based Production...

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

    Reserves Based Production (Million Barrels) New Mexico Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  8. An economic feasibility analysis of distributed electric power generation based upon the Natural Gas-Fired Fuel Cell: a model of the operations cost.

    SciTech Connect (OSTI)

    Not Available

    1993-06-30

    This model description establishes the revenues, expenses incentives and avoided costs of Operation of a Natural Gas-Fired Fuel Cell-Based. Fuel is the major element of the cost of operation of a natural gas-fired fuel cell. Forecasts of the change in the price of this commodity a re an important consideration in the ownership of an energy conversion system. Differences between forecasts, the interests of the forecaster or geographical areas can all have significant effects on imputed fuel costs. There is less effect on judgments made on the feasibility of an energy conversion system since changes in fuel price can affect the cost of operation of the alternatives to the fuel cell in a similar fashion. The forecasts used in this model are only intended to provide the potential owner or operator with the means to examine alternate future scenarios. The operations model computes operating costs of a system suitable for a large condominium complex or a residential institution such as a hotel, boarding school or prison. The user may also select large office buildings that are characterized by 12 to 16 hours per day of operation or industrial users with a steady demand for thermal and electrical energy around the clock.

  9. Science on the Hill: The forecast calls for flu

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

    The forecast calls for flu The forecast calls for flu Using mathematics, computer programs, statistics and information about how disease develops and spreads, a research team at Los Alamos National Laboratory found a way to forecast the flu season and even next week's sickness trends. January 15, 2016 Forecasting flu A team from Los Alamos has developed a method to predict flu outbreaks based in part on influenza-related searches of Wikipedia. The forecast calls for flu Beyond the familiar flu,

  10. Forecast Change

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

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (cents/kWh) 12.06 12.09 12.58 13.04 12.95 12.84 -0.9% Expenditures $415 $405 $393 $396 $408 $424 3.9% New England Usage (kWh) 2,122 2,188 2,173 1,930 1,992 2,082 4.5% Price (cents/kWh) 15.85 15.50 16.04 17.63 18.64 18.37 -1.5% Expenditures $336 $339 $348 $340 $371 $382 3.0% Mid-Atlantic Usage (kWh) 2,531 2,548 2,447 2,234 2,371 2,497 5.3% Price (cents/kWh) 16.39 15.63

  11. DOE Announces Webinars on Solar Forecasting Metrics, the DOE...

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

    DOE Announces Webinars on Solar Forecasting Metrics, the DOE ... from adopting the latest energy efficiency and renewable ... to liquids technology, advantages of using natural gas, ...

  12. ASSESSING AND FORECASTING, BY PLAY, NATURAL GAS ULTIMATE RECOVERY GROWTH AND QUANTIFYING THE ROLE OF TECHNOLOGY ADVANCEMENTS IN THE TEXAS GULF COAST BASIN AND EAST TEXAS

    SciTech Connect (OSTI)

    William L. Fisher; Eugene M. Kim

    2000-12-01

    A detailed natural gas ultimate recovery growth (URG) analysis of the Texas Gulf Coast Basin and East Texas has been undertaken. The key to such analysis was determined to be the disaggregation of the resource base to the play level. A play is defined as a conceptual geologic unit having one or more reservoirs that can be genetically related on the basis of depositional origin of the reservoir, structural or trap style, source rocks and hydrocarbon generation, migration mechanism, seals for entrapment, and type of hydrocarbon produced. Plays are the geologically homogeneous subdivision of the universe of petroleum pools within a basin. Therefore, individual plays have unique geological features that can be used as a conceptual model that incorporates geologic processes and depositional environments to explain the distribution of petroleum. Play disaggregation revealed important URG trends for the major natural gas fields in the Texas Gulf Coast Basin and East Texas. Although significant growth and future potential were observed for the major fields, important URG trends were masked by total, aggregated analysis based on a broad geological province. When disaggregated by plays, significant growth and future potential were displayed for plays that were associated with relatively recently discovered fields, deeper reservoir depths, high structural complexities due to fault compartmentalization, reservoirs designated as tight gas/low-permeability, and high initial reservoir pressures. Continued technology applications and advancements are crucial in achieving URG potential in these plays.

  13. Colorado Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Colorado Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 11 10 9 8 9 8 8 9 10 1990's 10 12 13 14 15 18 17 21 18 19 2000's 21 22 23 24 26 26 26 27 38 48 2010's 58 63 57 52 61 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015

  14. Kansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 29 1980's 26 24 14 17 20 20 19 19 18 18 1990's 17 26 27 27 29 29 31 24 28 30 2000's 28 26 25 22 22 19 18 18 18 16 2010's 16 16 15 11 12 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  15. Lower 48 States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Lower 48 States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 579 1980's 572 580 564 568 597 580 566 569 572 549 1990's 556 577 599 608 608 616 655 655 631 649 2000's 688 655 657 593 627 597 615 637 654 701 2010's 734 773 854 920 1,107 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  16. Michigan Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Michigan Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 11 1980's 12 12 11 10 10 8 9 8 8 8 1990's 6 6 6 5 5 5 5 4 4 4 2000's 4 4 3 3 3 3 2 3 3 2 2010's 3 2 2 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  17. Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 8 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 1 1 1 1 0 2010's 0 0 0 1 24 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  18. North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 3 4 4 5 6 6 5 6 5 5 1990's 5 5 5 5 4 4 4 4 4 4 2000's 5 5 5 4 5 5 6 6 6 8 2010's 9 11 19 26 36 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  19. Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 59 1980's 62 65 67 70 75 77 76 76 79 73 1990's 75 76 77 77 76 70 74 71 69 70 2000's 69 66 61 59 64 65 67 69 74 77 2010's 82 88 96 99 117 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  20. Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Liquids Lease Condensate, Reserves Based Production (Million Barrels) Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 4 1980's 5 11 8 20 26 31 31 28 25 23 1990's 16 17 15 14 14 9 8 8 8 14 2000's 7 11 11 10 10 12 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  1. West Virginia Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) West Virginia Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 6 6 5 5 6 7 6 6 7 7 1990's 7 7 7 7 6 4 4 4 4 4 2000's 6 6 6 4 4 4 5 5 5 5 2010's 5 5 8 10 41 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  2. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect (OSTI)

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  3. Wind Power Forecasting Data

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

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  4. probabilistic energy production forecasts

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

    energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary ...

  5. Wind Power Forecasting

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

    data Presentations BPA Super Forecast Methodology Related Links Near Real-time Wind Animation Meteorological Data Customer Supplied Generation Imbalance Dynamic Transfer Limits...

  6. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability ... that measure feedstock production, water quality, water quantity, and biodiversity. ...

  7. Intermediate future forecasting system

    SciTech Connect (OSTI)

    Gass, S.I.; Murphy, F.H.; Shaw, S.H.

    1983-12-01

    The purposes of the Symposium on the Department of Energy's Intermediate Future Forecasting System (IFFS) were: (1) to present to the energy community details of DOE's new energy market model IFFS; and (2) to have an open forum in which IFFS and its major elements could be reviewed and critiqued by external experts. DOE speakers discussed the total system, its software design, and the modeling aspects of oil and gas supply, refineries, electric utilities, coal, and the energy economy. Invited experts critiqued each of these topics and offered suggestions for modifications and improvement. This volume documents the proceedings (papers and discussion) of the Symposium. Separate abstracts have been prepared for each presentation for inclusion in the Energy Data Base.

  8. Formulation of cracking catalyst based on zeolite and natural clays

    SciTech Connect (OSTI)

    Aliev, R.R.; Lupina, M.I.

    1995-11-01

    Domestically manufactured cracking catalysts are based on a synthetic amorphous aluminosilicate matrix and Y zeolite. A multistage {open_quotes}gel{close_quotes} technology is used in manufacturing the catalysts. The process includes mixing solutions of sodium silicate and acidic aluminum sulfate, forming, syneresis, and activation of the beaded gel. In the manufacture of bead catalysts, the next steps in the process are washing, drying, and calcining; in the manufacture of microbead catalysts, the next steps are dispersion and formation of a hydrogel slurry, spray-drying, and calcining. The Y zeolite is either introduced into the alumina-silica sol in the stage of forming the beads, or introduced in the dispersion stage. With the aim of developing an active and selective cracking catalyst based on Y zeolite and natural clays, with improved physicomechanical properties, the authors carried out a series of studies, obtaining results that are set forth in the present article.

  9. Louisiana Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Louisiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 262,136 1991 264,324 264,324 264,304 264,497 265,121 265,448 265,816 266,390 262,350 266,030 267,245 267,245 1992 267,245 267,245 265,296 262,230 262,454 263,788 266,852 260,660 257,627 258,575 259,879 262,144 1993 261,841 255,035 251,684

  10. Mountain Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) Mountain Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 421,075 420,615 419,767 420,250 420,606 420,353 422,402 422,811 423,525 423,507 423,501 421,314 2015 421,311 421,304 423,663 423,684 423,689 423,689 423,690 423,699 423,698 423,690 425,847 426,205 2016 426,151 426,075 426,050 426,104 426,133 426,165 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  11. Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 0 0 0 0 0 0 0 0 0 2000's 0 1 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  12. Florida Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Florida Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 5 4 3 2 2 1 1 1 1990's 1 1 1 1 1 1 1 1 1 1 2000's 1 1 1 1 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  13. Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 3 1980's 3 2 3 2 2 2 2 1 2 1 1990's 1 2 2 2 3 3 3 3 3 3 2000's 2 3 3 3 3 3 3 3 3 4 2010's 5 4 5 5 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  14. Montana Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Montana Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 1 1 1 1 0 0 0 0 0 2000's 0 0 1 1 1 1 1 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  15. California Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) California Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 1991 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 248,389 248,389 248,389 248,389 1992 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 250,206 1993 250,206 250,206

  16. Tennessee Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Tennessee Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 340 340 340 340 340 340 340 340 340 340 340 340 1999 340 340 340 340 340 340 340 340 340 340 340 340 2000 340 340 340 340 340 340 340 340 340 340 340 340 2001 340 340 340 340 340 340 340 340 340 340 340 340 2002 340 340 340 340 340 340 340 340 340 340 340 340 2003 340 340 340 340 340 340 340

  17. Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 352,686 352,686 352,686 351,920 352,686 352,686 353,407 353,407 353,407 353,407 359,236 358,860 1991 349,459 348,204 334,029 335,229 353,405 349,188 350,902 352,314 353,617 354,010 353,179 355,754 1992 358,198 353,313 347,361 341,498 344,318 347,751 357,498 358,432 359,300 359,504 359,321 362,275 1993 362,222 358,438

  18. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

    SciTech Connect (OSTI)

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

  19. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

  20. NREL: Transmission Grid Integration - Forecasting

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

    Forecasting NREL researchers use solar and wind resource assessment and forecasting techniques to develop models that better characterize the potential benefits and impacts of ...

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

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

    Bonneville Power Administration Power Business Line Generation (PBL) Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

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

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

    2003 Bonneville Power Administration Power Business Line Generation Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  3. Minnesota Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Minnesota Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1991 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1992 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1993 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 4,655 1994 4,655 4,655

  4. Mississippi Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Mississippi Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 46,050 1991 47,530 47,483 47,483 47,483 47,483 47,868 48,150 48,150 48,150 48,150 48,150 48,150 1992 48,150 48,150 48,149 48,149 48,149 48,149 48,149 48,149 48,149 48,149 47,851 48,049 1993 48,039 48,049 48,049 48,049 47,792 48,049 48,049 48,049

  5. Washington Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Washington Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,300 21,300 21,300 21,300 0 21,300 21,300 21,300 21,300 21,300 21,300 1991 21,300 21,300 21,300 21,300 21,300 21,300 21,300 21,300 21,300 18,800 18,800 18,800 1992 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 1993 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800 18,800

  6. 2016 Solar Forecasting Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

    On August 3, 2016, the SunShot Initiative's systems integration subprogram hosted the Solar Forecasting Workshop to convene experts in the areas of bulk power system operations, distribution system operations, weather and solar irradiance forecasting, and photovoltaic system operation and modeling. The goal was to identify the technical challenges and opportunities in solar forecasting as a capability that can significantly reduce the integration cost of high levels of solar energy into the electricity grid. This will help SunShot to assess current technology and practices in this field and identify the gaps and needs for further research.

  7. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

  8. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

  9. Today's Forecast: Improved Wind Predictions

    Broader source: Energy.gov [DOE]

    Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable.

  10. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more...

  11. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers.

    SciTech Connect (OSTI)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments. Biotechnol. Bioeng.

  12. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

    SciTech Connect (OSTI)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.

  13. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are onemore » of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.« less

  14. Acquisition Forecast | Department of Energy

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

    Acquisition Forecast Acquisition Forecast Acquisition Forecast It is the policy of the U.S. Department of Energy (DOE) to provide timely information to the public regarding DOE's forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department's major site and facilities management contractors. This forecast has been expanded to also provide timely status information for ongoing prime contracting actions that are valued in excess of the

  15. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

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

  16. Control method for mixed refrigerant based natural gas liquefier

    DOE Patents [OSTI]

    Kountz, Kenneth J.; Bishop, Patrick M.

    2003-01-01

    In a natural gas liquefaction system having a refrigerant storage circuit, a refrigerant circulation circuit in fluid communication with the refrigerant storage circuit, and a natural gas liquefaction circuit in thermal communication with the refrigerant circulation circuit, a method for liquefaction of natural gas in which pressure in the refrigerant circulation circuit is adjusted to below about 175 psig by exchange of refrigerant with the refrigerant storage circuit. A variable speed motor is started whereby operation of a compressor is initiated. The compressor is operated at full discharge capacity. Operation of an expansion valve is initiated whereby suction pressure at the suction pressure port of the compressor is maintained below about 30 psig and discharge pressure at the discharge pressure port of the compressor is maintained below about 350 psig. Refrigerant vapor is introduced from the refrigerant holding tank into the refrigerant circulation circuit until the suction pressure is reduced to below about 15 psig, after which flow of the refrigerant vapor from the refrigerant holding tank is terminated. Natural gas is then introduced into a natural gas liquefier, resulting in liquefaction of the natural gas.

  17. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

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

    2013-10-01

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

  18. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    SciTech Connect (OSTI)

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

    2012-09-01

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

  19. New Mexico--East Natural Gas Plant Liquids, Reserves Based Production...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) New Mexico--East Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  20. New Mexico--West Natural Gas Plant Liquids, Reserves Based Production...

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

    Reserves Based Production (Million Barrels) New Mexico--West Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 ...

  1. Alabama (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Alabama (with State Offshore) Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 182 1980's 193 167 158 166 152 143 139 132 130 130 1990's 122 110 118 103 91 72 67 59 50 50 2000's 46 32 29 27 21 30 15 21 14 16 2010's 18 19 18 14 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  2. Alaska (with Total Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Alaska (with Total Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 13 1980's 11 10 9 8 0 382 381 418 401 380 1990's 340 360 347 321 301 306 337 631 320 299 2000's 277 405 405 387 369 352 338 325 312 299 2010's 288 288 288 288 241 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  3. California (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) California (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 107 1980's 109 73 146 139 128 124 118 109 1990's 101 87 94 98 86 88 89 92 71 97 2000's 100 75 95 101 121 135 130 126 113 129 2010's 114 94 99 102 112 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  4. Louisiana (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Louisiana (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 400 287 301 294 294 1990's 324 321 317 260 281 430 381 261 234 281 2000's 241 204 186 183 167 191 176 191 201 231 2010's 216 192 189 212 243 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. Louisiana--North Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Louisiana--North Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 54 1980's 59 63 59 50 38 47 39 33 39 40 1990's 38 38 41 38 48 55 61 50 34 36 2000's 35 35 30 48 53 57 60 69 68 98 2010's 79 54 35 52 83 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  6. Louisiana--South Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Louisiana--South Onshore Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 413 1980's 273 291 258 289 225 222 220 235 228 215 1990's 249 242 229 201 214 359 284 199 187 222 2000's 178 128 119 100 87 103 94 97 78 90 2010's 113 94 134 144 145 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  7. Lower 48 Federal Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Lower 48 Federal Offshore Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 363 382 350 331 337 1990's 295 329 295 309 309 239 245 389 370 427 2000's 515 486 511 364 423 416 399 369 321 302 2010's 341 355 405 335 399 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  8. Mississippi (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Future Production (Million Barrels) Expected Future Production (Million Barrels) Mississippi (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 5 1980's 5 5 6 6 5 4 3 3 3 3 1990's 3 3 3 3 3 3 2 2 3 3 2000's 2 2 2 2 1 2 2 3 3 4 2010's 4 6 4 3 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  9. Utah and Wyoming Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Utah and Wyoming Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 280 1980's 294 363 381 483 577 681 700 701 932 704 1990's 641 580 497 458 440 503 639 680 600 531 2000's 858 782 806 756 765 710 686 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  10. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect (OSTI)

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and

  11. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    SciTech Connect (OSTI)

    Wilczak, James M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  12. California--State Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Feet) Marketed Production (Million Cubic Feet) California--State Offshore Natural Gas Marketed Production (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 7,211 6,467 7,204 5,664 5,975 6,947 6,763 6,500 2000's 6,885 6,823 6,909 6,087 6,803 6,617 6,652 7,200 6,975 5,832 2010's 5,120 4,760 5,051 5,470 5,961 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  13. Texas (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas (with State Offshore) Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2,125 1980's 2,081 2,285 2,393 2,650 2,660 2,610 2,671 2,509 2,339 2,270 1990's 2,305 2,237 2,162 2,211 2,151 2,269 2,337 2,376 2,262 2,257 2000's 2,479 2,318 2,368 2,192 2,466 2,723 2,913 3,158 3,148 3,432 2010's 3,983 4,541 4,727 5,653

  14. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

  15. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  16. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Gomez-Lazaro, E.; Lovholm, A. L.; Berge, E.; Miettinen, J.; Holttinen, H.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Dobschinski, J.

    2013-10-01

    One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

  17. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect (OSTI)

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

    2014-12-30

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

  18. Coal supply/demand, 1980 to 2000. Task 3. Resource applications industrialization system data base. Final review draft. [USA; forecasting 1980 to 2000; sector and regional analysis

    SciTech Connect (OSTI)

    Fournier, W.M.; Hasson, V.

    1980-10-10

    This report is a compilation of data and forecasts resulting from an analysis of the coal market and the factors influencing supply and demand. The analyses performed for the forecasts were made on an end-use-sector basis. The sectors analyzed are electric utility, industry demand for steam coal, industry demand for metallurgical coal, residential/commercial, coal demand for synfuel production, and exports. The purpose is to provide coal production and consumption forecasts that can be used to perform detailed, railroad company-specific coal transportation analyses. To make the data applicable for the subsequent transportation analyses, the forecasts have been made for each end-use sector on a regional basis. The supply regions are: Appalachia, East Interior, West Interior and Gulf, Northern Great Plains, and Mountain. The demand regions are the same as the nine Census Bureau regions. Coal production and consumption in the United States are projected to increase dramatically in the next 20 years due to increasing requirements for energy and the unavailability of other sources of energy to supply a substantial portion of this increase. Coal comprises 85 percent of the US recoverable fossil energy reserves and could be mined to supply the increasing energy demands of the US. The NTPSC study found that the additional traffic demands by 1985 may be met by the railways by the way of improved signalization, shorter block sections, centralized traffic control, and other modernization methods without providing for heavy line capacity works. But by 2000 the incremental traffic on some of the major corridors was projected to increase very significantly and is likely to call for special line capacity works involving heavy investment.

  19. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

  20. Natural Gas Weekly Update

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

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  1. Using Wikipedia to forecast diseases

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

    Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. November 13, 2014 Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505)

  2. Baseline and Target Values for PV Forecasts: Toward Improved...

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

    Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting ... Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting Jie ...

  3. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

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

  4. The forecast calls for flu

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

    Science on the Hill: The forecast calls for flu Using mathematics, computer programs, ... We're getting close. Using mathematics, computer programs, statistics and information ...

  5. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    Zip: 94965 Region: Bay Area Sector: Services Product: Intelligent Monitoring and Forecasting Services Year Founded: 2010 Website: www.forecastenergy.net Coordinates:...

  6. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT...

    Open Energy Info (EERE)

    Tool (EFFECT) EFFECT is an open, Excel-based modeling tool used to forecast greenhouse gas emissions from a range of development scenarios at the regional and national levels....

  7. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

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

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

  8. Upcoming Funding Opportunity for Wind Forecasting Improvement...

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

    Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am ...

  9. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast AgencyCompany Organization: United States Department of Energy Sector:...

  10. Project Profile: Forecasting and Influencing Technological Progress...

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

    Soft Costs Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar ...

  11. National Oceanic and Atmospheric Administration Provides Forecasting...

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

    ... will share their expertise with CLASIC and CHAPS forecasters and project leaders as they consult on the forecast that will determine the day's operations plan. -- Storm Prediction ...

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

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

    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

  13. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

    Davis, J.I.; Spaeth, M.L.

    1986-01-01

    The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

  14. Science on Tap - Forecasting illness

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

    Science on Tap - Forecasting illness Science on Tap - Forecasting illness WHEN: Mar 17, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Mark your calendars for this event held every third Thursday from 5:30 to 7 p.m. A short presentation is followed by a lively discussion on a different subject each month. Forecasting the flu (and other

  15. Development and testing of improved statistical wind power forecasting methods.

    SciTech Connect (OSTI)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.

    2011-12-06

    (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  16. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

  17. Global disease monitoring and forecasting with Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

  18. Acquisition Forecast Download | Department of Energy

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

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. Acquisition-Forecast-2016-07-20.xlsx (72.85 KB) More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Assessment Report: OAS-V-15-01

  19. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  20. Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western United States based on Weather Research and Forecasting chemistry and regional climate simulations

    SciTech Connect (OSTI)

    Qian, Yun; Gustafson, William I.; Leung, Lai-Yung R.; Ghan, Steven J.

    2009-02-14

    Radiative forcing induced by soot on snow is a major anthropogenic forcing affecting the global climate. However, it is uncertain how the soot-induced snow albedo perturbation affects regional snowpack and the hydrological cycle. In this study we simulated the deposition of soot aerosol on snow and investigated the resulting impact on snowpack and the surface water budget in the western United States. A yearlong simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine an annual budget of soot deposition, followed by two regional climate simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the net solar radiation flux at the surface during late winter to early spring, increase the surface air temperature, reduce snow water equivalent amount, and lead to reduced snow accumulation and less spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow covered regions. Our simulations indicate that the change of maximum snow albedo induced by soot on snow contributes to 60% of the net albedo reduction over the central Rockies. Snowpack reduction accounts for the additional 40%.

  1. Holographic imaging based on time-domain data of natural-fiber-containing materials

    DOE Patents [OSTI]

    Bunch, Kyle J.; McMakin, Douglas L.

    2012-09-04

    Methods and apparatuses for imaging material properties in natural-fiber-containing materials can utilize time-domain data. In particular, images can be constructed that provide quantified measures of localized moisture content. For example, one or more antennas and at least one transceiver can be configured to collect time-domain data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz, according to a time-domain impulse function. A computing device is configured to transform the time-domain data to frequency-domain data, to apply a synthetic imaging algorithm for constructing a three-dimensional image of the natural-fiber-containing materials, and to provide a quantified measure of localized moisture content based on a pre-determined correlation of moisture content to frequency-domain data.

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

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

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

  3. Picture of the Week: Forecasting Flu

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

    3 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? March 6, 2016 flu epidemics modellled using social media Watch the video on YouTube. Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del

  4. Natural language processing-based COTS software and related technologies survey.

    SciTech Connect (OSTI)

    Stickland, Michael G.; Conrad, Gregory N.; Eaton, Shelley M.

    2003-09-01

    Natural language processing-based knowledge management software, traditionally developed for security organizations, is now becoming commercially available. An informal survey was conducted to discover and examine current NLP and related technologies and potential applications for information retrieval, information extraction, summarization, categorization, terminology management, link analysis, and visualization for possible implementation at Sandia National Laboratories. This report documents our current understanding of the technologies, lists software vendors and their products, and identifies potential applications of these technologies.

  5. Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 0 1990's 0 0 1 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  6. Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 4 4 9 9 9 10 10 10 9 8 1990's 8 7 8 8 7 8 8 7 6 7 2000's 7 7 9 9 9 10 10 10 10 10 2010's 9 9 9 10 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  7. ,"U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)"

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

    - Base Gas (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)",1,"Monthly","6/2016" ,"Release Date:","08/31/2016" ,"Next Release Date:","09/30/2016" ,"Excel File

  8. U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production

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

    (Million Barrels) Based Production (Million Barrels) U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 147 1980's 159 161 157 157 179 168 169 162 162 165 1990's 158 153 147 153 157 145 162 174 178 199 2000's 208 215 207 191 182 174 182 181 173 178 2010's 224 231 274 311 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  9. U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels)

    Gasoline and Diesel Fuel Update (EIA)

    Based Production (Million Barrels) U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 580 1980's 572 580 564 568 597 585 569 585 592 566 1990's 574 601 626 635 634 646 688 690 655 697 2000's 710 675 677 611 645 614 629 650 667 714 2010's 745 784 865 931 1,124 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  10. The Value of Wind Power Forecasting

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

    ... day-ahead wind generation forecasts yields an average of 195M savings in annual operating costs. Figure 6 shows how operating cost savings vary with improvements in forecasting. ...

  11. EIA lowers forecast for summer gasoline prices

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

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be ... according to the new monthly forecast from the U.S. Energy Information Administration. ...

  12. UPF Forecast | Y-12 National Security Complex

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

    Subcontracting / Subcontracting Forecasts / UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be revised or cancelled, depending on program budget funding or departmental needs. If you have questions or would like to express an interest in any of the opportunities listed below, contact UPF Procurement. Descriptiona Methodb NAICS Est. Dollar Range RFP release/ Award datec Buyer/ Phone Commodities

  13. Wind Forecasting Improvement Project | Department of Energy

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

    Forecasting Improvement Project Wind Forecasting Improvement Project October 3, 2011 - 12:12pm Addthis This is an excerpt from the Third Quarter 2011 edition of the Wind Program R&D Newsletter. In July, the Department of Energy launched a $6 million project with the National Oceanic and Atmospheric Administration (NOAA) and private partners to improve wind forecasting. Wind power forecasting allows system operators to anticipate the electrical output of wind plants and adjust the electrical

  14. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  15. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  16. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  17. Natural gas resource data base for the United States (1987). Final report, June-December 1987

    SciTech Connect (OSTI)

    Kent, H.C.; Finney, J.J.

    1988-02-01

    This data base gives a detailed summary of the estimated potential resources of natural gas in the United States, including postulated depth distributions, field sizes, well recoveries and success rates. The study (an expansion on the 1986 resource estimates of the Potential Gas Committee) analyzed the distribution and characteristics of the resource potential estimated to occur in the onshore geologic provinces of the lower 48 states, as well as the resources beneath the continental shelf and slope offshore from Louisiana and Texas. The areas that hold the greatest potential for future natural gas exploration and development include the Atlantic, Gulf Coast, Mid-Continent and Rocky Mountain areas, which contain approximately 92% of the estimated undiscovered resources. The results of the study are intended to be used to assist in making cost determinations which can be utilized in the development of supply models and in planning.

  18. Forecast of contracting and subcontracting opportunities. Fiscal year 1996

    SciTech Connect (OSTI)

    1996-02-01

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

  19. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

    This report presents an overview of forecasts of domestic energy consumption, production, and prices for the year 1990. These results are selected from more detailed projections prepared and published in Volume 3 of the Energy Information Administration 1980 Annual Report to Congress. This report focuses specifically upon the 1980's and concentrates upon similarities and differences in the domestic energy system, as forecast, compared to the national experience in the years immediately following the 1973--1974 oil embargo. Interest in the 1980's stems not only from its immediacy in time, but also from its importance as a time in which certain adjustments to higher energy prices are expected to take place. The forecasts presented do not attempt to account for all of this wide range of potentially important forces that could conceivably alter the energy situation. Instead, the projections are based on a particular set of assumptions that seems reasonable in light of what is currently known. 9 figs., 25 tabs.

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

    SciTech Connect (OSTI)

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

    2010-09-01

    features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

  1. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  2. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

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

    SciTech Connect (OSTI)

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

    2013-05-01

    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.

  4. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  5. Michigan Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Michigan Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 395,529 395,529 395,529 395,529 395,529 395,180 396,744 396,491 396,293 396,099 395,934 395,790 1991 394,527 393,885 392,506 394,146 413,930 413,764 413,617 413,530 413,468 413,390 413,242 413,275 1992 413,430 413,426 413,356 413,302 413,258 413,224 413,182 413,226 413,225 413,194 413,136 413,069 1993 413,736 413,707 410,316 411,038

  6. Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 109,573 109,573 109,573 109,573 112,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1991 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1992 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 1993 169,892 169,892 169,892 169,892

  7. Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,505,645 1,504,979 1,497,798 1,502,556 1,498,128 1,498,610 1,498,610 1,498,610 1,498,887 1,496,791 1,496,848 1,497,021 2015 1,497,256 1,496,957 1,496,400 1,495,858 1,495,743 1,496,917 1,496,915 1,489,324 1,490,195 1,488,404 1,488,432 1,488,593 2016 1,488,560 1,488,552 1,487,836 1,487,397 1,488,033 1,489,057 - = No

  8. West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 310,640 310,640 310,640 310,640 310,640 310,640 311,765 311,765 311,765 311,765 312,670 309,331 1991 331,618 332,229 331,898 332,278 332,288 332,288 331,275 332,283 332,269 332,264 332,259 332,070 1992 336,854 336,689 335,303 335,602 335,965 336,044 336,309 336,528 336,527 336,526 336,525 305,441 1993 305,478 304,578

  9. U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million Cubic

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

    Feet) - Base Gas (Million Cubic Feet) U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 4,310,511 4,299,144 4,304,605 4,305,250 4,311,328 4,310,801 4,313,863 4,313,462 4,311,826 4,311,686 4,309,746 4,316,503 1995 4,311,142 4,313,967 4,307,833 4,306,142 4,338,851 4,351,366 4,285,411 4,285,137 4,286,773 4,282,697 4,286,509 4,289,504 1996 4,291,262 4,285,701 4,227,609 4,249,339 4,268,329 4,277,305 4,275,962

  10. Pacific Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Pacific Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 258,736 258,541 258,456 258,619 258,736 258,736 258,736 258,736 258,736 259,036 259,036 259,036 2015 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,036 259,331 259,331 259,331 2016 259,331 259,331 259,331 259,331 259,331 259,331 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  11. AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 2,700,245 2,697,308 2,696,823 2,698,489 2,699,802 2,699,840 2,700,331 2,701,227 2,701,285 2,702,703 2,702,571 2,703,149 1995 2,699,674 2,699,575 2,696,880 2,695,400 2,726,268 2,726,255 2,668,312 2,671,818 2,672,399 2,672,258 2,671,362 2,672,808 1996 2,670,906 2,670,070 2,646,056 2,654,836

  12. AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 1,039,864 1,032,160 1,033,297 1,032,517 1,037,294 1,037,338 1,038,940 1,036,193 1,037,422 1,035,931 1,035,050 1,043,103 1995 1,051,669 1,054,584 1,051,120 1,051,697 1,052,949 1,062,613 1,058,260 1,054,218 1,054,870 1,051,687 1,056,704 1,060,588 1996 1,067,220 1,062,343 1,027,692 1,040,511 1,055,164

  13. AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 607,596 607,629 612,749 613,849 614,562 614,534 615,937 617,412 614,732 615,667 615,712 613,840 1995 613,874 613,874 613,898 613,357 613,699 616,811 613,151 613,413 613,504 613,752 613,514 615,837 1996 616,124 616,330 616,610 617,033 616,902 617,159 616,822 615,039 616,632 616,849 617,148

  14. Alaska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Alaska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 7,622 14,197 14,197 14,196 14,196 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2014 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2015 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 14,197 2016 14,197 14,197 14,197 14,197 14,197 14,197 - = No Data Reported; -- =

  15. Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 571,959 571,959 571,959 571,959 572,425 572,423 572,421 572,421 572,419 572,419 573,776 577,424 1991 577,418 577,418 577,418 568,227 568,178 568,160 568,158 568,157 568,157 568,158 568,158 568,158 1992 576,257 576,227 576,227 576,227 576,227 576,227 576,227 576,234 576,234 577,202 577,202 579,715 1993 620,575 620,856 620,777 621,051

  16. Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 153,933 153,933 153,933 153,933 153,920 153,919 153,917 153,917 153,916 153,916 153,916 153,916 1991 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 1992 154,574 154,574 154,574 154,161 154,574 154,574 154,574 154,574 154,574 154,574 154,574 154,574 1993 200,700 199,929 199,482 200,679

  17. Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 191,402 190,669 1991 188,597 191,203 191,198 191,198 191,126 192,733 192,736 192,798 192,798 192,805 192,563 192,563 1992 190,943 190,963 190,914 190,591 190,765 190,714 190,611 190,578 190,606 190,643 189,320 186,399 1993 184,254 180,510 181,152 186,315

  18. Kentucky Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Kentucky Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 105,889 1991 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 103,881 1992 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 105,481 1993 105,430 105,394 105,392 105,446

  19. Texas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Texas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 134,707 134,707 134,707 160,665 160,663 160,663 160,663 160,697 160,697 160,697 160,697 160,697 1991 165,309 165,039 165,039 164,407 164,407 164,407 164,407 168,776 169,114 169,114 170,183 170,183 1992 170,483 170,633 170,631 170,630 170,630 170,631 170,630 170,630 170,630 171,139 171,359 171,360 1993 248,991 239,554 235,259 239,554

  20. East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,113,096 1,112,811 1,110,723 1,111,592 1,111,730 1,113,003 1,113,262 1,113,458 1,113,383 1,113,607 1,113,589 1,113,356 2015 1,111,081 1,110,574 1,112,593 1,112,719 1,113,055 1,114,216 1,119,070 1,118,884 1,119,057 1,119,175 1,119,046 1,119,011 2016 1,118,751 1,118,483 1,111,752 1,111,114 1,111,399 1,112,116 - = No Data

  1. South Central Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) South Central Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,050,691 1,049,083 1,049,047 1,049,443 1,049,496 1,053,249 1,054,073 1,058,479 1,060,363 1,060,181 1,060,298 1,059,866 2015 1,057,760 1,057,807 1,054,816 1,054,786 1,057,044 1,058,973 1,059,103 1,058,987 1,058,721 1,060,652 1,061,199 1,055,894 2016 1,054,232 1,054,693 1,053,049 1,057,433 1,058,680

  2. Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 338,916 336,243 331,979 1991 357,743 357,743 357,743 357,674 351,476 357,598 357,566 357,743 357,743 357,743 357,743 357,743 1992 357,689 357,689 356,333 355,927 356,779 356,747 356,880 357,810 357,808 357,856 357,856 358,966 1993 358,966 357,823 354,044 354,688

  3. Oklahoma Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Oklahoma Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 167,385 163,458 167,385 163,458 167,385 167,385 167,385 167,385 167,385 167,385 173,097 172,762 1991 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 172,757 1992 172,757 172,757 172,368 172,573 172,757 172,757 172,757 172,757 172,757 172,757 176,765 176,765 1993 228,593 227,252 227,560 226,942

  4. Supply Forecast and Analysis (SFA)

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

    Matthew Langholtz Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies Office Goal Statement * Provide timely and credible estimates of feedstock supplies and prices to support - the development of a bioeconomy; feedstock demand analysis of EISA, RFS2, and RPS mandates - the data and analysis of other projects in Analysis and Sustainability, Feedstock Supply and Logistics,

  5. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect (OSTI)

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

    2014-07-09

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

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

    SciTech Connect (OSTI)

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

    2010-01-01

    unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  7. World Natural Gas Model

    Energy Science and Technology Software Center (OSTI)

    1994-12-01

    RAMSGAS, the Research and Development Analysis Modeling System World Natural Gas Model, was developed to support planning of unconventional gaseoues fuels research and development. The model is a scenario analysis tool that can simulate the penetration of unconventional gas into world markets for oil and gas. Given a set of parameter values, the model estimates the natural gas supply and demand for the world for the period from 1980 to 2030. RAMSGAS is based onmore » a supply/demand framwork and also accounts for the non-renewable nature of gas resources. The model has three fundamental components: a demand module, a wellhead production cost module, and a supply/demand interface module. The demand for gas is a product of total demand for oil and gas in each of 9 demand regions and the gas share. Demand for oil and gas is forecast from the base year of 1980 through 2030 for each demand region, based on energy growth rates and price-induced conservation. For each of 11 conventional and 19 unconventional gas supply regions, wellhead production costs are calculated. To these are added transportation and distribution costs estimates associated with moving gas from the supply region to each of the demand regions and any economic rents. Based on a weighted average of these costs and the world price of oil, fuel shares for gas and oil are computed for each demand region. The gas demand is the gas fuel share multiplied by the total demand for oil plus gas. This demand is then met from the available supply regions in inverse proportion to the cost of gas from each region. The user has almost complete control over the cost estimates for each unconventional gas source in each year and thus can compare contributions from unconventional resources under different cost/price/demand scenarios.« less

  8. Stress state and nature of failure of detonation coatings based on alumina

    SciTech Connect (OSTI)

    Anisimov, M.I.; Galeev, I.M.; Gol`dfain, V.N.

    1995-03-01

    Detonation coatings based on alumina are used on an increasing scale in industry for increasing the corrosion and wear resistance of materials. The physicomechanical and service characteristics of coatings are determined by the stress state . In this work, investigations were carried out into the distribution of residual phase stresses in the layer and the nature of failure of coatings in combined deformation with the substrate at T = 600{degrees}C. Coatings of electrocorundum powder of 24A grade with a grain size of M40 were deposited on a substrate of KhN78 alloy. In certain cases, an intermediate layer of PN85Yu15 powder was deposited on the substrate prior to spraying. Spraying was carried out in ADK Prometei equipment using an oxygen-acetylene mixture. The thickness of the coatings was 0.4-0.5 mm.

  9. ARM - CARES - Tracer Forecast for CARES

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

    CampaignsCarbonaceous Aerosols and Radiative Effects Study (CARES)Tracer Forecast for CARES Related Links CARES Home AAF Home ARM Data Discovery Browse Data Post-Campaign Data Sets Field Updates CARES Wiki Campaign Images Experiment Planning Proposal Abstract and Related Campaigns Science Plan Operations Plan Measurements Forecasts News News & Press Backgrounder (PDF, 1.45MB) G-1 Aircraft Fact Sheet (PDF, 1.3MB) Contacts Rahul Zaveri, Lead Scientist Tracer Forecasts for CARES This webpage

  10. LED Lighting Forecast | Department of Energy

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

    Publications » Market Studies » LED Lighting Forecast LED Lighting Forecast The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030. With declining costs and improving performance, LED products have been seeing increased adoption for general illumination applications. This is a positive development in terms of energy consumption, as LEDs use significantly

  11. Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1

    SciTech Connect (OSTI)

    Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

    1994-05-01

    This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

  12. NREL: Resource Assessment and Forecasting Home Page

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

    are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research:...

  13. Funding Opportunity Announcement for Wind Forecasting Improvement...

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

    There is no cost to participate and all applicants are encouraged to attend. To join the ... Related Articles Upcoming Funding Opportunity for Wind Forecasting Improvement Project in ...

  14. Module 6 - Metrics, Performance Measurements and Forecasting...

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

    This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as ...

  15. Forecast and Funding Arrangements - Hanford Site

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

    Annual Waste Forecast and Funding Arrangements About Us Hanford Site Solid Waste Acceptance Program What's New Acceptance Criteria Acceptance Process Becoming a new Hanford...

  16. NREL: Resource Assessment and Forecasting - Webmaster

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

    email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home Capabilities Facilities Working with Us Research Staff Data & Resources Did...

  17. Development and Demonstration of Advanced Forecasting, Power...

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

    and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices 63wateruseoptimizationprojectanlgasper.ppt (7.72 MB) More ...

  18. Incorporating Wind Generation Forecast Uncertainty into Power System Operation, Dispatch, and Unit Commitment Procedures

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Subbarao, Krishnappa

    2010-10-19

    In this paper, an approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the "flying-brick" technique is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through integration with an EMS system illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems from other vendors.

  19. Maryland Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Maryland Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1991 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1992 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 1993 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677 46,677

  20. Missouri Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Missouri Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1991 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1992 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 1993 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600 21,600

  1. Nebraska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Nebraska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1991 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1992 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 1993 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312 27,312

  2. Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,944 46,944 46,944 46,944 48,144 46,944 49,350 50,457 51,244 51,397 42,464 42,464 1991 42,454 42,454 44,628 44,342 45,120 49,179 51,258 49,908 48,558 47,678 47,118 47,118 1992 47,118 47,739 48,770 49,900 50,972 52,189 53,369 54,688 55,934 57,208 49,578 49,736 1993 49,736 49,742 49,749 50,238 51,803 51,028 52,377 53,704 54,973 54,847

  3. Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0 0 0 0 0 0 1998 2,345 2,371 2,369 2,366 2,361 2,356 2,353 2,347 2,289 2,382 2,436 2,433 1999 2,485 2,478 2,470 2,467 2,464 2,459 2,437 2,450 2,443 2,434 2,424 2,410 2000 2,400 2,441 2,475 2,394 2,094 2,094 2,094 2,152 2,134 2,192 2,192 2,192 2001 2,192 2,312 2,312 2,312 2,312 2,312 2,312 2,312 2,312 2,362 2,362 2,372

  4. Wyoming Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Wyoming Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 31,205 31,205 31,205 31,205 31,353 31,205 31,501 31,638 31,735 31,754 30,652 30,652 1991 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 34,651 1992 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,130 59,127 59,382 1993 59,382 59,382 59,382 59,382 59,382 59,382 59,382 59,427 59,427 59,427

  5. Alabama Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Alabama Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 880 880 880 880 880 880 880 880 880 880 880 880 1996 880 650 650 650 880 1,071 1,083 1,088 1,190 1,190 1,190 1,190 1997 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1998 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1999 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190 1,190

  6. Arkansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Arkansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 1991 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 19,202 1992 19,202 19,202 19,112 19,021 19,007 19,007 19,007 19,007 19,007 18,887 18,748 18,615 1993 18,607 18,523 18,484 18,472 18,156 17,897 17,888 17,888 17,888

  7. Colorado Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Colorado Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 39,062 45,393 1991 45,258 45,263 45,263 45,252 45,252 45,252 45,252 45,252 45,252 45,252 45,252 45,252 1992 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 45,237 1993 45,210 45,210 45,210 45,210 45,210 45,210 45,210 45,210 45,210

  8. Indiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Indiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 74,572 74,572 74,558 74,558 74,558 74,565 74,572 74,572 74,572 74,572 74,572 74,729 1991 74,588 70,962 70,956 70,856 70,892 70,956 70,957 70,962 70,962 81,536 71,050 71,050 1992 71,050 71,050 71,005 70,920 71,043 71,050 71,050 71,050 71,050 71,139 71,139 71,139 1993 71,407 71,390 71,377 71,255 71,338 71,407 71,407 71,407 71,407 71,453

  9. Study on systems based on coal and natural gas for producing dimethyl ether

    SciTech Connect (OSTI)

    Zhou, L.; Hu, S.Y.; Chen, D.J.; Li, Y.R.; Zhu, B.; Jin, Y.

    2009-04-15

    China is a coal-dependent country and will remain so for a long time. Dimethyl ether (DME), a potential substitute for liquid fuel, is a kind of clean diesel motor fuel. The production of DME from coal is meaningful and is studied in this article. Considering the C/H ratios of coal and natural gas (NG), the cofeed (coal and NG) system (CFS), which does not contain the water gas shift process, is studied. It can reduce CO{sub 2} emission and increase the conversion rate of carbon, producing more DME. The CFS is simulated and compared with the coal-based and NG-based systems with different recycling ratios. The part of the exhaust gas that is not recycled is burned, producing electricity. On the basis of the simulation results, the thermal efficiency, economic index, and CO{sub 2} emission ratio are calculated separately. The CFS with a 100% recycling ratio has the best comprehensive evaluation index, while the energy, economy, and environment were considered at the same time.

  10. Oregon Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Oregon Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1991 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1992 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1993 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 3,291 1994 3,291 3,291 3,291 4,896 4,896

  11. Natural Gas Transmission and Distribution Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the archived version of the Natural Gas Transmission and Distribution Model that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 2014.

  12. Characterization and testing of amidoxime-based adsorbent materials to extract uranium from natural seawater

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kuo, Li-Jung; Janke, Christopher James; Wood, Jordana; Strivens, Jonathan E.; Gill, Gary

    2015-11-19

    Extraction of uranium (U) from seawater for use as a nuclear fuel is a significant challenge due to the low concentration of U in seawater (~3.3 ppb) and difficulties to selectively extract U from the background of major and trace elements in seawater. The Pacific Northwest National Laboratory (PNNL) s Marine Sciences Laboratory (MSL) has been serving as a marine test site for determining performance characteristics (adsorption capacity, adsorption kinetics, and selectivity) of novel amidoxime-based polymeric adsorbents developed at Oak Ridge National Laboratory (ORNL) under natural seawater exposure conditions. This report describes the performance of three formulations (38H, AF1, AI8)more » of amidoxime-based polymeric adsorbent produced at ORNL in MSL s ambient seawater testing facility. The adsorbents were produced in two forms, fibrous material (40-100 mg samples) and braided material (5-10 g samples), exposed to natural seawater using flow-through columns and recirculating flumes. All three formulations demonstrated high 56 day uranium adsorption capacity (>3 gU/kg adsorbent). The AF1 formulation had the best uranium adsorption performance, with 56-day capacity of 3.9 g U/kg adsorbent, saturation capacity of 5.4 g U/kg adsorbent, and ~25 days half-saturation time. The two exposure methods, flow-through columns and flumes were demonstrated to produce similar performance results, providing confidence that the test methods were reliable, that scaling up from 10 s of mg quantities of exposure in flow-through columns to gram quantities in flumes produced similar results, and that the manufacturing process produces a homogenous adsorbent. Adsorption kinetics appear to be element specific, with half-saturation times ranging from minutes for the major cations in seawater to 8-10weeks for V and Fe. Reducing the exposure time provides a potential pathway to improve the adsorption capacity of U by reducing the V/U ratio on the adsorbent.« less

  13. Sensing, Measurement, and Forecasting | Grid Modernization | NREL

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

    Sensing, Measurement, and Forecasting NREL measures weather resources and power systems, forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support a modern grid. Photo of solar resource monitoring equipment Modernizing the grid involves assessing its health in real time, predicting its behavior and potential disruptions, and quickly responding to events-which requires understanding vital parameters throughout the electric

  14. Pollutant Exposures from Natural Gas Cooking Burners: A Simulation-Based Assessment for Southern California

    SciTech Connect (OSTI)

    Logue, Jennifer M.; Klepeis, Neil E.; Lobscheid, Agnes B.; Singer, Brett C.

    2014-06-01

    Residential natural gas cooking burners (NGCBs) can emit substantial quantities of pollutants and they are typically used without venting. The objective of this study is to quantify pollutant concentrations and occupant exposures resulting from NGCB use in California homes. A mass balance model was applied to estimate time-dependent pollutant concentrations throughout homes and the "exposure concentrations" experienced by individual occupants. The model was applied to estimate nitrogen dioxide (NO{sub 2}), carbon monoxide (CO), and formaldehyde (HCHO) concentrations for one week each in summer and winter for a representative sample of Southern California homes. The model simulated pollutant emissions from NGCBs, NO{sub 2} and CO entry from outdoors, dilution throughout the home, and removal by ventilation and deposition. Residence characteristics and outdoor concentrations of CO and NO{sub 2} were obtained from available databases. Ventilation rates, occupancy patterns, and burner use were inferred from household characteristics. Proximity to the burner(s) and the benefits of using venting range hoods were also explored. Replicate model executions using independently generated sets of stochastic variable values yielded estimated pollutant concentration distributions with geometric means varying less than 10%. The simulation model estimates that in homes using NGCBs without coincident use of venting range hoods, 62%, 9%, and 53% of occupants are routinely exposed to NO{sub 2}, CO, and HCHO levels that exceed acute health-based standards and guidelines. NGCB use increased the sample median of the highest simulated 1-hr indoor concentrations by 100, 3000, and 20 ppb for NO{sub 2}, CO, and HCHO, respectively. Reducing pollutant exposures from NGCBs should be a public health priority. Simulation results suggest that regular use of even moderately effective venting range hoods would dramatically reduce the percentage of homes in which concentrations exceed health-based

  15. Efficient Use of Natural Gas Based Fuels in Heavy-Duty Engines

    Office of Energy Efficiency and Renewable Energy (EERE)

    Natural gas and other liquid feedstocks for transportation fuels are compared for use in a dual-fuel engine. Benefits include economic stability, national security, environment, and cost.

  16. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in ...

  17. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

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

    Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ ... Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen, ...

  18. Data Collection and Comparison with Forecasted Unit Sales of...

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

    Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection ...

  19. Computerized map-based information management system for natural resource management

    SciTech Connect (OSTI)

    Miller, K.

    1995-12-01

    Federal agencies, states and resource managers have control and stewardship responsibility over a significant inventory of natural resources. A number of federal regulations require the review, protection and preservation of natural resource protection. Examples of such actions include the reauthorization of the Clean Water Act and the modification of the National Contingency Plan to incorporate the requirements of the Oil Pollution Act of 1990. To successfully preserve conserve and restore natural resources on federal reservations, and state and private lands, and to comply with Federal regulations designed to protect natural resources located on their sites, and the type of information on these resources required by environmental regulations. This paper presents an approach using a computerized, graphical information management system to catalogue and track data for the management of natural resources under Federal and state regulations, and for promoting resource conservation, preservation and restoration. The system is designed for use by Federal facility resource managers both for the day-to-day management of resources under their control, and for the longer-term management of larger initiatives, including restoration of significant or endangered resources, participation in regional stewardship efforts, and general ecosystem management. The system will be valuable for conducting natural resource baseline inventories an implementing resource management plans on lands other than those controlled by the Federal government as well. The system can provide a method for coordinating the type of natural resource information required by major federal environmental regulations--thereby providing a cost-effective means for managing natural resource information.

  20. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

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

  1. Technology-Based Oil and Natural Gas Plays: Shale Shock! Could There Be Billions in the Bakken?

    Reports and Publications (EIA)

    2006-01-01

    This report presents information about the Bakken Formation of the Williston Basin: its location, production, geology, resources, proved reserves, and the technology being used for development. This is the first in a series intending to share information about technology-based oil and natural gas plays.

  2. STEO December 2012 - natural gas production

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

    2012 natural gas production seen at record 69 billion cubic feet per day U.S. natural gas production is expected to increase 4.5 percent this year to a record 69 billion cubic feet per day, according to the new monthly energy forecast from the U.S. Energy Information Administration. A big portion of that natural gas is going to the U.S. electric power sector, which is generating more electricity from gas in place of coal. Consumption of natural gas for power generation this year is forecast to

  3. Coal Fired Power Generation Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Coal Fired Power Generation Market Forecast Home There are currently no posts in this category. Syndicate...

  4. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Offshore Lubricants Market Forecast Home There are currently no posts in this category. Syndicate...

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  7. Flood Forecasting in River System Using ANFIS

    SciTech Connect (OSTI)

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  8. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

    The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030....

  9. energy data + forecasting | OpenEI Community

    Open Energy Info (EERE)

    energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in...

  10. Accounting for fuel price risk when comparing renewable togas-fired generation: the role of forward natural gas prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2004-07-17

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.

  11. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

    Templeton, K.J.; Clary, J.L.

    1994-09-01

    This report describes a 30-year forecast of the solid waste volumes by container type. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste. These volumes and their associated container types will be generated or received at the US Department of Energy Hanford Site for storage, treatment, and disposal at Westinghouse Hanford Company`s Solid Waste Operations Complex (SWOC) during a 30-year period from FY 1994 through FY 2023. The forecast data for the 30-year period indicates that approximately 307,150 m{sup 3} of LLMW and TRU/TRUM waste will be managed by the SWOC. The main container type for this waste is 55-gallon drums, which will be used to ship 36% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of 55-gallon drums is Past Practice Remediation. This waste will be generated by the Environmental Restoration Program during remediation of Hanford`s past practice sites. Although Past Practice Remediation is the primary generator of 55-gallon drums, most waste generators are planning to ship some percentage of their waste in 55-gallon drums. Long-length equipment containers (LECs) are forecasted to contain 32% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of LECs is the Long-Length Equipment waste generator, which is responsible for retrieving contaminated long-length equipment from the tank farms. Boxes are forecasted to contain 21% of the waste. These containers are primarily forecasted for use by the Environmental Restoration Operations--D&D of Surplus Facilities waste generator. This waste generator is responsible for the solid waste generated during decontamination and decommissioning (D&D) of the facilities currently on the Surplus Facilities Program Plan. The remaining LLMW and TRU/TRUM waste volume is planned to be shipped in casks and other miscellaneous containers.

  12. Calif--Coastal Region Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Liquids Lease Condensate, Proved Reserves (Million Barrels) Calif--Coastal Region Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 1 1 0 0 0 0 1990's 0 1 1 2 2 1 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 3 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  13. Texas--RRC District 1 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 1 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 16 1980's 18 20 24 35 33 33 30 22 23 15 1990's 20 23 24 23 23 23 44 46 32 161 2000's 49 35 34 24 31 31 32 43 44 87 2010's 163 158 197 233 343 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  14. Texas--RRC District 10 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 10 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 356 1980's 350 349 376 397 425 416 411 402 351 331 1990's 318 346 327 316 305 343 323 372 342 191 2000's 191 311 326 315 373 367 396 458 473 494 2010's 566 578 522 481 598 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  15. Texas--RRC District 2 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 2 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 19 1980's 16 20 23 26 22 24 20 32 25 16 1990's 17 14 14 14 12 11 8 12 10 12 2000's 13 14 11 13 15 19 16 17 17 15 2010's 47 229 506 594 706 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  16. Texas--RRC District 3 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 3 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 54 1980's 52 51 53 57 53 49 53 75 58 73 1990's 49 48 39 57 54 68 79 116 77 74 2000's 69 82 71 72 72 78 75 128 65 74 2010's 75 76 81 63 67 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Texas--RRC District 4 Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Reserves (Million Barrels) Proved Reserves (Million Barrels) Texas--RRC District 4 Onshore Natural Gas Liquids Lease Condensate, Proved Reserves (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 76 1980's 75 77 85 80 87 86 84 80 74 72 1990's 71 69 65 65 70 70 82 86 96 122 2000's 90 97 91 85 73 71 87 77 79 74 2010's 96 202 181 228 223 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  18. Texas--RRC District 5 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 5 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 24 1980's 32 42 44 61 61 62 73 76 72 65 1990's 61 53 55 50 50 47 48 31 31 24 2000's 24 43 39 40 44 40 42 50 126 192 2010's 225 237 214 183 193 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  19. Texas--RRC District 6 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 6 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 228 1980's 268 259 232 280 253 247 224 213 210 212 1990's 195 195 205 202 218 223 242 221 235 182 2000's 182 215 213 195 233 264 279 324 318 330 2010's 369 360 269 376 387 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  20. Texas--RRC District 7B Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 7B Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 62 1980's 82 99 99 129 103 101 106 90 95 71 1990's 74 81 67 73 61 69 64 57 48 34 2000's 34 28 24 31 42 89 131 200 269 326 2010's 359 416 295 332 312 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  1. Texas--RRC District 7C Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 7C Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 168 1980's 120 172 184 204 219 242 232 231 226 225 1990's 234 218 266 250 241 255 285 309 266 291 2000's 291 271 326 319 365 391 404 464 402 412 2010's 465 549 524 438 473 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  2. Texas--RRC District 8 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 8 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 452 1980's 452 498 554 650 662 646 697 623 530 542 1990's 545 466 426 430 398 432 417 447 479 479 2000's 479 504 488 484 487 559 547 525 524 536 2010's 618 689 802 830 1,240 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  3. Texas--RRC District 8A Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 8A Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 350 1980's 289 335 296 262 282 282 331 307 325 332 1990's 353 333 257 297 267 284 262 290 226 222 2000's 222 250 180 163 197 248 231 260 194 201 2010's 230 239 242 239 245 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  4. Texas--RRC District 9 Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Expected Future Production (Million Barrels) Texas--RRC District 9 Natural Gas Plant Liquids, Expected Future Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 75 1980's 81 81 111 115 113 106 112 107 102 90 1990's 100 96 89 88 94 90 116 96 91 156 2000's 156 182 229 228 228 276 372 347 348 419 2010's 488 552 542 578 662 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. Natural gas transmission and distribution model of the National Energy Modeling System

    SciTech Connect (OSTI)

    1997-02-01

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. From 1982 through 1993, the Intermediate Future Forecasting System (IFFS) was used by the EIA for its analyses, and the Gas Analysis Modeling System (GAMS) was used within IFFS to represent natural gas markets. Prior to 1982, the Midterm Energy Forecasting System (MEFS), also referred to as the Project Independence Evaluation System (PIES), was employed. NEMS was developed to enhance and update EIA`s modeling capability by internally incorporating models of energy markets that had previously been analyzed off-line. In addition, greater structural detail in NEMS permits the analysis of a broader range of energy issues. The time horizon of NEMS is the midterm period (i.e., through 2015). In order to represent the regional differences in energy markets, the component models of NEMS function at regional levels appropriate for the markets represented, with subsequent aggregation/disaggregation to the Census Division level for reporting purposes.

  6. Texas--State Offshore Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    Marketed Production (Million Cubic Feet) Texas--State Offshore Natural Gas Marketed Production (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 78,263 79,234 84,573 63,181 63,340 64,528 60,298 48,918 2000's 41,195 53,649 57,063 53,569 44,946 36,932 24,785 29,229 46,786 37,811 2010's 28,574 23,791 16,506 14,036 11,222 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  7. Natural Gas Weekly Update, Printer-Friendly Version

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

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

  8. Natural Gas Weekly Update, Printer-Friendly Version

    Gasoline and Diesel Fuel Update (EIA)

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  9. The Value of Improved Short-Term Wind Power Forecasting

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

    ... up-ramp reserves c down cost in MWh of down-ramp reserves R down MW range for ... power forecasting and the increased gas usage that comes with less-accurate forecasting. ...

  10. PBL FY 2003 Second Quarter Review Forecast of Generation Accumulated...

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

    the rate period (i.e., FY 2002-2006), a forecast of that end-of-year Accumulated Net Revenue (ANR) will be completed. If the ANR at the end of the forecast year falls below the...

  11. Solar Forecasting Gets a Boost from Watson, Accuracy Improved...

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

    Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM ...

  12. Natural Abundance 17O Nuclear Magnetic Resonance and Computational Modeling Studies of Lithium Based Liquid Electrolytes

    SciTech Connect (OSTI)

    Deng, Xuchu; Hu, Mary Y.; Wei, Xiaoliang; Wang, Wei; Chen, Zhong; Liu, Jun; Hu, Jian Z.

    2015-07-01

    Natural abundance 17O NMR measurements were conducted on electrolyte solutions consisting of Li[CF3SO2NSO2CF3] (LiTFSI) dissolved in the solvents of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and their mixtures at various concentrations. It was observed that 17O chemical shifts of solvent molecules change with the concentration of LiTFSI. The chemical shift displacements of carbonyl oxygen are evidently greater than those of ethereal oxygen, strongly indicating that Li+ ion is coordinated with carbonyl oxygen rather than ethereal oxygen. To understand the detailed molecular interaction, computational modeling of 17O chemical shifts was carried out on proposed solvation structures. By comparing the predicted chemical shifts with the experimental values, it is found that a Li+ ion is coordinated with four double bond oxygen atoms from EC, PC, EMC and TFSI- anion. In the case of excessive amount of solvents of EC, PC and EMC the Li+ coordinated solvent molecules are undergoing quick exchange with bulk solvent molecules, resulting in average 17O chemical shifts. Several kinds of solvation structures are identified, where the proportion of each structure in the liquid electrolytes investigated depends on the concentration of LiTFSI.

  13. Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01

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

  14. Combined Heat And Power Installation Market Forecast | OpenEI...

    Open Energy Info (EERE)

    Combined Heat And Power Installation Market Forecast Home There are currently no posts in this category. Syndicate...

  15. Wind power forecasting in U.S. electricity markets.

    SciTech Connect (OSTI)

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

    2010-04-01

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

  16. Wind power forecasting in U.S. Electricity markets

    SciTech Connect (OSTI)

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

    2010-04-15

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

  17. DOE Taking Wind Forecasting to New Heights | Department of Energy

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

    Taking Wind Forecasting to New Heights DOE Taking Wind Forecasting to New Heights May 18, 2015 - 3:24pm Addthis A 2013 study conducted for the U.S. Department of Energy (DOE) by the National Oceanic and Atmospheric Administration (NOAA), AWS Truepower, and WindLogics in the Great Plains and Western Texas, demonstrated that wind power forecasts can be improved substantially using data collected from tall towers, remote sensors, and other devices, and incorporated into improved forecasting models

  18. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

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

  19. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

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

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

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

    Department of Energy Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf (15.76 MB) More Documents & Publications QER - Comment of Edison Electric Institute (EEI) 1 QER - Comment of Canadian Hydropower Association QER - Comment of Edison Electric Institute (EEI) 2

  1. LCO Nature | Open Energy Information

    Open Energy Info (EERE)

    LCO Nature Jump to: navigation, search Name: LCO Nature Place: Germany Sector: Renewable Energy Product: Based in Hamburg, LCO Nature is a consultancy and renewable energy project...

  2. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    SciTech Connect (OSTI)

    Zulkepli, Jafri Abidin, Norhaslinda Zainal; Fong, Chan Hwa

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  3. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

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

  4. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. 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 statistics are intended for the use of 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 2006 fiscal year.

  5. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

    SciTech Connect (OSTI)

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-08-15

    Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models

  6. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

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

  7. Weekly Natural Gas Storage Report

    Weekly Natural Gas Storage Report (EIA)

    Weekly Natural Gas Storage Report PERFORMANCE EVALUATION for 2011 through 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 September 2014 U.S. Energy Information Administration | PERFORMANCE EVALUATION for 2011 through 2013 i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  8. High natural gas output and inventories contribute to lower prices

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

    High natural gas output and inventories contribute to lower prices High natural gas production and ample gas inventories are expected to keep natural gas prices relatively low for the rest of 2015. In its new monthly forecast, the U.S. Energy Information Administration says that while expected production growth is slowing from last year's torrid pace, domestic natural gas production in 2015 is still expected to be almost 6 percent above the 2014 level. Higher production has pushed U.S. natural

  9. EIA - Natural Gas Pipeline Network - Natural Gas Supply Basins...

    Gasoline and Diesel Fuel Update (EIA)

    Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates U.S. Natural Gas Supply Basins Relative to Major Natural ...

  10. EIA - Natural Gas Pipeline Network - Natural Gas Transmission Path Diagram

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

    Transmission Path Diagram About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Transmission Path Natural Gas Transmission Path