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1

Wind Energy Forecasting Technology Update: 2004  

Science Conference Proceedings (OSTI)

This report describes the status of wind energy forecasting technology for predicting wind speed and energy generation of wind energy facilities short-term (minutes to hours), intermediate-term (hours to days), and long-term (months to years) average wind speed and energy generation. The information should be useful to companies that are evaluating or planning to incorporate wind energy forecasting into their operations.

2005-04-26T23:59:59.000Z

2

Wind Energy Forecasting Technology Update: 2006  

Science Conference Proceedings (OSTI)

The worldwide installed wind generation capacity increased by 25 and reached almost 60,000 MW worldwide during 2005. As wind capacity continues to grow and large regional concentrations of wind generation emerge, utilities and regional transmission organizations will increasingly need accurate same-day and next-day forecasts of wind energy generation to dispatch system generation and transmission resource and anticipate rapid changes of wind generation.

2006-12-05T23:59:59.000Z

3

Wind Energy Forecasting Technology Update: 2005  

Science Conference Proceedings (OSTI)

The worldwide installed wind generation capacity increased by 25 and reached almost 60,000 MW worldwide during 2005. As wind capacity continues to grow and large regional concentrations of wind generation emerge, utilities and regional transmission organizations will increasingly need accurate same-day and next-day forecasts of wind energy generation to dispatch system generation and transmission resource and anticipate rapid changes of wind generation. The project objective is to summarize the results o...

2006-03-31T23:59:59.000Z

4

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

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

in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar --...

5

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

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

6

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

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

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

7

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

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

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

8

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network (OSTI)

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

Washington at Seattle, University of

9

Wind forecasting objectives for utility schedulers and energy traders  

DOE Green Energy (OSTI)

The wind energy industry and electricity producers can benefit in a number of ways from increased wind forecast accuracy. Higher confidence in the reliability of wind forecasts can help persuade an electric utility to increase the penetration of wind energy into its operating system and to augment the capacity value of wind electric generation. Reliable forecasts can also assist daily energy traders employed by utilities in marketing the available and anticipated wind energy to power pools and other energy users. As the number of utilities with wind energy experience grows, and wind energy penetration levels increase, the need for reliable wind forecasts will likely grow as well. This period of wind energy growth also coincides with advances in computer weather prediction technology that could lead to more accurate wind forecasts. Thus, it is important to identify the type of forecast information needed by utility schedulers and energy traders. This step will help develop approaches to the challenge of wind forecasting that will result in useful products being supplied to utilities or other energy generating entities. This paper presents the objectives, approach, and current findings of a US Department of Energy National Renewable Energy Laboratory (DOE/NREL) initiative to develop useful wind forecasting tools for utilities involved with wind energy generation. The focus of this initiative thus far has been to learn about the needs of prospective utility users. NREL representatives conducted a series of onsite interviews with key utility staff, usually schedulers and research planners, at seven US utilities. The purpose was to ascertain information on actual scheduling and trading procedures, and how utilities could integrate wind forecasting into these activities.

Schwartz, M.N. [National Renewable Energy Lab., Golden, CO (United States); Bailey, B.H. [AWS Scientific, Inc., Albany, NY (United States)

1998-05-01T23:59:59.000Z

11

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

DOE Green Energy (OSTI)

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

Lantz, E.; Hand, M.

2010-05-01T23:59:59.000Z

12

California Regional Wind Energy Forecasting System Development, Vol. 3  

Science Conference Proceedings (OSTI)

The rated capacity of wind generation in California is expected to grow rapidly in the future beyond the approximately 2100 MW in place at the end of 2005. The main drivers are the state's 20 percent Renewable Portfolio Standard requirement in 2010 and the low cost of wind energy relative to other renewable energy sources. As wind is an intermittent generation resource and weather changes can cause large and rapid changes in output, system operators will need accurate and robust wind energy forecasting ...

2006-11-15T23:59:59.000Z

13

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

DOE Green Energy (OSTI)

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

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

2011-03-28T23:59:59.000Z

14

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

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

15

European Union Wind Energy Forecasting Model Development and Testing: U.S. Department of Energy -- EPRI Wind Turbine Verification Pr ogram  

Science Conference Proceedings (OSTI)

Wind forecasting can increase the strategic and market values of wind power from large wind facilities. This report summarizes the results of the European Union (EU) wind energy forecasting project and performance testing of the EU wind forecasting model. The testing compared forecast and observed wind speed and generation data from U.S. wind facilities.

1999-12-15T23:59:59.000Z

16

Emerging challenges in wind energy forecasting for Australia  

E-Print Network (OSTI)

Growing concern about climate change has led to significant interest in renewable energy resources such as wind energy. However, such non-storable energy sources present a significant issue – how to maintain continuity of supply in the event of possible disturbances to power production. For example, in the case of wind energy, such disturbances can result from extreme weather events due to frontal systems or rapidly evolving low pressure systems. Such events cannot be avoided, but if they can be accurately forecast, their impact can be minimized by ensuring that alternative sources are available to make up any power shortfalls. Thus as wind energy makes up an ever greater component of our energy supply, there is greater interest in developing models to produce accurate, local scale, wind-focused forecasts for wind farm sites that push the boundaries of current weather prediction techniques. In this article we present a case study focusing on the Woolnorth wind farm on the northwest tip of Tasmania, to highlight some of the key challenges that will be involved in developing such forecasts.

Merlinde J. Kay; Nicholas Cutler; Adam Micolich; Iain Macgill; Hugh Outhred Centre For Energy; Environmental Markets; South Wales

2008-01-01T23:59:59.000Z

17

California Wind Energy Forecasting System Development and Testing Phase 2: 12-Month Testing  

Science Conference Proceedings (OSTI)

This report describes results from the second phase of the California Wind Energy Forecasting System Development and Testing Project.

2003-07-22T23:59:59.000Z

18

Texas Wind Energy Forecasting System Development and Testing, Phase 1: Initial Testing  

Science Conference Proceedings (OSTI)

This report describes initial results from the Texas Wind Energy Forecasting System Development and Testing Project at a 75-MW wind project in west Texas.

2003-12-31T23:59:59.000Z

19

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

DOE Green Energy (OSTI)

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 direction, using its wind tunnel facility at the windmill farm at the Altamont Pass. The main objective of LLNL's involvement is to provide UC-Davis with improved wind forecasts to drive the parameterization scheme of turbine power curves developed from the wind tunnel facility. Another objective of LLNL's effort is to support the windmill farm operation with real-time wind forecasts for the effective energy management. The forecast skill in capturing the situation to meet the cut-in and cutout speed of given turbines would help reduce the operation cost in low and strong wind scenarios, respectively. The main focus of this report is to evaluate the wind forecast errors of LLNL's three-dimensional real-time weather forecast model at the location with the complex terrain. The assessment of weather forecast accuracy would help quantify the source of wind energy forecast errors from the atmospheric forecast model and/or wind-tunnel module for further improvement in the wind energy forecasting system.

Chin, H S

2005-07-26T23:59:59.000Z

20

California Wind Energy Forecasting System Development and Testing, Phase 1: Initial Testing  

Science Conference Proceedings (OSTI)

Wind energy forecasting uses sophisticated numerical weather forecasting and wind plant power generation models to predict the hourly energy generation of a wind power plant up to 48 hours in advance. As a result, it has great potential to address the needs of the California Independent System Operator (ISO) and the wind plant operators, as well as power marketers and buyers and utility system dispatch personnel. This report gives the results of 28 days of testing of wind energy forecasting at a Californ...

2003-01-31T23:59:59.000Z

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


21

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

E-Print Network (OSTI)

European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind forecasting. I. INTRODUCTION HE actual large-scale integration of wind energy in several European countries enhance the position of wind energy compared to other dispatchable forms of generation. Predicting

Paris-Sud XI, Université de

22

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

E-Print Network (OSTI)

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

Genton, Marc G.

23

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

SciTech Connect

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

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

1983-07-01T23:59:59.000Z

24

Texas Wind Energy Forecasting System Development and Testing: Phase 2: 12-Month Testing  

Science Conference Proceedings (OSTI)

Wind energy forecasting systems are expected to support system operation in cases where wind generation contributes more than a few percent of total generating capacity. This report presents final results from the Texas Wind Energy Forecasting System Development and Testing Project at a 75-MW wind project in west Texas.

2004-09-30T23:59:59.000Z

25

High Horizontal and Vertical Resolution Limited-Area Model: Near-Surface and Wind Energy Forecast Applications  

Science Conference Proceedings (OSTI)

As harvesting of wind energy grows, so does the need for improved forecasts from the surface to the top of wind turbines. To improve mesoscale forecasts of wind, temperature, and dewpoint temperature in this layer, two different approaches are ...

Natacha B. Bernier; Stéphane Bélair

2012-06-01T23:59:59.000Z

26

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

E-Print Network (OSTI)

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

27

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

DOE Green Energy (OSTI)

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

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

2011-10-01T23:59:59.000Z

28

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

Science Conference Proceedings (OSTI)

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

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

2012-09-01T23:59:59.000Z

29

California Regional Wind Energy Forecasting System Development, Volume 2:  

Science Conference Proceedings (OSTI)

The rated capacity of wind generation in California is expected to grow rapidly in the future beyond the approximately 2100 MW in place at the end of 2005. The main drivers are the state's 20 percent renewable portfolio standard requirement in 2010 and the low cost of wind energy relative to other renewable energy sources.

2006-11-15T23:59:59.000Z

30

California Wind Energy Forecasting Program Description and Status - 2000: California Energy Commission--EPRI Wind Energy Forecasting Program  

Science Conference Proceedings (OSTI)

The modern era of wind power began in the early 1980s when the first large installations of modern wind turbines were installed in California. The industry has grown rapidly in recent years and, at the end of 1999, the total installed wind capacity was 13.4 gigawatts (GW) worldwide and 2.5 GW in the U.S., of which about 1.6 GW is operating in California. Deregulation of the California electricity markets in 1998 created a challenge for the California investor-owned utilitiies and the owners and operators...

2000-12-18T23:59:59.000Z

31

California Regional Wind Energy Forecasting System Development, Volume 4: California Wind Generation Research Dataset (CARD)  

Science Conference Proceedings (OSTI)

The rated capacity of wind generation in California is expected to grow rapidly in the future beyond the approximately 2100 megawatts in place at the end of 2005. The main drivers are the state's 20 percent renewable portfolio standard requirement in 2010 and the low cost of wind energy relative to other renewable energy sources. As wind is an intermittent generation resource and weather changes can cause large and rapid changes in output, system operators will need accurate and robust wind energy forec...

2006-11-13T23:59:59.000Z

32

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

SciTech Connect

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. 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.

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

2010-01-01T23:59:59.000Z

33

Forecasting Wind Markets  

U.S. Energy Information Administration (EIA)

Emerging Technologies, Data, and NEM Modeling Issues in Wind Resource Supply Data and Modeling Chris Namovicz ASA Committee on Energy Statistics

34

Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

Probabilistic forecasts of wind vectors are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Unlike other common forecasting ...

J. McLean Sloughter; Tilmann Gneiting; Adrian E. Raftery

2013-06-01T23:59:59.000Z

35

Forecasting the Wind to Reach Significant Penetration Levels of Wind Energy  

Science Conference Proceedings (OSTI)

Advances in atmospheric science are critical to increased deployment of variable renewable energy (VRE) sources. For VRE sources, such as wind and solar, to reach high penetration levels in the nation's electric grid, electric system operators and VRE ...

Melinda Marquis; Jim Wilczak; Mark Ahlstrom; Justin Sharp; Andrew Stern; J. Charles Smith; Stan Calvert

2011-09-01T23:59:59.000Z

36

Wind Speed Forecasting for Power System Operation  

E-Print Network (OSTI)

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model.

Zhu, Xinxin

2013-08-01T23:59:59.000Z

37

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

SciTech Connect

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

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

2010-09-01T23:59:59.000Z

38

Value of Wind Power Forecasting  

DOE Green Energy (OSTI)

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

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

2011-04-01T23:59:59.000Z

39

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network (OSTI)

bid is computed by exploiting the forecast energy price for the day ahead market, the historical windOptimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts Antonio statistics at the plant site and the day-ahead wind speed forecasts provided by a meteorological service. We

Giannitrapani, Antonello

40

Short-Term Wind Speed Forecasting for Power System Operations  

E-Print Network (OSTI)

Global large scale penetration of wind energy is accompanied by significant challenges due to the intermittent and unstable nature of wind. High quality short-term wind speed forecasting is critical to reliable and secure power system operations. This paper gives an overview of the current status of worldwide wind power developments and future trends, and reviews some statistical short-term wind speed forecasting models, including traditional time series models and advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented.

Xinxin Zhu; Marc G. Genton

2011-01-01T23:59:59.000Z

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


41

Short term wind power forecasting using time series neural networks  

Science Conference Proceedings (OSTI)

Forecasting wind power energy is very important issue in a liberalized market and the prediction tools can make wind energy be competitive in these kinds of markets. This paper will study an application of time-series and neural network for predicting ... Keywords: neural networks, time series, wind power forecasting

Mohammadsaleh Zakerinia; Seyed Farid Ghaderi

2011-04-01T23:59:59.000Z

42

A survey on wind power ramp forecasting.  

DOE Green Energy (OSTI)

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

Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-02-23T23:59:59.000Z

43

California Regional Wind Energy Forecasting System Development, Volume 1: Executive Summary  

Science Conference Proceedings (OSTI)

The rated capacity of wind generation in California is expected to grow rapidly in the future beyond the approximately 2100 megawatts (MW) in place at the end of 2005. The main drivers are the state's 20 Renewable Portfolio Standard requirement in 2010 and the low cost of wind energy relative to other renewable energy sources. As wind is an intermittent generation resource and weather changes can cause large and rapid changes in output, system operators will need accurate and robust wind energy forecasti...

2006-11-14T23:59:59.000Z

44

Forecasting Solar Wind Speeds  

E-Print Network (OSTI)

By explicitly taking into account effects of Alfven waves, I derive from a simple energetics argument a fundamental relation which predicts solar wind (SW) speeds in the vicinity of the earth from physical properties on the sun. Kojima et al. recently found from their observations that a ratio of surface magnetic field strength to an expansion factor of open magnetic flux tubes is a good indicator of the SW speed. I show by using the derived relation that this nice correlation is an evidence of the Alfven wave which accelerates SW in expanding flux tubes. The observations further require that fluctuation amplitudes of magnetic field lines at the surface should be almost universal in different coronal holes, which needs to be tested by future observations.

Takeru K. Suzuki

2006-02-03T23:59:59.000Z

45

Short term wind speed forecasting with evolved neural networks  

Science Conference Proceedings (OSTI)

Concerns about climate change, energy security and the volatility of the price of fossil fuels has led to an increased demand for renewable energy. With wind turbines being one of the most mature renewable energy technologies available, the global use ... Keywords: forecasting, renewable energy, wind-speed

David Corne; Alan Reynolds; Stuart Galloway; Edward Owens; Andrew Peacock

2013-07-01T23:59:59.000Z

46

Short-term wind speed forecasting based on a hybrid model  

Science Conference Proceedings (OSTI)

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting ... Keywords: Forecasting, RBF neural networks, Seasonal adjustment, Wavelet transform, Wind speed

Wenyu Zhang, Jujie Wang, Jianzhou Wang, Zengbao Zhao, Meng Tian

2013-07-01T23:59:59.000Z

47

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations  

E-Print Network (OSTI)

Power Forecasting in Five U.S. Electricity Markets MISO NYISO PJM ERCOT CAISO Peak load 109,157 MW (7 ........................................................................................... 18 4 WIND POWER FORECASTING AND ELECTRICITY MARKET OPERATIONS............................................................ 18 4-1 Market Operation and Wind Power Forecasting in Five U.S. Electricity Markets .......... 21 #12

Kemner, Ken

48

Missing wind data forecasting with adaptive neuro-fuzzy inference system  

Science Conference Proceedings (OSTI)

In any region, to begin generating electricity from wind energy, it is necessary to determine the 1-year distribution characteristics of wind speed. For this aim, a wind observation station must be constructed and 1-year wind speed and direction data ... Keywords: ANFIS, Back-propagation, Forecasting, Missing data, Wind energy, Wind speed

Fatih O. Hocaoglu; Yusuf Oysal; Mehmet Kurban

2009-02-01T23:59:59.000Z

49

Development of Wind Speed Forecasting Model Based on the Weibull Probability Distribution  

Science Conference Proceedings (OSTI)

Wind is a variable energy source. The power output of a wind turbine generator (WTG) unit, therefore, fluctuates with wind speed variations. Accurate models reflecting the variability of wind speed is hence required in both reliability evaluation of ... Keywords: Wind Energy, Wind Speed Forecasting Model, Weibull Distribution, Maximum Likelihood Method, Time Series Model

Ruigang Wang; Wenyi Li; B. Bagen

2011-02-01T23:59:59.000Z

50

The effects of energy storage properties and forecast accuracy on mitigating variability in wind power generation  

E-Print Network (OSTI)

Electricity generation from wind power is increasing worldwide. Wind power can offset traditional fossil fuel generators which is beneficial to the environment. However, wind generation is unpredictable. Wind speeds have ...

Jaworsky, Christina A

2013-01-01T23:59:59.000Z

51

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

SciTech Connect

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

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

2011-10-01T23:59:59.000Z

52

Use of wind power forecasting in operational decisions.  

DOE Green Energy (OSTI)

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

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

2011-11-29T23:59:59.000Z

53

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network (OSTI)

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

54

Subhourly wind forecasting techniques for wind turbine operations  

DOE Green Energy (OSTI)

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

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

1984-08-01T23:59:59.000Z

55

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

DOE Green Energy (OSTI)

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

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

2012-09-01T23:59:59.000Z

56

A Short-Term Ensemble Wind Speed Forecasting System for Wind Power Applications  

Science Conference Proceedings (OSTI)

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather ...

Justin J. Traiteur; David J. Callicutt; Maxwell Smith; Somnath Baidya Roy

2012-10-01T23:59:59.000Z

57

Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)  

DOE Green Energy (OSTI)

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

Hodge, B. M.; Milligan, M.

2011-07-01T23:59:59.000Z

58

Powering up with space-time wind forecasting  

E-Print Network (OSTI)

The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality. High-quality short-term forecasts of wind speed are vital to making this a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, i.e., highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an off-site location. This paper both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting at new locations. We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model’s predictions. Some key words: Circular variable, power curve, skew-t distribution, wind direction, wind speed.

A S. Hering; Marc G. Genton

2009-01-01T23:59:59.000Z

59

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

60

Wind Energy  

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

FUPWG Meeting FUPWG Meeting NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy operated by the Alliance for Sustainable Energy, LLC Robi Robichaud November 18, 2009 Topics Introduction Review of the Current Wind Market Drivers for Wind Development Siting g Issues Wind Resource Assessment Wind Characteristics Wind Power Potential Basic Wind Turbine Theory Basic Wind Turbine Theory Types of Wind Turbines Facts About Wind Siting Facts About Wind Siting Wind Performance 1. United States: MW 1 9 8 2 1 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 Current Status of the Wind Industry Total Global Installed Wind Capacity Total Global Installed Wind Capacity Total Global Installed Wind Capacity

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


61

Solar Wind Forecasting with Coronal Holes  

E-Print Network (OSTI)

An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the method presented here can be used to forecast solar wind velocities and magnetic polarity from a single coronal hole image, along with a single magnetic full-disk image. The coronal hole parameters used in this study are estimated with Kitt Peak Vacuum Telescope He I 1083 nm spectrograms and photospheric magnetograms. Solar wind and coronal hole data for the period between May 1992 and September 2003 are investigated. The new model is found to be accurate to within 10% of observed solar wind measurements for its best one-month periods, and it has a linear correlation coefficient of ~0.38 for the full 11 years studied. Using a single estimated coronal hole map, the model can forecast the Earth directed solar wind velocity up to 8.5 days in advance. In addition, this method can be used with any source of coronal hole area and location data.

S. Robbins; C. J. Henney; J. W. Harvey

2007-01-09T23:59:59.000Z

62

Wind News | Department of Energy  

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

Wind News Wind News Wind News RSS February 7, 2011 Salazar, Chu Announce Major Offshore Wind Initiatives Strategic plan, $50 million in R&D funding, identified Wind Energy Areas will speed offshore wind energy development December 16, 2010 Department of Energy Finalizes Loan Guarantee to Support World's Largest Wind Project 845-Megawatt Wind Facility Will Create Hundreds of Jobs and Avoid Over 1.2 Million Tons of Carbon Dioxide Annually October 29, 2010 Statement by Energy Secretary Steven Chu on Today's Grand Opening of the Nordex Manufacturing Facility in Jonesboro, Arkansas Recovery Act investment creates jobs, helps lay the foundation for a clean energy economy September 13, 2010 DOE Announces More than $5 Million to Support Wind Energy Development Funds to Enhance Short-Term Wind Forecasting and Accelerate Midsize Wind

63

Wind | Department of Energy  

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

Assessment and Characterization Defining, measuring, and forecasting land-based and offshore wind resources Environmental Impacts and Siting of Wind Projects Avoiding,...

64

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

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

65

Space-Time Wind Speed Forecasting for Improved Power System Dispatch  

E-Print Network (OSTI)

In order to support large scale integration of wind power, state-of-the-art wind speed forecasting methods should provide accurate and adequate information to enable efficient scheduling of wind power in electric energy systems. In this article, space-time wind forecasts are incorporated into power system economic dispatch models. First, we proposed a new space-time wind forecasting model, which generalizes and improves upon a so-called regime-switching space-time model by allowing the forecast regimes to vary with the dominant wind direction and with the seasons. Then, results from the new wind forecasting model are implemented into a power system economic dispatch model, which takes into account both spatial and temporal wind speed correlations. This, in turn, leads to an overall more cost-effective scheduling of system-wide wind generation portfolio. The potential economic benefits arise in the system-wide generation cost savings and in the ancillary service cost savings. This is illustrated in a test system in the northwest region of the U.S. Compared with persistent and autoregressive models, our proposed method could lead to annual integration cost savings on the scale of tens of millions of dollars in regions with high wind penetration, such as Texas and the Northwest. Key words: Power system economic dispatch; Power system operation; Space-time statistical model; Wind data; Wind speed forecasting.

Xinxin Zhu; Marc G. Genton; Yingzhong Gu; Le Xie

2012-01-01T23:59:59.000Z

66

Solar Wind Forecast by Using Interplanetary Scintillation Observations  

Science Conference Proceedings (OSTI)

Interplanetary scintillation (IPS) allows us to determine solar wind velocity and density structures over a relatively short time by employing computer assisted tomography. This method can be applied to forecast solar wind changes for a few days prior to its reaching Earth. We have been attempting solar wind forecasting by using IPS data observed at Solar?Terrestrial Environment Laboratory (STELab)

Ken’ichi Fujiki; Hiroaki Ito; Munetoshi Tokumaru

2010-01-01T23:59:59.000Z

67

Wind power forecasting in U.S. electricity markets.  

Science Conference Proceedings (OSTI)

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

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

2010-04-01T23:59:59.000Z

68

Wind power forecasting in U.S. Electricity markets  

Science Conference Proceedings (OSTI)

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

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

2010-04-15T23:59:59.000Z

69

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

DOE Green Energy (OSTI)

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

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

2013-05-01T23:59:59.000Z

70

Modelling and forecasting wind speed intensity for weather risk management  

Science Conference Proceedings (OSTI)

The main interest of the wind speed modelling is on the short-term forecast of wind speed intensity and direction. Recently, its relationship with electricity production by wind farms has been studied. In fact, electricity producers are interested in ... Keywords: ARFIMA-FIGARCH, Auto Regressive Gamma, Gamma Auto Regressive, Weather risk management, Wind speed modelling, Wind speed simulation

Massimiliano Caporin; Juliusz Pre

2012-11-01T23:59:59.000Z

71

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

DOE Green Energy (OSTI)

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

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

2012-09-01T23:59:59.000Z

72

The ANEMOS Project: Next Generation Forecasting of Wind Power. G.Kariniotakis*  

E-Print Network (OSTI)

integration of wind energy in the developing liberalized electricity markets. Keywords - Wind power, short-resolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information will allow validation of the models and an analysis of the value of wind prediction for a competitive

Heinemann, Detlev

73

Energy Basics: Wind Energy Resources  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Wind Wind Turbines Wind Resources Wind Energy...

74

Energy Basics: Wind Energy Technologies  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Wind Wind Turbines Wind Resources Wind Energy...

75

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

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

76

Estimating the economic value of wind forecasting to utilities  

SciTech Connect

Utilities are sometimes reluctant to assign capacity value to wind plants because they are an intermittent resource. One of the potential difficulties is that the output of a wind plant may not be known in advance, thereby making it difficult for the utility to consider wind output as firm. In this paper, we examine the economics of an accurate wind forecast, and provide a range of estimates calculated by a production cost model and real utility data. We discuss how an accurate forecast will affect resource scheduling and the mechanism by which resource scheduling can benefit from an accurate wind forecast.

Milligan, M.R.; Miller, A.H. [National Renewable Energy Lab., Golden, CO (United States); Chapman, F. [Environmental Defense Fund, Oakland, CA (United States)

1995-05-01T23:59:59.000Z

77

Energy Basics: Wind Turbines  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Wind Wind Turbines Wind Resources Wind Turbines...

78

WIND ENERGY Wind Energ. (2012)  

E-Print Network (OSTI)

WIND ENERGY Wind Energ. (2012) Published online in Wiley Online Library (wileyonlinelibrary since energy production depends non-linearly on wind speed (U ), and wind speed observa- tions for the assessment of future long-term wind supply A. M. R. Bakker1 , B. J. J. M. Van den Hurk1 and J. P. Coelingh2 1

Haak, Hein

79

Impact of Wind PowerImpact of Wind Power Forecasting on Unit  

E-Print Network (OSTI)

Impact of Wind PowerImpact of Wind Power Forecasting on Unit Commitment and Dispatchp Jianhui Wang and University of Porto, Portugal 8th Int. Wind Integration Workshop, Bremen, Germany, Oct. 14 2009 #12;Outline of the information in wind power forecasts in system and market operationsin system and market operations Stochastic

Hudson, Randy

80

Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.  

DOE Green Energy (OSTI)

We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); (Univ. of Chicago); (New York Univ.)

2009-10-09T23:59:59.000Z

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


81

Wind Energy Leasing Handbook  

E-Print Network (OSTI)

Wind Energy Leasing Handbook Wind Energy Leasing Handbook E-1033 Oklahoma Cooperative Extension?..................................................................................................................... 31 What do wind developers consider in locating wind energy projects?............................................................................................ 37 How do companies and individuals invest in wind energy projects?....................................................................

Balasundaram, Balabhaskar "Baski"

82

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast

83

Energy Basics: Wind Energy Technologies  

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

Photo of a hilly field, with six visible wind turbines spinning in the wind. Wind energy technologies use the energy in wind for practical purposes such as generating...

84

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

DOE Green Energy (OSTI)

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

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

2009-11-20T23:59:59.000Z

85

Evaluating a Hybrid Prognostic–Diagnostic Model That Improves Wind Forecast Resolution in Complex Coastal Topography  

Science Conference Proceedings (OSTI)

The results from a hybrid approach that combines the forecasts of a mesoscale model with a diagnostic wind model to produce high-resolution wind forecasts in complex coastal orography are evaluated. The simple diagnostic wind model [Winds on ...

Francis L. Ludwig; Douglas K. Miller; Shawn G. Gallaher

2006-01-01T23:59:59.000Z

86

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

87

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

88

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

89

Wind Energy Technologies  

Science Conference Proceedings (OSTI)

... Avg Wind Speed 7.5 m/s 8.74 m/s GE 2.x turbine family ... 1 to 48 Hour Wind Forecasting ... Danish Transmission Grid w/ Interconnects & Offshore Sites ...

2012-08-31T23:59:59.000Z

90

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

DOE Green Energy (OSTI)

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

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

2012-07-01T23:59:59.000Z

91

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

92

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

93

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

94

Energy conservation and official UK energy forecasts  

SciTech Connect

Behind the latest United Kingdom (UK) official forecasts of energy demand are implicit assumptions about future energy-price elasticities. Mr. Pearce examines the basis of the forecasts and finds that the long-term energy-price elasticities that they imply are two or three times too low. The official forecasts substantially understate the responsiveness of demand to energy price rises. If more-realistic price elasticities were assumed, the official forecasts would imply a zero primary energy-demand growth to 2000. This raises the interesting possibility of a low energy future being brought about entirely by market forces. 15 references, 3 tables.

Pearce, D.

1980-09-01T23:59:59.000Z

95

Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010  

SciTech Connect

Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

Porter, K.; Rogers, J.

2010-04-01T23:59:59.000Z

96

Evaluation of a Wind-Wave System for Ensemble Tropical Cyclone Wave Forecasting. Part II: Waves  

Science Conference Proceedings (OSTI)

A wind-wave forecast system, designed with the intention of generating unbiased ensemble wave forecasts for extreme wind events, is assessed. Wave hindcasts for 12 tropical cyclones (TCs) are forced using a wind analysis produced from a ...

Steven M. Lazarus; Samuel T. Wilson; Michael E. Splitt; Gary A. Zarillo

2013-04-01T23:59:59.000Z

97

Evaluation of errors in national energy forecasts.  

E-Print Network (OSTI)

??Energy forecasts are widely used by the U.S. government, politicians, think tanks, and utility companies. While short-term forecasts were reasonably accurate, medium and long-range forecasts… (more)

Sakva, Denys

2005-01-01T23:59:59.000Z

98

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

99

Statistical Wind Power Forecasting Models: Results for U.S. Wind Farms; Preprint  

DOE Green Energy (OSTI)

Electricity markets in the United States are evolving. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast makes it possible for grid operators to schedule the economically efficient generation to meet the demand of electrical customers. In the evolving markets, some form of auction is held for various forward markets, such as hour ahead or day ahead. This paper develops several statistical forecasting models that can be useful in hour-ahead markets that have a similar tariff. Although longer-term forecasting relies on numerical weather models, the statistical models used here focus on the short-term forecasts that can be useful in the hour-ahead markets. We investigate the extent to which time-series analysis can improve on simplistic persistence forecasts. This project applied a class of models known as autoregressive moving average (ARMA) models to both wind speed and wind power output.

Milligan, M.; Schwartz, M.; Wan, Y.

2003-05-01T23:59:59.000Z

100

Development and testing of improved statistical wind power forecasting methods.  

DOE Green Energy (OSTI)

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

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

2011-12-06T23:59:59.000Z

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


101

Managing Wind Power Forecast Uncertainty in Electric Brandon Keith Mauch  

E-Print Network (OSTI)

i Managing Wind Power Forecast Uncertainty in Electric Grids Brandon Keith Mauch Co in Electric Grids Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have

102

Statistical Wind Power Forecasting for U.S. Wind Farms: Preprint  

DOE Green Energy (OSTI)

Electricity markets in the United States are evolving. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. The evolving markets hold some form of auction for various forward markets, such as hour ahead or day ahead. This paper describes several statistical forecasting models that can be useful in hour-ahead markets. Although longer-term forecasting relies on numerical weather models, the statistical models used here focus on the short-term forecasts that can be useful in the hour-ahead markets. The purpose of the paper is not to develop forecasting models that can compete with commercially available models. Instead, we investigate the extent to which time-series analysis can improve simplistic persistence forecasts. This project applied a class of models known as autoregressive moving average (A RMA) models to both wind speed and wind power output. The results from wind farms in Minnesota, Iowa, and along the Washington-Oregon border indicate that statistical modeling can provide a significant improvement in wind forecasts compared to persistence forecasts.

Milligan, M.; Schwartz, M. N.; Wan, Y.

2003-11-01T23:59:59.000Z

103

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

Science Conference Proceedings (OSTI)

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

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

2010-11-02T23:59:59.000Z

104

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity forecast is the combined product of the hard work and expertise of numerous staff members in the Demand the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast. Miguel Garcia

105

Wind Energy Technologies  

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

Wind energy technologies use the energy in wind for practical purposes such as generating electricity, charging batteries, pumping water, and grinding grain.

106

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height  

Science Conference Proceedings (OSTI)

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

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

2013-02-01T23:59:59.000Z

107

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

DOE Green Energy (OSTI)

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

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

108

Wind energy systems: program summary  

Science Conference Proceedings (OSTI)

The Federal Wind Energy Program (FWEP) was initiated to provide focus, direction and funds for the development of wind power. Each year a summary is prepared to provide the American public with an overview of government sponsored activities in the FWEP. This program summary describes each of the Department of Energy's (DOE) current wind energy projects initiated or renewed during FY 1979 (October 1, 1978 through September 30, 1979) and reflects their status as of April 30, 1980. The summary highlights on-going research, development and demonstration efforts and serves as a record of progress towards the program objectives. It also provides: the program's general management structure; review of last year's achievements; forecast of expected future trends; documentation of the projects conducted during FY 1979; and list of key wind energy publications.

None

1980-05-01T23:59:59.000Z

109

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

110

Annual Energy Outlook Forecast Evaluation-Table 1  

Annual Energy Outlook 2012 (EIA)

Annual Energy Outlook Forecast Evaluation > Table 1 Annual Energy Outlook Forecast Evaluation Table 1. Comparison of Absolute Percent Errors for AEO Forecast Evaluation, 1996 to...

111

German Wind Energy Association | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Association Place Osnabrck, Germany Zip 49074 Sector Wind energy Product Assocation for the promotion of wind energy in Germany. References German Wind Energy...

112

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

Alphabetically Tools by Platform PC Mac UNIX Internet Tools by Country Related Links Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides...

113

Wind energy | Open Energy Information  

Open Energy Info (EERE)

(Redirected from Wind) (Redirected from Wind) Jump to: navigation, search Wind energy is a form of solar energy.[1] Wind energy (or wind power) describes the process by which wind is used to generate electricity. Wind turbines convert the kinetic energy in the wind into mechanical power. A generator can convert mechanical power into electricity[2]. Mechanical power can also be utilized directly for specific tasks such as pumping water. The US DOE developed a short wind power animation that provides an overview of how a wind turbine works and describes the wind resources in the United States. Contents 1 Wind Energy Basics 1.1 Equation for Wind Power 2 DOE Wind Programs and Information 3 Worldwide Installed Capacity 3.1 United States Installed Capacity 4 Wind Farm Development 4.1 Land Requirements

114

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

DOE Green Energy (OSTI)

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

Porter, K.; Rogers, J.

2009-12-01T23:59:59.000Z

115

The Wind Energy Outlook Scenarios 1 India Wind Energy  

E-Print Network (OSTI)

1 ?Status of wind energy in India ????????????????????6 Wind energy in India????????????????????????????????????????????????????????????????????????????????????7 Wind power resource assessment?????????????????????????????????????????????????????????6 Wind power installations by state?????????????????????????????????????????????????????????8

unknown authors

2012-01-01T23:59:59.000Z

116

Wind energy bibliography  

DOE Green Energy (OSTI)

This bibliography is designed to help the reader search for information on wind energy. The bibliography is intended to help several audiences, including engineers and scientists who may be unfamiliar with a particular aspect of wind energy, university researchers who are interested in this field, manufacturers who want to learn more about specific wind topics, and librarians who provide information to their clients. Topics covered range from the history of wind energy use to advanced wind turbine design. References for wind energy economics, the wind energy resource, and environmental and institutional issues related to wind energy are also included.

None

1995-05-01T23:59:59.000Z

117

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

118

Energy Basics: Wind Power Animation  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Wind Wind Turbines Wind Resources Wind Power...

119

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

120

New Concepts in Wind Power Forecasting Models  

E-Print Network (OSTI)

of the motivations behind the project led by ANL ­ Argonne National Laboratory, together with INESC Porto from a manageable procedure to compute the solution. IV. ENTROPY AND PARZEN WINDOW PDF ESTIMATION The most well into the substation connecting it to the electric power network. Other model's input variables include forecasts

Kemner, Ken

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


121

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

E-Print Network (OSTI)

The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model.

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

122

Han Wind Energy Corporation | Open Energy Information  

Open Energy Info (EERE)

Han Wind Energy Corporation Jump to: navigation, search Name Han Wind Energy Corporation Place Beijing, Beijing Municipality, China Zip 100027 Sector Wind energy Product Han Wind...

123

Analysis and Forecasting of the Low-Level Wind during the Sydney 2000 Forecast Demonstration Project  

Science Conference Proceedings (OSTI)

During the Sydney 2000 Forecast Demonstration Project (FDP) a four-dimensional variational assimilation (4DVAR) scheme was run to analyze the low-level wind field with high spatial and temporal resolution. The 4DVAR scheme finds an optimal fit to ...

N. Andrew Crook; Juanzhen Sun

2004-02-01T23:59:59.000Z

124

Forecasting for energy and chemical decision analysis  

SciTech Connect

This paper focuses on uncertainty and bias in forecasts used for major energy and chemical investment decisions. Probability methods for characterizing uncertainty in the forecast are reviewed. Sources of forecasting bias are classified based on the results of relevant psychology research. Examples are drawn from the energy and chemical industry to illustrate the value of explicit characterization of uncertainty and reduction of bias in forecasts.

Cazalet, E.G.

1984-08-01T23:59:59.000Z

125

Comparison of post-processing methods for the calibration of 100 m wind ensemble forecasts at off- and onshore sites  

Science Conference Proceedings (OSTI)

Ensemble forecasts are a valuable addition to deterministic wind forecasts since they allow the quantification of forecast uncertainties. To remove common deficiencies of ensemble forecasts such as biases and ensemble spread deficits, various post-...

Constantin Junk; Lueder von Bremen; Martin Kühn; Stephan Späth; Detlev Heinemann

126

Empirical Solar Wind Forecasting from the Chromosphere  

E-Print Network (OSTI)

Recently, we correlated the inferred structure of the solar chromospheric plasma topography with solar wind velocity and composition data measured at 1AU. We now offer a physical justification of these relationships and present initial results of a empirical prediction model based on them. While still limited by the fundamentally complex physics behind the origins of the solar wind and how its structure develops in the magnetic photosphere and expands into the heliosphere, our model provides a near continuous range of solar wind speeds and composition quantities that are simply estimated from the inferred structure of the chromosphere. We suggest that the derived quantities may provide input to other, more sophisticated, prediction tools or models such as those to study Coronal Mass Ejections (CME) propagation and Solar Energetic Particle (SEP) generation.

Leamon, Robert J; 10.1086/511777

2009-01-01T23:59:59.000Z

127

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

128

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

129

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

130

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

131

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

E-Print Network (OSTI)

presents forecasts of energy supply, demand and pricesa reference case forecast with fossil fuel prices close toforecast for wind technologies. The AEO’s annual report of energy supply, demand, and prices

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

2001-01-01T23:59:59.000Z

132

Solar Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Jump to: navigation, search Name Solar Wind Place Krasnodar, Romania Zip 350000 Sector Solar, Wind energy Product Russia-based PV product manufacturer. Solar Wind manufactures...

133

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

DOE Green Energy (OSTI)

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

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

2012-09-01T23:59:59.000Z

134

Wind Energy Resources  

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

Wind energy can be produced anywhere in the world where the wind blows with a strong and consistent force. Windier locations produce more energy, which lowers the cost of producing electricity....

135

Wind Energy Benefits  

DOE Green Energy (OSTI)

Wind energy provides many benefits, including economic and environmental. This two-sided fact sheet succinctly outlines the top ten wind energy benefits and is especially well suited for general audiences.

Not Available

2005-04-01T23:59:59.000Z

136

Wind | Department of Energy  

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

Wind Wind Wind America is home to one of the largest and fastest growing wind markets in the world. Watch the video to learn more about the latest trends in the U.S. wind power market and join us this Thursday, August 8 at 3 pm ET for a Google+ Hangout on wind energy in America. The United States is home to one of the largest and fastest growing wind markets in the world. To stay competitive in this sector, the Energy Department invests in wind projects, both on land and offshore, to advance technology innovations, create job opportunities and boost economic growth. Moving forward, the U.S. wind industry remains a critical part of the Energy Department's all-of-the-above energy strategy to cut carbon pollution, diversify our energy economy and bring the next-generation of

137

Wind energy information guide  

DOE Green Energy (OSTI)

This book is divided into nine chapters. Chapters 1--8 provide background and annotated references on wind energy research, development, and commercialization. Chapter 9 lists additional sources of printed information and relevant organizations. Four indices provide alphabetical access to authors, organizations, computer models and design tools, and subjects. A list of abbreviations and acronyms is also included. Chapter topics include: introduction; economics of using wind energy; wind energy resources; wind turbine design, development, and testing; applications; environmental issues of wind power; institutional issues; and wind energy systems development.

NONE

1996-04-01T23:59:59.000Z

138

The Value of Wind Power Forecasting  

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

Preprint Debra Lew and Michael Milligan National Renewable Energy Laboratory Gary Jordan and Richard Piwko GE Energy Presented at the 91 st American Meteorological Society...

139

The wind power probability density forecast problem can be formulated as: forecast the wind power pdf at time step t for each look-ahead time step t+k of a given time-horizon  

E-Print Network (OSTI)

The wind power probability density forecast problem can be formulated as: forecast the wind power forecasted for look-ahead time t+k, xt is a set of explanatory variables available at time step t, fP,x is the joint density function of the forecasted wind power and explanatory variables, fX is the density

Kemner, Ken

140

Objective Forecasting of Foehn Winds for a Subgrid-Scale Alpine Valley  

Science Conference Proceedings (OSTI)

Foehn winds often depend on topographical features of a scale that is not sufficiently resolved in numerical models. Consequently, a successful foehn forecast has crucially depended on the experience of bench forecasters. This study provides a ...

Susanne Drechsel; Georg J. Mayr

2008-04-01T23:59:59.000Z

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


141

An Application of Model Output Statistics to the Development of a Local Wind Regime Forecast Procedure  

Science Conference Proceedings (OSTI)

The Model Output Statistics (MOS) approach is used to develop a procedure for forecasting the occurrence of a local wind regime at Rota, Spain known as the levante. Variables derived solely from surface pressure and 500 mb height forecast fields ...

Robert A. Godfrey

1982-12-01T23:59:59.000Z

142

Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)  

Science Conference Proceedings (OSTI)

This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.

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

2013-10-01T23:59:59.000Z

143

A Quick Guide to Wind Power Forecasting: State-of-the-Art 2009  

E-Print Network (OSTI)

challenges with regard to both power production and load balance in the electricity grid. This new source reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly other electricity sources, must be scheduled. Although wind power forecasting methods are used

Kemner, Ken

144

Wind Energy Technologies | Department of Energy  

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

Wind Energy Technologies Wind Energy Technologies August 15, 2013 - 4:10pm Addthis Photo of a hilly field, with six visible wind turbines spinning in the wind. Wind energy...

145

Energy Basics: Wind Energy Resources  

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

Resources Wind energy can be produced anywhere in the world where the wind blows with a strong and consistent force. Windier locations produce more energy, which lowers the cost of...

146

Foresight Wind Energy LLC | Open Energy Information  

Open Energy Info (EERE)

Foresight Wind Energy LLC Jump to: navigation, search Name Foresight Wind Energy LLC Place San Francisco, California Zip 94105 Sector Wind energy Product San Francisco-based...

147

Berrendo Wind Energy | Open Energy Information  

Open Energy Info (EERE)

Berrendo Wind Energy Jump to: navigation, search Name Berrendo Wind Energy Place Boulder, Colorado Zip 80304 Sector Wind energy Product Colorado-based firm developing utility scale...

148

Astraeus Wind Energy Inc | Open Energy Information  

Open Energy Info (EERE)

Astraeus Wind Energy Inc Jump to: navigation, search Name Astraeus Wind Energy Inc Place Eaton Rapids, Michigan Sector Wind energy Product Michigan-based manufacturer of large...

149

Wind energy information directory  

DOE Green Energy (OSTI)

Wind Energy Information has been prepared to provide researchers, designers, manufacturers, distributors, dealers, and users of wind energy conversion systems with easy access to technical information. This directory lists organizations and publications which have the main objective of promoting the use of wind energy conversion systems, some organizations that can respond to requests for information on wind energy or make referrals to other sources of information, and some publications that occasionally include information on wind energy. The bibliography contains references to information for both the neophyte and the expert.

None

1979-10-01T23:59:59.000Z

150

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

DOE Green Energy (OSTI)

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.

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.

2013-10-01T23:59:59.000Z

151

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

DOE Green Energy (OSTI)

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

Rogers, J.; Porter, K.

2011-03-01T23:59:59.000Z

152

CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Demand Forecast report is the product of the efforts of many current and former California Energy Commission staff. Staff contributors to the current forecast are: Project Management and Technical Direction

153

Energy Basics: Wind Turbines  

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

Photo of a crane lifting the blades onto a wind turbine that reads 'U.S. Department of Energy, NREL.' You can learn more about horizontal axis turbines from the EERE Wind Program's...

154

Upstream Measurements of Wind Profiles with Doppler Lidar for Improved Wind Energy Integration  

DOE Green Energy (OSTI)

New upstream measurements of wind profiles over the altitude range of wind turbines will be produced using a scanning Doppler lidar. These long range high quality measurements will provide improved wind power forecasts for wind energy integration into the power grid. The main goal of the project is to develop the optimal Doppler lidar operating parameters and data processing algorithms for improved wind energy integration by enhancing the wind power forecasts in the 30 to 60 minute time frame, especially for the large wind power ramps. Currently, there is very little upstream data at large wind farms, especially accurate wind profiles over the full height of the turbine blades. The potential of scanning Doppler lidar will be determined by rigorous computer modeling and evaluation of actual Doppler lidar data from the WindTracer system produced by Lockheed Martin Coherent Technologies, Inc. of Louisville, Colorado. Various data products will be investigated for input into numerical weather prediction models and statistically based nowcasting algorithms. Successful implementation of the proposed research will provide the required information for a full cost benefit analysis of the improved forecasts of wind power for energy integration as well as the added benefit of high quality wind and turbulence information for optimal control of the wind turbines at large wind farms.

Rodney Frehlich

2012-10-30T23:59:59.000Z

155

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

156

Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012  

DOE Green Energy (OSTI)

AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

Pennock, K.

2012-10-01T23:59:59.000Z

157

Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012  

SciTech Connect

AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

Pennock, K.

2012-10-01T23:59:59.000Z

158

Atmospheric Boundary-Layer Properties Affecting Wind Forecasting in Coastal Regions  

Science Conference Proceedings (OSTI)

Atmospheric boundary-layer properties and processes in gulf and coastal regions have special characteristics that are important in forecasting winds and ocean forcing. Accurate coastal-wind predictions require knowledge of local responses to a ...

Kenneth L. Davidson; Patricia J. Boyle; Peter S. Guest

1992-08-01T23:59:59.000Z

159

A Real-Time Hurricane Surface Wind Forecasting Model: Formulation and Verification  

Science Conference Proceedings (OSTI)

A real-time hurricane wind forecast model is developed by 1) incorporating an asymmetric effect into the Holland hurricane wind model; 2) using the National Oceanic and Atmospheric Administration (NOAA)/National Hurricane Center’s (NHC) hurricane ...

Lian Xie; Shaowu Bao; Leonard J. Pietrafesa; Kristen Foley; Montserrat Fuentes

2006-05-01T23:59:59.000Z

160

Wind Energy Permitting Standards | Department of Energy  

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

Wind Energy Permitting Standards Wind Energy Permitting Standards < Back Eligibility Commercial Construction Industrial InstallerContractor Savings Category Wind Buying & Making...

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


161

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

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

162

Chronological Reliability Model Incorporating Wind Forecasts to Assess Wind Plant Reserve Allocation: Preprint  

DOE Green Energy (OSTI)

Over the past several years, there has been considerable development and application of wind forecasting models. The main purpose of these models is to provide grid operators with the best information available so that conventional power generators can be scheduled as efficiently and as cost-effectively as possible. One of the important ancillary services is reserves, which involves scheduling additional capacity to guard against shortfalls. In a recent paper, Strbac and Kirschen[1] proposed a method to allocate the reserve burden to generators. Although Milligan adapted this technique to wind plants[2], neither of these papers accounts for the wind forecast in the reliability calculation. That omission is rectified here. For the system studied in this paper, we found that a reserve allocation scheme using 1-hour forecasts results in a small allocation of system reserve relative to the rated capacity of the wind power plant. This reserve allocation is even smaller when geographically dispersed wind sites are used instead of a large single site.

Milligan, M. R.

2002-05-01T23:59:59.000Z

163

High Winds Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Winds Wind Farm Winds Wind Farm Jump to: navigation, search Name High Winds Wind Farm Facility High Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser PPM Energy Inc Location Solano County CA Coordinates 38.124844°, -121.764915° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":38.124844,"lon":-121.764915,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

164

Comparison of 10-m Wind Forecasts from a Regional Area Model and QuikSCAT Scatterometer Wind Observations over the Mediterranean Sea  

Science Conference Proceedings (OSTI)

Surface wind forecasts from a limited-area model [the Quadrics Bologna Limited-Area Model (QBOLAM)] covering the entire Mediterranean area at 0.1° grid spacing are verified against Quick Scatterometer (QuikSCAT) wind observations. Only forecasts ...

Christophe Accadia; Stefano Zecchetto; Alfredo Lavagnini; Antonio Speranza

2007-05-01T23:59:59.000Z

165

Wind Energy Myths  

DOE Green Energy (OSTI)

This two-sided fact sheet succinctly outlines and counters the top misconceptions about wind energy. It is well suited for general audiences.

Not Available

2005-05-01T23:59:59.000Z

166

WindEnergyPEIS  

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

all or parts of the States of Iowa, Minnesota, Montana, Nebraska, North Dakota, and South Dakota. The draft PEIS assesses environmental impacts associated with wind energy...

167

Energy in the Wind  

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

Provi and BP Energy in the Wind - Exploring Basic Electrical Concepts by Modeling Wind Turbines Curriculum: Wind Power (simple machines, aerodynamics, weather/climatology, leverage, mechanics, atmospheric pressure, and energy resources/transformations) Grade Level: High School Small groups: 2 students Time: Introductory packet will take 2-3 periods. Scientific investigation will take 2-3 periods. (45-50 minute periods) Summary: Students explore basic electrical concepts. Students are introduced to electrical concepts by using a hand held generator utilizing a multimeter, modeling, and designing a wind turbine in a wind tunnel (modifications are given if a wind tunnel is not available). Students investigate how wind nergy is used as a renewable energy resource. e

168

Port Clair Wind Energy | Open Energy Information  

Open Energy Info (EERE)

Port Clair Wind Energy Jump to: navigation, search Name Port Clair Wind Energy Place United Kingdom Sector Wind energy Product Company setup to develop the 35MW Port Clair wind...

169

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

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

170

Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations  

Science Conference Proceedings (OSTI)

As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind ...

Barry E. Schwartz; Stanley G. Benjamin; Steven M. Green; Matthew R. Jardin

2000-06-01T23:59:59.000Z

171

Do Wind Forecasts Make Good Generation Schedules? Preprint  

SciTech Connect

Energy market scheduling conventions can needlessly increase the wind balancing area's regulation requirements. This economic inefficiency can be eliminated once it is recognized. The paper provides a detailed discussion of these issues.

Dragoon, K.; Kirby, B.; Milligan, M.

2008-06-01T23:59:59.000Z

172

Do Wind Forecasts Make Good Generation Schedules? Preprint  

DOE Green Energy (OSTI)

Energy market scheduling conventions can needlessly increase the wind balancing area's regulation requirements. This economic inefficiency can be eliminated once it is recognized. The paper provides a detailed discussion of these issues.

Dragoon, K.; Kirby, B.; Milligan, M.

2008-06-01T23:59:59.000Z

173

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

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

174

Wind energy conversion system  

DOE Patents (OSTI)

The wind energy conversion system includes a wind machine having a propeller connected to a generator of electric power, the propeller rotating the generator in response to force of an incident wind. The generator converts the power of the wind to electric power for use by an electric load. Circuitry for varying the duty factor of the generator output power is connected between the generator and the load to thereby alter a loading of the generator and the propeller by the electric load. Wind speed is sensed electro-optically to provide data of wind speed upwind of the propeller, to thereby permit tip speed ratio circuitry to operate the power control circuitry and thereby optimize the tip speed ratio by varying the loading of the propeller. Accordingly, the efficiency of the wind energy conversion system is maximized.

Longrigg, Paul (Golden, CO)

1987-01-01T23:59:59.000Z

175

Wind Energy Update  

Wind Powering America (EERE)

by the Alliance for Sustainable Energy, LLC. by the Alliance for Sustainable Energy, LLC. Wind Energy Update Wind Powering America January 2012 NATIONAL RENEWABLE ENERGY LABORATORY Evolution of Commercial Wind Technology NATIONAL RENEWABLE ENERGY LABORATORY Small (≤100 kW) Homes Farms Remote Applications (e.g. water pumping, telecom sites, icemaking) Midscale (100-1000 kW) Village Power Hybrid Systems Distributed Power Large, Land-based (1-3 MW) Utility-scale wind farms Large Distributed Power Sizes and Applications Large, Offshore (3-7 MW) Utility-scale wind farms, shallow coastal waters No U.S. installations NATIONAL RENEWABLE ENERGY LABORATORY Capacity & Cost Trends As of January 2012 (AWEA) 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 $- $200 $400 $600 $800 $1,000 $1,200

176

Wind | Department of Energy  

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

Wind Wind Wind EERE plays a key role in advancing America's "all of the above" energy strategy, leading a large network of researchers and other partners to deliver innovative technologies that will make renewable electricity generation cost-competitive with traditional sources of energy. EERE plays a key role in advancing America's "all of the above" energy strategy, leading a large network of researchers and other partners to deliver innovative technologies that will make renewable electricity generation cost-competitive with traditional sources of energy. Image of a wind turbine against a partly cloudy sky. The U.S. Department of Energy (DOE) leads national efforts to improve the performance, lower the costs, and accelerate the deployment of wind energy technologies-both on

177

Wind energy | Open Energy Information  

Open Energy Info (EERE)

energy in the wind into mechanical power. A generator can convert mechanical power into electricity2. Mechanical power can also be utilized directly for specific tasks such as...

178

Wind energy applications guide  

DOE Green Energy (OSTI)

The brochure is an introduction to various wind power applications for locations with underdeveloped transmission systems, from remote water pumping to village electrification. It includes an introductory section on wind energy, including wind power basics and system components and then provides examples of applications, including water pumping, stand-alone systems for home and business, systems for community centers, schools, and health clinics, and examples in the industrial area. There is also a page of contacts, plus two specific example applications for a wind-diesel system for a remote station in Antarctica and one on wind-diesel village electrification in Russia.

anon.

2001-01-01T23:59:59.000Z

179

Wind Energy 101.  

DOE Green Energy (OSTI)

This presentation on wind energy discusses: (1) current industry status; (2) turbine technologies; (3) assessment and siting; and (4) grid integration. There are no fundamental technical barriers to the integration of 20% wind energy into the nation's electrical system, but there needs to be a continuing evolution of transmission planning and system operation policy and market development for this to be most economically achieved.

Karlson, Benjamin; Orwig, Kirsten (NREL)

2010-12-01T23:59:59.000Z

180

Module Handbook Specialisation Wind Energy  

E-Print Network (OSTI)

of wind energy External costs Future price trends 3. Environmental Issues Environmental benefits of WT and Externalities Clculation methods Current plant costs Wind energy prices The value Module Handbook Specialisation Wind Energy 2nd Semester for the Master Programme

Habel, Annegret

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


181

Energy Basics: Wind Power Animation  

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

EERE: Energy Basics Wind Power Animation This animation discusses the advantages of wind power, the workings of a wind turbine, and wind resources in the United States. It also...

182

Wind Blog | Department of Energy  

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

Wind Blog Wind Blog RSS September 26, 2013 Wind Farm Brings Clean, Affordable Energy to Alaskan Cooperative How can we make it easier for more communities to use wind power?...

183

Diablo Winds Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Diablo Winds Wind Farm Diablo Winds Wind Farm Facility Diablo Winds Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser Pacific Gas & Electric Co Location Altamont Pass CA Coordinates 37.7347°, -121.652° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.7347,"lon":-121.652,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

184

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

185

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

186

Space-time forecasting and evaluation of wind speed with statistical tests for comparing accuracy of spatial predictions  

E-Print Network (OSTI)

High-quality short-term forecasts of wind speed are vital to making wind power a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, i.e., highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an off-site location. This work both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting at new locations. This is compared with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model's predictions. One method of evaluating time series forecasts, such as wind speed forecasts, is to test the null hypothesis of no difference in the accuracy of two competing sets of forecasts. Diebold and Mariano (1995) proposed a test in this setting that has been extended and widely applied. It allows the researcher to specify a wide variety of loss functions, and the forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. In this work, a similar unconditional test of forecast accuracy for spatial data is proposed. The forecast errors are no longer potentially serially correlated but spatially correlated. Simulations will illustrate the properties of this test, and an example with daily average wind speeds measured at over 100 locations in Oklahoma will demonstrate its use. This test is compared with a wavelet-based method introduced by Shen et al. (2002) in which the presence of a spatial signal at each location in the dataset is tested.

Hering, Amanda S.

2009-08-01T23:59:59.000Z

187

WindLogics Inc | Open Energy Information  

Open Energy Info (EERE)

Product WindLogics provides wind resource analysis and long-period variability forecasting services. References WindLogics Inc1 LinkedIn Connections CrunchBase Profile No...

188

Wind Energy Resources | Department of Energy  

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

determine whether the wind resource in a particular area is adequate for wind power. Addthis Related Articles Glossary of Energy Related Terms Hydropower Technologies Wind Turbines...

189

Indian Wind Energy Outlook 2009  

E-Print Network (OSTI)

1. ?The status of wind energy in India ? 4 Indian power sector?????????????????????????????????????????????????????????????????????????5 Renewable Energy in India ?????????????????????????????????????????????????????????????5 Wind potential???????????????????????????????????????????????????????????????????????????????? ? 7

unknown authors

2009-01-01T23:59:59.000Z

190

Mathematical and computer modelling reports: Modeling and forecasting energy markets with the intermediate future forecasting system  

Science Conference Proceedings (OSTI)

This paper describes the Intermediate Future Forecasting System (IFFS), which is the model used to forecast integrated energy markets by the U.S. Energy Information Administration. The model contains representations of supply and demand for all of the ...

Frederic H. Murphy; John J. Conti; Susan H. Shaw; Reginald Sanders

1989-09-01T23:59:59.000Z

191

Great Plains Wind Energy Transmission Development Project  

DOE Green Energy (OSTI)

In fiscal year 2005, the Energy & Environmental Research Center (EERC) received funding from the U.S. Department of Energy (DOE) to undertake a broad array of tasks to either directly or indirectly address the barriers that faced much of the Great Plains states and their efforts to produce and transmit wind energy at the time. This program, entitled Great Plains Wind Energy Transmission Development Project, was focused on the central goal of stimulating wind energy development through expansion of new transmission capacity or development of new wind energy capacity through alternative market development. The original task structure was as follows: Task 1 - Regional Renewable Credit Tracking System (later rescoped to Small Wind Turbine Training Center); Task 2 - Multistate Transmission Collaborative; Task 3 - Wind Energy Forecasting System; and Task 4 - Analysis of the Long-Term Role of Hydrogen in the Region. As carried out, Task 1 involved the creation of the Small Wind Turbine Training Center (SWTTC). The SWTTC, located Grand Forks, North Dakota, consists of a single wind turbine, the Endurance S-250, on a 105-foot tilt-up guyed tower. The S-250 is connected to the electrical grid on the 'load side' of the electric meter, and the power produced by the wind turbine is consumed locally on the property. Establishment of the SWTTC will allow EERC personnel to provide educational opportunities to a wide range of participants, including grade school through college-level students and the general public. In addition, the facility will allow the EERC to provide technical training workshops related to the installation, operation, and maintenance of small wind turbines. In addition, under Task 1, the EERC hosted two small wind turbine workshops on May 18, 2010, and March 8, 2011, at the EERC in Grand Forks, North Dakota. Task 2 involved the EERC cosponsoring and aiding in the planning of three transmission workshops in the midwest and western regions. Under Task 3, the EERC, in collaboration with Meridian Environmental Services, developed and demonstrated the efficacy of a wind energy forecasting system for use in scheduling energy output from wind farms for a regional electrical generation and transmission utility. With the increased interest at the time of project award in the production of hydrogen as a critical future energy source, many viewed hydrogen produced from wind-generated electricity as an attractive option. In addition, many of the hydrogen production-related concepts involve utilization of energy resources without the need for additional electrical transmission. For this reason, under Task 4, the EERC provided a summary of end uses for hydrogen in the region and focused on one end product in particular (fertilizer), including several process options and related economic analyses.

Brad G. Stevens, P.E.; Troy K. Simonsen; Kerryanne M. Leroux

2012-06-09T23:59:59.000Z

192

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

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

193

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint  

DOE Green Energy (OSTI)

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01T23:59:59.000Z

194

Cambrian Wind Energy | Open Energy Information  

Open Energy Info (EERE)

London, Greater London, United Kingdom Zip W1U 6RP Sector Renewable Energy, Wind energy Product UK wind energy company acquired by Falck Renewables Ltd, the wind energy subsidiary...

195

Wind Energy Technologies Available for Licensing - Energy ...  

Wind Energy Technologies Available for Licensing U.S. Department of Energy (DOE) laboratories and participating research institutions have wind energy ...

196

EIA Energy Kids - Wind - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Wind is a clean source of energy, and overall, the use of wind for energy has fewer environmental impacts than using many other energy sources.

197

Wind Energy Technologies - Energy Innovation Portal  

Wind Energy Technology Marketing Summaries Here you’ll find marketing summaries of wind energy technologies available for licensing from U.S. Department of Energy ...

198

Wind Vision Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Wind Farm Wind Farm Jump to: navigation, search Name Wind Vision Wind Farm Facility Wind Vision Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Wind Vision Developer Wind Vision Location St. Ansgar IA Coordinates 43.348224°, -92.888816° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.348224,"lon":-92.888816,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

199

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

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

200

Evaluation of Wave Forecasts Consistent with Tropical Cyclone Warning Center Wind Forecasts  

Science Conference Proceedings (OSTI)

An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in near–real time to forecasters since summer 2007. The algorithm removes the tropical cyclone from ...

Charles R. Sampson; Paul A. Wittmann; Efren A. Serra; Hendrik L. Tolman; Jessica Schauer; Timothy Marchok

2013-02-01T23:59:59.000Z

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


201

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY AND TRANSPORTATION DIVISION B.B. Blevins Executive Director DISCLAIMER This report was prepared by a California has developed longterm forecasts of transportation energy demand as well as projected ranges

202

1) INTRODUCTION The accuracy of short-term wind power forecasts is besides  

E-Print Network (OSTI)

unconsidered outages of single turbines reflect a higher forecast error than expected from NWP. Wind power. The wind farm was in the commissioning phase in early 2001, when gradually more and more turbines became due to turbine wakes in the wind park and vi) accounting the availability of turbines with respect

Heinemann, Detlev

203

Price and Load Forecasting in Volatile Energy Markets  

Science Conference Proceedings (OSTI)

With daily news stories about wildly fluctuating electricity prices and soaring natural gas prices, forecasters' responsibilities are expanding, visibility is increasing, and pressure exists to produce more frequent forecasts and more kinds of forecasts. The proceedings of EPRI's 13th Forecasting Symposium, held November 13-15 in Nashville, Tennessee, address current forecasting issues and developments, as well as explain the role that forecasters have played in recent events in energy markets.

2001-12-05T23:59:59.000Z

204

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff members in the Demand prepared the residential sector forecast. Mohsen Abrishami prepared the commercial sector forecast. Lynn

205

A SENSITIVITY ANALYSIS OF THE TREATMENT OF WIND ENERGY IN THE AEO99 VERSION OF NEMS  

E-Print Network (OSTI)

LBNL-44070 TP-28529 A SENSITIVITY ANALYSIS OF THE TREATMENT OF WIND ENERGY IN THE AEO99 VERSION and market penetration on the U.S. Department of Energy's Annual Energy Outlook (AEO) forecast for wind supply mix remains fairly steady, and renewable energy technologies such as wind do not achieve

206

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty  

Science Conference Proceedings (OSTI)

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2013-07-25T23:59:59.000Z

207

Energy Basics: Wind Power Animation (Text Version)  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Wind Wind Turbines Wind Resources Wind Power...

208

Blyth Offshore Wind Ltd | Open Energy Information  

Open Energy Info (EERE)

Blyth Offshore Wind Ltd Jump to: navigation, search Name Blyth Offshore Wind Ltd Place United Kingdom Sector Renewable Energy, Wind energy Product Blyth Offshore Wind Limited,...

209

Wind Energy and Spatial Technology  

E-Print Network (OSTI)

2/3/2011 1 Wind Energy and Spatial Technology Lori Pelech Why Wind Energy? A clean, renewable 2,600 tons of carbon emissions annually ­ The economy · Approximately 85,000 wind energy workers (existing transmission lines)? #12;2/3/2011 3 US Energy Transmission Grid US Wind Map #12;2/3/2011 4

Schweik, Charles M.

210

Wind Energy Resource Basics | Department of Energy  

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

Energy Resource Basics Wind Energy Resource Basics July 30, 2013 - 3:11pm Addthis Wind energy can be produced anywhere in the world where the wind blows with a strong and...

211

An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study  

SciTech Connect

This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

2011-01-17T23:59:59.000Z

212

Wind characteristics for agricultural wind energy applications  

SciTech Connect

Wind energy utilization in agriculture can provide a potentially significant savings in fuel oil consumption and ultimately a cost savings to the farmer. A knowledge of the wind characteristics within a region and at a location can contribute greatly to a more efficient and cost-effective use of this resource. Current research indicates that the important wind characteristics include mean annual wind speed and the frequency distribution of the wind, seasonal and diurnal variations in wind speed and direction, and the turbulent and gustiness characteristics of the wind. Further research is underway to provide a better definition of the total wind resource available, improved methods for siting WECS and an improved understanding of the environment to which the WECS respond.

Renne, D. S.

1979-01-01T23:59:59.000Z

213

Wind Energy Teachers Guide  

DOE Green Energy (OSTI)

This guide, created by the American Wind Association, with support from the U.S. Department of Energy, is a learning tool about wind energy targeted toward grades K-12. The guide provides teacher information, ideas for sparking children's and students' interest, suggestions for activities to undertake in and outside the classroom, and research tools for both teachers and students. Also included is an additional resources section.

anon.

2003-01-01T23:59:59.000Z

214

Wind Power | Open Energy Information  

Open Energy Info (EERE)

Wind Power Wind Power Jump to: navigation, search Wind Power WIndfarm.Sunset.jpg Wind power is a form of solar energy.[1] Wind is caused by the uneven heating of the atmosphere by the sun, variations in the earth's surface, and rotation of the earth. Mountains, bodies of water, and vegetation all influence wind flow patterns[2], [3]. Wind energy (or wind power) describes the process by which wind is used to generate electricity. Wind turbines convert the energy in wind to electricity by rotating propeller-like blades around a rotor. The rotor turns the drive shaft, which turns an electric generator.[2] Three key factors affect the amount of energy a turbine can harness from the wind: wind speed, air density, and swept area.[4] Mechanical power can also be utilized directly for specific tasks such as

215

Lake Country Wind Energy LLC | Open Energy Information  

Open Energy Info (EERE)

Country Wind Energy LLC Jump to: navigation, search Name Lake Country Wind Energy LLC Place Minnesota Zip 56209 Sector Renewable Energy, Wind energy Product Minnesota-based wind...

216

West Winds Wind Farm | Open Energy Information  

Open Energy Info (EERE)

West Winds Wind Farm West Winds Wind Farm Facility West Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Caithness Developer SeaWest Energy Purchaser Southern California Edison/PacifiCorp Location San Gorgonio CA Coordinates 33.9095°, -116.734° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":33.9095,"lon":-116.734,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

217

Wind energy, offers considerable promise: the wind itself is free,  

E-Print Network (OSTI)

Wind energy, offers considerable promise: the wind itself is free, wind power is clean. One of these sources, wind energy, offers considerable promise: the wind itself is free, wind power is clean, and it is virtually inexhaustible. In recent years, research on wind energy has accelerated

Langendoen, Koen

218

Wind Energy Technologies - Energy Innovation Portal  

Wind Energy Technology Marketing Summaries Here you’ll find marketing summaries of wind energy technologies available for licensing from U.S. ...

219

China Wind Energy Association | Open Energy Information  

Open Energy Info (EERE)

China Wind Energy Association Place Beijing, Beijing Municipality, China Zip 100013 Sector Wind energy Product A non-profit industrial association devoted to promote the...

220

Cisco Wind Energy Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Cisco Wind Energy Wind Farm Cisco Wind Energy Wind Farm Jump to: navigation, search Name Cisco Wind Energy Wind Farm Facility Cisco Wind Energy Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner John Deere Wind Energy Developer Community Energy Purchaser Northern States Power Location Brewster MN Coordinates 43.696164°, -95.467078° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.696164,"lon":-95.467078,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

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


221

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

222

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

Gasoline and Diesel Fuel Update (EIA)

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

223

Forecasting the Impacts of Strong Wintertime Post-Cold Front Winds in the Northern Plains  

Science Conference Proceedings (OSTI)

Strong post-cold front wind events in the northern plains of the United States are a difficult problem for operational forecasters. The various atmospheric ingredients that lead to these events are examined from an operational point of view. ...

Anton F. Kapela; Preston W. Leftwich; Richard Van Ess

1995-06-01T23:59:59.000Z

224

Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics: Preprint  

DOE Green Energy (OSTI)

In this paper, we examine the parameters associated with the calculation of the Renyi entropy in order to further the understanding of its application to assessing wind power forecasting errors.

Hodge, B. M.; Orwig, K.; Milligan, M.

2012-06-01T23:59:59.000Z

225

Impact of Kalpana-1-Derived Water Vapor Winds on Indian Ocean Tropical Cyclone Forecasts  

Science Conference Proceedings (OSTI)

The water vapor winds from the operational geostationary Indian National Satellite (INSAT) Kalpana-1 have recently become operational at the Space Applications Centre (SAC). A series of experimental forecasts are attempted here to evaluate the ...

S. K. Deb; C. M. Kishtawal; P. K. Pal

2010-03-01T23:59:59.000Z

226

Analyzing and Forecasting Rocky Mountain Lee Cyclogenesis Often Associated with Strong Winds  

Science Conference Proceedings (OSTI)

Since numerical forecast models often err in predicting the timing and location of lee cyclogenesis, a physically based method to diagnose such errors is sought. A case of Rocky Mountain lee cyclogenesis associated with strong winds is examined ...

David M. Schultz; Charles A. Doswell III

2000-04-01T23:59:59.000Z

227

Evaluation of the National Hurricane Center’s Tropical Cyclone Wind Speed Probability Forecast Product  

Science Conference Proceedings (OSTI)

A tropical cyclone (TC) wind speed probability forecast product developed at the Cooperative Institute for Research in the Atmosphere (CIRA) and adopted by the National Hurricane Center (NHC) is evaluated for U.S. land-threatening and landfalling ...

Michael E. Splitt; Jaclyn A. Shafer; Steven M. Lazarus; William P. Roeder

2010-04-01T23:59:59.000Z

228

Nettuno: Analysis of a Wind and Wave Forecast System in the Mediterranean Sea  

Science Conference Proceedings (OSTI)

Nettuno is a wind and wave forecast system in the Mediterranean Sea. It has been operational since 2009 producing twice a day high resolution forecasts for the next 72 hours. We have carried out a detailed analysis of the results, both in space ...

Luciana Bertotti; Luigi Cavaleri; Layla Loffredo; Lucio Torrisi

229

Wind World | Open Energy Information  

Open Energy Info (EERE)

World Jump to: navigation, search Name Wind World Place Denmark Sector Wind energy Product WindWorld was a turbine manufacturer that was purchased by NEG Micon in 1998. NEG Micon...

230

Wind Turbines | Department of Energy  

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

Turbines Wind Turbines July 30, 2013 - 2:58pm Addthis Energy 101: Wind Turbines Basics This video explains the basics of how wind turbines operate to produce clean power from an...

231

Minster Wind | Open Energy Information  

Open Energy Info (EERE)

Minster Wind Jump to: navigation, search Name Minster Wind Address 240 W. Fifth St Place Minster, Ohio Zip 45865 Sector Services, Wind energy Website http:www.minster.comwindwi...

232

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022  

E-Print Network (OSTI)

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA or adequacy of the information in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast forecast. Bryan Alcorn and Mehrzad Soltani Nia prepared the industrial forecast. Miguel Garcia- Cerrutti

233

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty  

SciTech Connect

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2013-12-18T23:59:59.000Z

234

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

SciTech Connect

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

Martin Wilde, Principal Investigator

2012-12-31T23:59:59.000Z

235

Using Wind Anomalies to Forecast East Coast Winter Storms  

Science Conference Proceedings (OSTI)

Forecasting major winter storms is a critical function for all weather services. Conventional model-derived fields from numerical weather prediction models most frequently utilized by operational forecasters, such as pressure level geopotential ...

Neil A. Stuart; Richard H. Grumm

2006-12-01T23:59:59.000Z

236

Emergency Response Transport Forecasting Using Historical Wind Field Pattern Matching  

Science Conference Proceedings (OSTI)

Historical pattern matching, or analog forecasting, is used to generate short-term mesoscale transport forecasts for emergency response at the Idaho National Engineering and Environmental Laboratory. A simple historical pattern-matching algorithm ...

Roger G. Carter; Robert E. Keislar

2000-03-01T23:59:59.000Z

237

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

238

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect

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

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

2009-03-01T23:59:59.000Z

239

Commercial Wind Energy Property Valuation | Department of Energy  

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

Commercial Wind Energy Property Valuation Commercial Wind Energy Property Valuation < Back Eligibility Commercial Industrial Utility Savings Category Wind Buying & Making...

240

Wind Report | Department of Energy  

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

Wind Report Wind Report Wind Report Watch as our clean energy experts answer your questions about the U.S. wind industry -- one of the largest and fastest growing wind markets in the world. Related Links Top 8 Things You Didn't Know About Distributed Wind Small-Scale Distributed Wind: Northern Power Systems 100 kW turbine at the top of Burke Mountain in East Burke, Vermont. | Photo courtesy of Northern Power Systems. Test your energy knowledge by learning interesting facts about distributed wind. Charting the Future of Energy Storage As we continue to incorporate more renewable energy into the grid, technologies that store energy like batteries will be key to providing a continuous flow of clean energy even when the wind isn't blowing and the sun doesn't shine. Wind Industry Soars to New Heights

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


241

Tribal Renewable Energy Curriculum Foundational Course: Wind...  

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

Wind Tribal Renewable Energy Curriculum Foundational Course: Wind Watch the DOE Office of Indian Energy foundational course webinar on wind renewable energy by clicking on the .swf...

242

Wave Wind LLC | Open Energy Information  

Open Energy Info (EERE)

Wave Wind LLC Place Sun Prairie, Wisconsin Zip 53590 Sector Services, Wind energy Product Wisconsin-based wind developer and construction services provider. References Wave Wind...

243

Heilongjiang Lishu Wind Power | Open Energy Information  

Open Energy Info (EERE)

Lishu Wind Power Jump to: navigation, search Name Heilongjiang Lishu Wind Power Place Heilongjiang Province, China Sector Wind energy Product China-based wind project developer...

244

Crownbutte Wind Power LLC | Open Energy Information  

Open Energy Info (EERE)

Crownbutte Wind Power LLC Jump to: navigation, search Name Crownbutte Wind Power LLC Place Mandan, North Dakota Zip 58554 Sector Wind energy Product North Dakota wind power company...

245

Daqing Longjiang Wind Power | Open Energy Information  

Open Energy Info (EERE)

Longjiang Wind Power Jump to: navigation, search Name Daqing Longjiang Wind Power Place Daqing, Heilongjiang Province, China Zip 163316 Sector Wind energy Product Local wind...

246

Gansu Xinhui Wind Power | Open Energy Information  

Open Energy Info (EERE)

Xinhui Wind Power Jump to: navigation, search Name Gansu Xinhui Wind Power Place China Sector Wind energy Product China-based joint venture engaged in developing wind projects....

247

Short-Term Wind Generation Forecasting Using Artificial Neural Networks  

Science Conference Proceedings (OSTI)

Wind power is a highly intermittent power output resource that cannot be bid competitively in a traditional market due to scheduling problems associated with the resource. The California Independent System Operator (CAISO) has proposed a unique market arrangement that makes such bidding possible. The central part of the arrangement is a provision that deviations between metered and scheduled energy for participating intermittent renewable resources will be averaged across a calendar month, and paid or ch...

2003-10-27T23:59:59.000Z

248

Wind Powering America: Wind Energy Videos  

DOE Data Explorer (OSTI)

Wind Powering America is a nationwide initiative designed to increase the use of wind energy across the United States by working with regional stakeholders. A list of videos developed by and for the program includes interviews, short news clips, and documentary-like programs.

249

AEP Wind Energy LLC | Open Energy Information  

Open Energy Info (EERE)

Wind Energy LLC Wind Energy LLC Jump to: navigation, search Name AEP Wind Energy LLC Place Dallas, Texas Zip 75266 1064 Sector Wind energy Product AEP Wind Energy LLC is a project developer in the wind industry. It is an affiliate of American Electric Power. References AEP Wind Energy LLC[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. AEP Wind Energy LLC is a company located in Dallas, Texas . References ↑ "AEP Wind Energy LLC" Retrieved from "http://en.openei.org/w/index.php?title=AEP_Wind_Energy_LLC&oldid=341822" Categories: Clean Energy Organizations Companies Organizations Stubs What links here Related changes Special pages Printable version Permanent link

250

Improving High-Resolution Model Forecasts of Downslope Winds in the Las Vegas Valley  

Science Conference Proceedings (OSTI)

Numerical simulations for severe downslope winds as well as trapped lee waves in Nevada’s Las Vegas Valley were performed in this study. The goal of this study was to improve model forecasts of downslope-wind-event intensities. This was measured ...

Andre K. Pattantyus; Sen Chiao; Stanley Czyzyk

2011-06-01T23:59:59.000Z

251

Utilization of Automatic Weather Station Data for Forecasting High Wind Speeds at Pegasus Runway, Antarctica  

Science Conference Proceedings (OSTI)

Reduced visibility due to blowing snow can severely hinder aircraft operations in the Antarctic. Wind speeds in excess of approximately 7–13 m s?1 can result in blowing snow. The ability to forecast high wind speed events can improve the safety ...

R. E. Holmes; C. R. Stearns; G. A. Weidner; L. M. Keller

2000-04-01T23:59:59.000Z

252

Scanning Doppler Lidar for Input into Short-Term Wind Power Forecasts  

Science Conference Proceedings (OSTI)

Scanning Doppler lidar is a promising technology for improvements in short-term wind power forecasts since it can scan close to the surface and produce wind profiles at a large distance upstream (15–30 km) if the atmosphere has sufficient aerosol ...

Rod Frehlich

2013-02-01T23:59:59.000Z

253

Wind Energy Technology Basics | Department of Energy  

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

Wind Energy Technology Basics Wind Energy Technology Basics Wind Energy Technology Basics August 15, 2013 - 4:10pm Addthis Photo of a hilly field, with six visible wind turbines spinning in the wind. Wind energy technologies use the energy in wind for practical purposes such as generating electricity, charging batteries, pumping water, and grinding grain. Most wind energy technologies can be used as stand-alone applications, connected to a utility power grid, or even combined with a photovoltaic system. For utility-scale sources of wind energy, a large number of turbines are usually built close together to form a wind farm that provides grid power. Several electricity providers use wind farms to supply power to their customers. Stand-alone turbines are typically used for water pumping or

254

Wind Energy Systems Exemption | Department of Energy  

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

Systems Exemption Wind Energy Systems Exemption Eligibility Commercial Industrial Utility Savings For Wind Buying & Making Electricity Maximum Rebate None Program Information Start...

255

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

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

256

Definition: Wind energy | Open Energy Information  

Open Energy Info (EERE)

Wikipedia Wikipedia Definition Related Terms Wind turbine, Solar energy, power, energy, electricity generation, turbine References http:www.eia.govkids...

257

Wind Energy Sales Tax Exemption  

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

Wind-energy conversion systems used as electric-power sources are exempt from Minnesota's sales tax. Materials used to manufacture, install, construct, repair or replace wind-energy systems also...

258

Wind Energy In America: Ventower Industries | Department of Energy  

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

Wind Manufacturing Saving Energy and Resources Revolutionizing Manufacturing INFOGRAPHIC: Wind Energy in America National Wind Technology Center - Colorado America's Wind Testing...

259

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-Print Network (OSTI)

in this report. #12;i ABSTRACT These electricity demand forms and instructions ask load-serving entities and Instructions for Electricity Demand Forecasts. California Energy Commission, Electricity Supply Analysis.................................................................................................................................7 Form 1 Historic and Forecast Electricity Demand

Abdel-Aal, Radwan E.

260

2008 Wind Energy Projects, Wind Powering America (Poster)  

SciTech Connect

The Wind Powering America program produces a poster at the end of every calendar year that depicts new U.S. wind energy projects. The 2008 poster includes the following projects: Stetson Wind Farm in Maine; Dutch Hill Wind Farm in New York; Grand Ridge Wind Energy Center in Illinois; Hooper Bay, Alaska; Forestburg, South Dakota; Elbow Creek Wind Project in Texas; Glacier Wind Farm in Montana; Wray, Colorado; Smoky Hills Wind Farm in Kansas; Forbes Park Wind Project in Massachusetts; Spanish Fork, Utah; Goodland Wind Farm in Indiana; and the Tatanka Wind Energy Project on the border of North Dakota and South Dakota.

2009-01-01T23:59:59.000Z

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


261

Wind Energy Information Guide 2004  

DOE Green Energy (OSTI)

The guide provides a list of contact information and Web site addresses for resources that provide a range of general and technical information about wind energy, including general information, wind and renewable energy, university programs and research institutes, international wind energy associations and others.

anon.

2004-01-01T23:59:59.000Z

262

Experiments with Wind to Produce Energy  

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

Nat EXPERIMENTS WITH WIND TO PRODUCE ENERGY Curriculum: Wind Power (simple machines, weatherclimatology, aerodynamics, leverage, mechanics, atmospheric pressure, and energy...

263

Annual Energy Outlook 1998 Forecasts  

Gasoline and Diesel Fuel Update (EIA)

EIA Administrator's Press Briefing on the Annual Energy Outlook 1998 (AEO98) Annual Energy Outlook 1998 - Errata as of 3698 Data from the AEO98 Assumptions to the AEO98 (Nat'Gas...

264

Offshore Wind Accelerator | Open Energy Information  

Open Energy Info (EERE)

Sector Wind energy Product Research and development initiative aimed at cutting the cost of offshore wind energy. References Offshore Wind Accelerator1 LinkedIn Connections...

265

Beaufort Wind Ltd | Open Energy Information  

Open Energy Info (EERE)

Kingdom Sector Renewable Energy, Wind energy Product UK-based operator of a portfolio of wind farms that were originally developed by npower renewables. References Beaufort Wind...

266

Cowal Wind Energy Ltd | Open Energy Information  

Open Energy Info (EERE)

Cowal Wind Energy Ltd Cowal Wind Energy Ltd Jump to: navigation, search Name Cowal Wind Energy Ltd Place Flintshire, Wales, United Kingdom Zip CH7 4EW Sector Wind energy Product Wind Farm developer with its office in north Wales. References Cowal Wind Energy Ltd[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Cowal Wind Energy Ltd is a company located in Flintshire, Wales, United Kingdom . References ↑ "Cowal Wind Energy Ltd" Retrieved from "http://en.openei.org/w/index.php?title=Cowal_Wind_Energy_Ltd&oldid=343949" Categories: Clean Energy Organizations Companies Organizations Stubs What links here Related changes Special pages Printable version Permanent link Browse properties

267

Energy 101: Wind Turbines | Department of Energy  

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

Wind Turbines Wind Turbines Energy 101: Wind Turbines Addthis Description See how wind turbines generate clean electricity from the power of the wind. Highlighted are the various parts and mechanisms of a modern wind turbine. Duration 2:16 Topic Tax Credits, Rebates, Savings Wind Energy Economy Credit Energy Department Video MR. : We've all seen those creaky old windmills on farms, and although they may seem about as low-tech as you can get, those old windmills are the predecessors for new modern wind turbines that generate electricity. The same wind that used to pump water for cattle is now turning giant wind turbines to power cities and homes. OK, have a look at this wind farm in the California desert, a hot desert next to tall mountains - an ideal place for a lot of wind.

268

Prairie Winds Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Prairie Winds Wind Farm Prairie Winds Wind Farm Facility Prairie Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Basin Electric Power Coop/Central Power Electric Coop Developer Basin Electric Power Coop/Central Power Electric Coop Energy Purchaser Basin Electric Power Coop/Central Power Electric Coop Location Near Minot ND Coordinates 48.022927°, -101.291435° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":48.022927,"lon":-101.291435,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

269

NREL: Wind Research - Xcel Energy Small Wind Funding Available...  

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

Xcel Energy Small Wind Funding Available in Minnesota, Wisconsin February 25, 2013 Xcel Energy is releasing a new round of funding through a request for proposals. Small wind...

270

Alta Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Alta Wind Energy Center Alta Wind Energy Center Address 10315 Oak Creek Road Place Mojave, California Zip 93501 Sector Wind energy Phone number 1-877-4WI-ND88 (1-877-494-6388) Website http://altawindenergycenter.co Region Southern CA Area References Alta Wind Energy Center[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! The Alta Wind Energy Center (AWEC) is located in the heart of one of the most proven wind resources in the United States - the Tehachapi-Mojave Wind Resource Area. Terra-Gen is developing the AWEC, California's largest wind energy project, adjacent to existing wind projects between the towns of Mojave and Tehachapi. Due to a welcoming community and the participation of a diverse group of landowners (private and public, local and non-local,

271

Coupling Renewable Energy Supply with Deferrable Demand  

E-Print Network (OSTI)

forecasting for wind energy: Temperature dependence andlarge amounts of wind energy with a small electric system.Large scale integration of wind energy in the european power

Papavasiliou, Anthony

2011-01-01T23:59:59.000Z

272

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

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

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

273

Wind Blog | Department of Energy  

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

August 6, 2013 August 6, 2013 Our latest Infographic highlights key findings from the 2012 Wind Technologies Market Report. | Infographic by Sarah Gerrity. America's Wind Industry Reaches Record Highs Sharing key findings from two new Energy Department reports that highlight the record growth of America's wind industry. August 5, 2013 Wind Industry Soars to New Heights Watch the video as Jose Zayas, Director of the Wind and Water Power Technologies Office, highlights the latest wind industry trends in the 2012 Wind Technologies Market Report. August 16, 2012 Wind Energy In America: Supporting Our Manufacturers Profiling success stories of the American wind industry. August 14, 2012 A Banner Year for the U.S. Wind Industry

274

Wind Easements | Department of Energy  

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

Wind Easements Wind Easements Wind Easements < Back Eligibility Agricultural Fed. Government Institutional Local Government Nonprofit Residential Schools State Government Tribal Government Savings Category Wind Buying & Making Electricity Program Info State North Dakota Program Type Solar/Wind Access Policy North Dakota allows property owners to grant an easement that ensures adequate exposure of a wind-energy system to the wind. The easement runs with the land benefited and burdened, and terminates upon the conditions stated in the easement. The statutes authorizing the creation of wind easements include several provisions to protect property owners. For example, a wind easement may not make the property owner liable for any property tax associated with the wind-energy system or other equipment

275

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

276

Wind energy: Program overview, FY 1992  

DOE Green Energy (OSTI)

The DOE Wind Energy Program assists utilities and industry in developing advanced wind turbine technology to be economically competitive as an energy source in the marketplace and in developing new markets and applications for wind systems. This program overview describes the commercial development of wind power, wind turbine development, utility programs, industry programs, wind resources, applied research in wind energy, and the program structure.

Not Available

1993-06-01T23:59:59.000Z

277

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

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

278

Energy 101: Wind Turbines | Department of Energy  

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

Wind Turbines Wind Turbines Energy 101: Wind Turbines Addthis Below is the text version for the Energy 101: Wind Turbines video. The video opens with "Energy 101: Wind Turbines." This is followed by wooden windmills on farms. We've all seen those creaky, old windmills on farms. And although they may seem about as low-tech as you can get, those old windmills are the predecessors for new, modern wind turbines that generat electricity. The video pans through shots of large windmills and wind farms of different sizes, situated on cultivated plains and hills. The same wind that used to pump water for cattle is now turning giant wind turbines to power cities and homes. OK, have a look at this wind farm in the California desert. A hot desert, next to tall mountains. An ideal place for a lot of wind.

279

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks  

E-Print Network (OSTI)

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Stefan Achleitner, Tao Liu in power output is a major concern and forecasting is, therefore, a top priority. We propose a sensing infrastructure to enable sensing of solar irradiance with application to solar array output forecasting

Cerpa, Alberto E.

280

The Solar Wind Energy Flux  

E-Print Network (OSTI)

The solar-wind energy flux measured near the ecliptic is known to be independent of the solar-wind speed. Using plasma data from Helios, Ulysses, and Wind covering a large range of latitudes and time, we show that the solar-wind energy flux is independent of the solar-wind speed and latitude within 10%, and that this quantity varies weakly over the solar cycle. In other words the energy flux appears as a global solar constant. We also show that the very high speed solar-wind (VSW > 700 km/s) has the same mean energy flux as the slower wind (VSW solar-wind speed and density, which formalizes the anti-correlation between these quantities.

Chat, G Le; Meyer-Vernet, N

2012-01-01T23:59:59.000Z

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


281

20% Wind Energy by 2030  

DOE Green Energy (OSTI)

This analysis explores one clearly defined scenario for providing 20% of our nations electricity demand with wind energy by 2030 and contrasts it to a scenario of no new wind power capacity.

Not Available

2008-07-01T23:59:59.000Z

282

Idaho Winds LLC | Open Energy Information  

Open Energy Info (EERE)

Idaho Winds, LLC Place Idaho Sector Wind energy Product Wholly-owned subsidiary of PowerworksPacific Winds, operating wind farms in Idaho. References Idaho Winds, LLC1 LinkedIn...

283

Does Increased Horizontal Resolution Improve Hurricane Wind Forecasts?  

Science Conference Proceedings (OSTI)

The representation of tropical cyclone track, intensity, and structure in a set of 69 parallel forecasts performed at each of two horizontal grid increments with the Advanced Research Hurricane (AHW) component of the Weather and Research and ...

Christopher Davis; Wei Wang; Jimy Dudhia; Ryan Torn

2010-12-01T23:59:59.000Z

284

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

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

285

Mountain Wind | Open Energy Information  

Open Energy Info (EERE)

Mountain Wind Mountain Wind Jump to: navigation, search Mountain Wind is a wind farm located in Uinta County, Wyoming. It consists of 67 turbines and has a total capacity of 140.7 MW. It is owned by Edison Mission Group.[1] Based on assertions that the site is near Fort Bridger, its approximate coordinates are 41.318716°, -110.386418°.[2] References ↑ http://www.wsgs.uwyo.edu/Topics/EnergyResources/wind.aspx ↑ http://www.res-americas.com/wind-farms/operational-/mountain-wind-i-wind-farm.aspx Retrieved from "http://en.openei.org/w/index.php?title=Mountain_Wind&oldid=132229" Category: Wind Farms What links here Related changes Special pages Printable version Permanent link Browse properties 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load)

286

Wind turbine | Open Energy Information  

Open Energy Info (EERE)

turbine turbine Jump to: navigation, search Dictionary.png Wind turbine: A machine that converts wind energy to mechanical energy; typically connected to a generator to produce electricity. Other definitions:Wikipedia Reegle Contents 1 Types of Wind Turbines 1.1 Vertical Axis Wind Turbines 1.2 Horizontal Axis Wind Turbines 2 Wind Turbine Sizes 3 Components of a Wind Turbine 4 References Types of Wind Turbines There are two basic wind turbine designs: those with a vertical axis (sometimes referred to as VAWTs) and those with a horizontal axis (sometimes referred to as HAWTs). There are several manufacturers of vertical axis turbines, but they have not penetrated the "utility scale" (100 kW capacity and larger) market to the same degree as horizontal axis turbines.[1]

287

Cow Branch Wind Energy Center Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Cow Branch Wind Energy Center Wind Farm Cow Branch Wind Energy Center Wind Farm Jump to: navigation, search Name Cow Branch Wind Energy Center Wind Farm Facility Cow Branch Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Wind Capital Group/John Deere Capital Developer Wind Capital Group/John Deere Capital Energy Purchaser Associated Electric Cooperative Location Atchison County MO Coordinates 40.423897°, -95.477781° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.423897,"lon":-95.477781,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

288

Wind Farm | Department of Energy  

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

Wind Farm Wind Farm Wind Farm The wind farm in Greensburg, Kansas, was completed in spring 2010, and consists of ten 1.25 megawatt (MW) wind turbines that supply enough electricity to power every house, business, and municipal building in Greensburg. Technical assistance provided by the U.S. Department of Energy and the National Renewable Energy Laboratory was influential in helping Greensburg and its partners build the wind farm. The town uses only about 1/4 to 1/3 of the power generated to reach its "100% renewable energy, 100% of the time" goal. Excess power is placed back on the grid and offered as renewable energy credits for other Kansas Power Pool and Native Energy customers. The Greenburg Wind Farm continues to have an impact, inspiring Sunflower

289

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

290

Wind turbulence characterization for wind energy development  

DOE Green Energy (OSTI)

As part of its support of the US Department of Energy's (DOE's) Federal Wind Energy Program, the Pacific Northwest Laboratory (PNL) has initiated an effort to work jointly with the wind energy community to characterize wind turbulence in a variety of complex terrains at existing or potential sites of wind turbine installation. Five turbulence characterization systems were assembled and installed at four sites in the Tehachapi Pass in California, and one in the Green Mountains near Manchester, Vermont. Data processing and analyses techniques were developed to allow observational analyses of the turbulent structure; this analysis complements the more traditional statistical and spectral analyses. Preliminary results of the observational analyses, in the rotating framework or a wind turbine blade, show that the turbulence at a site can have two major components: (1) engulfing eddies larger than the rotor, and (2) fluctuating shear due to eddies smaller than the rotor disk. Comparison of the time series depicting these quantities at two sites showed that the turbulence intensity (the commonly used descriptor of turbulence) did not adequately characterize the turbulence at these sites. 9 refs., 10 figs.,

Wendell, L.L.; Gower, G.L.; Morris, V.R.; Tomich, S.D.

1991-09-01T23:59:59.000Z

291

Steel Winds | Open Energy Information  

Open Energy Info (EERE)

Steel Winds Steel Winds Facility Steel Winds Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner UPC Wind/BQ Energy Developer UPC Wind/BQ Energy Location Near Lackawanna NY Coordinates 42.81724°, -78.867542° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.81724,"lon":-78.867542,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

292

GL Wind | Open Energy Information  

Open Energy Info (EERE)

GL Wind GL Wind Jump to: navigation, search Name GL Wind Facility GL Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner GL Wind Developer Juhl Wind Energy Purchaser Xcel Energy Location Lewiston MN Coordinates 43.99800118°, -91.85827732° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.99800118,"lon":-91.85827732,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

293

Chiranjjeevi Wind Energy Limited CWEL | Open Energy Information  

Open Energy Info (EERE)

Chiranjjeevi Wind Energy Limited CWEL Jump to: navigation, search Name Chiranjjeevi Wind Energy Limited (CWEL) Place Pollachi, Tamil Nadu, India Zip 642 002 Sector Wind energy...

294

Spanish Wind Energy Association AEE | Open Energy Information  

Open Energy Info (EERE)

AEE Jump to: navigation, search Name Spanish Wind Energy Association (AEE) Place Madrid, Spain Zip 28006 Sector Wind energy Product Spain's association of wind-energy related...

295

from Wind Energy Development  

E-Print Network (OSTI)

These comments are submitted on behalf of the Clean Energy State Alliance (CESA) (electronically and by mail). CESA is a non-profit, multi-state coalition of state clean energy funds and programs working together to develop and promote clean energy technologies. CESA seeks to identify and address barriers to the development and growth of viable renewable energy resources in the United States. The California Energy Commission is a member of CESA. CESA offers its assistance and resources to the Commission and staff in the guidelines development process. CESA has substantial experience and expertise on the avian protection and wind siting issues that the Commission will consider in this Docket. Most notably, CESA is working actively with the United States Fish & Wildlife Service (USFWS), the Minerals Management Service, and several states (Pennsylvania, New York, Vermont, and others) to develop reasonable and effective approaches to addressing the impacts of wind projects on avian species. Many of the issues that the Commission will consider in this Docket are also being addressed by other states and federal agencies. CESA is available to provide relevant information and approaches that these other agencies and guidance development processes are employing, developing, and/or evaluating.

Dockets Office Ms; Dear Commissioners

2006-01-01T23:59:59.000Z

296

Building Energy Software Tools Directory: Degree Day Forecasts  

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

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

297

Environmental Energy Technologies Division Energy Analysis Department Community Wind Power  

E-Print Network (OSTI)

Environmental Energy Technologies Division · Energy Analysis Department Community Wind Power projects * standard US commercial wind development #12;Environmental Energy Technologies Division · Energy % Community- Owned Community- Owned Wind Capacity (MW) Total Wind Capacity (MW) #12;Environmental Energy

298

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network (OSTI)

of future contributions from various emerging transportation fuels and technologies is unknown. PotentiallyCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY AND TRANSPORTATION DIVISION B. B. Blevins Executive Director DISCLAIMER This report was prepared by a California

299

ESS 2012 Peer Review - Wind Firming EnergyFarm - Tom Stepien...  

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

the Bonneville Power Administration area. Forecasting wind is tricky. Plot shows: (forecast - actual)forecast. 5 sec data shown. 9 Modesto currently integrates intermittent...

300

NREL: Learning - Student Resources on Wind Energy  

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

Wind Energy Photo of a girl and a boy standing beneath a large wind turbine. Students can learn about wind energy by visiting a wind farm. The following resources will help you...

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


301

Gamesa Wind to Market | Open Energy Information  

Open Energy Info (EERE)

Market Jump to: navigation, search Name Gamesa Wind to Market Place Spain Sector Wind energy Product Represents the interests of wind project owner clients in the Spanish...

302

Wasatch Wind Inc | Open Energy Information  

Open Energy Info (EERE)

City, Utah Zip 44032 Sector Wind energy Product Wasatch Wind is a project developer of wind farms in the Intermountain region specializing in co-ownership with locally...

303

Westwind Wind Turbines | Open Energy Information  

Open Energy Info (EERE)

Westwind Wind Turbines Jump to: navigation, search Name Westwind Wind Turbines Place Northern Ireland, United Kingdom Zip BT29 4TF Sector Wind energy Product Northern Ireland based...

304

Offshore Ostsee Wind AG | Open Energy Information  

Open Energy Info (EERE)

Ostsee Wind AG Jump to: navigation, search Name Offshore Ostsee Wind AG Place Brgerende, Mecklenburg-Western Pomerania, Germany Zip 18211 Sector Wind energy Product Joint...

305

Norfolk Offshore Wind NOW | Open Energy Information  

Open Energy Info (EERE)

Norfolk Offshore Wind NOW Jump to: navigation, search Name Norfolk Offshore Wind (NOW) Place United Kingdom Sector Wind energy Product Formed to develop the 100MW Cromer offshore...

306

Wind Management LLC | Open Energy Information  

Open Energy Info (EERE)

Management LLC Jump to: navigation, search Name Wind Management LLC Place South Yarmouth, Massachusetts Zip 2664 Sector Wind energy Product Massachussets wind project development...

307

Sonne Wind Beteiligungen AG | Open Energy Information  

Open Energy Info (EERE)

search Name Sonne+Wind Beteiligungen AG Place Berlin, Germany Zip 10715 Sector Efficiency, Solar, Wind energy Product Berlin-based VC firm focusing on wind, solar and...

308

Danish Wind Industry Association | Open Energy Information  

Open Energy Info (EERE)

Jump to: navigation, search Name Danish Wind Industry Association Place Copenhagen V, Denmark Zip DK-1552 Sector Wind energy Product The Danish Wind Industry Association (DWIA) is...

309

Asia Wind Group Ltd | Open Energy Information  

Open Energy Info (EERE)

Group Ltd Place Beijing Municipality, China Zip 100085 Sector Wind energy Product Investment company focused on the wind sector in Asia. References Asia Wind Group Ltd1...

310

Wind Prospect Developments Ltd | Open Energy Information  

Open Energy Info (EERE)

Developments Ltd Jump to: navigation, search Name Wind Prospect Developments Ltd Place United Kingdom Zip BS8 1HG Sector Wind energy Product Wind Prospect Developments Limited was...

311

Guohua Hulunbeier Wind Power | Open Energy Information  

Open Energy Info (EERE)

Hulunbeier Wind Power Jump to: navigation, search Name Guohua (Hulunbeier) Wind Power Place Hulunbeier, Inner Mongolia Autonomous Region, China Zip 21300 Sector Wind energy Product...

312

Guohua Qiqihaer Wind Power | Open Energy Information  

Open Energy Info (EERE)

Qiqihaer Wind Power Jump to: navigation, search Name Guohua (Qiqihaer) Wind Power Place Qiqihaer, Heilongjiang Province, China Zip 161005 Sector Wind energy Product Guohua...

313

Wind Power Ltd | Open Energy Information  

Open Energy Info (EERE)

Power Ltd Place Wickam Market, United Kingdom Sector Wind energy Product Conducting research into alternative, large scale wind turbine design. References Wind Power Ltd1...

314

Wind Power Associates LLC | Open Energy Information  

Open Energy Info (EERE)

Associates LLC Jump to: navigation, search Name Wind Power Associates LLC Place Goldendale, Washington State Sector Wind energy Product Wind farm developer and operater....

315

Wind Park Solutions Arcadia | Open Energy Information  

Open Energy Info (EERE)

Arcadia Jump to: navigation, search Name Wind Park Solutions Arcadia Place Big Sandy, Montana Sector Wind energy Product JV between Wind Park Solutions America and Arcadia...

316

Auwahi Wind | Open Energy Information  

Open Energy Info (EERE)

Auwahi Wind Auwahi Wind Jump to: navigation, search Name Auwahi Wind Facility Auwahi Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner BP Wind Energy / Sempra Energy Developer Sempra Generation Energy Purchaser Maui Electric Co Location Maui HI Coordinates 20.596379°, -156.318304° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":20.596379,"lon":-156.318304,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

317

Rhaglen Ynni Gwynt Wind Energy Programme  

E-Print Network (OSTI)

Rhaglen Ynni Gwynt Wind Energy Programme Rhaglen Ynni Gwynt Wind Energy Programme Calculations supporting indicative figures used for the Wind Energy Programme Wind Energy (page) The energy to make,000,000 = 162.73 Therefore 4.5kWh/d/p = approximately 163 cups of tea per day per person Wind Energy Programme

318

Wind Energy Transmission | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Transmission Wind Energy Transmission Jump to: navigation, search Just a few years ago, 5% wind energy penetration in the United States was a lofty goal. In Europe, however, some countries have already reached wind energy penetrations of 10% or higher in a short period of time. The growth of domestic wind generation over the past decade has sharpened the focus on two questions: Can the electrical grid accommodate very high amounts of wind energy without jeopardizing security or degrading reliability? And, given that the nation's current transmission infrastructure is already constraining further development of wind generation in some regions, how could significantly larger amounts of wind energy be developed? The answers to these questions could hold the keys to determining how much of a role

319

Siting Wind Energy | Open Energy Information  

Open Energy Info (EERE)

Siting Wind Energy Siting Wind Energy Jump to: navigation, search Wind turbines at the Forward Wind Energy Center in Fond du Lac and Dodge Counties, Wisconsin. Photo from Ruth Baranowski/NREL, NREL 21207 The following resources provide information about siting wind energy projects. Some are specific to a state or region but may still contain information applicable to other areas. Wind project siting tools, such as calculators and databases, can be found here. Resources American Wind Energy Association. (Updated 2011). Siting, Health, and the Environment. Accessed August 13, 2013. This fact sheet provides an overview of siting myths and facts. Environmental Law Institute. Siting Wind Energy Facilities: What Do Local Elected Officials Need to Know?. Accessed November 29, 2013.

320

Wind energy manual  

E-Print Network (OSTI)

Objectives: The course introduces principles of wind power production, design of wind turbines, location and design of wind farms, control of turbines and wind farms, predictive modeling, diagnostics, operations and maintenance, condition monitoring, health monitoring and of turbine components and systems, wind farm performance optimization, and integration of wind power with a grid. The modeling and analysis aspect of the topics discussed in the class will be illustrated with examples and case studies. Textbook: References:

A. Vieira; Da Rosa; Fundamentals Renewable; Energy Processes; San Diego; Jacob Kirpes; Small Wind

2013-01-01T23:59:59.000Z

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


321

Wind News | Department of Energy  

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

News News Wind News RSS October 23, 2013 New Report Shows Trend Toward Larger Offshore Wind Systems, with 11 Advanced Stage Projects Proposed in U.S. Waters The Energy Department today released a new report showing progress for the U.S. offshore wind energy market in 2012. August 13, 2013 Largest Federally-Owned Wind Farm Breaks Ground at U.S. Weapons Facility Supports Obama Administration Goal to Power Federal Agencies with 20 Percent Clean Energy by 2020 August 6, 2013 Reports Show Record High U.S. Wind Energy Production and Manufacturing The Energy Department released two new reports today showcasing record growth across the U.S. wind market, supporting an increase in America's share of clean, renewable energy and tens of thousands of jobs nationwide. According to these reports, the United States continues to be one of the

322

Palouse Wind | Open Energy Information  

Open Energy Info (EERE)

Palouse Wind Palouse Wind Jump to: navigation, search Name Palouse Wind Facility Palouse Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Energy Purchaser Avista Location Naff Ridge Coordinates 47.1572222°, -117.3325° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":47.1572222,"lon":-117.3325,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

323

Sheffield Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Sheffield Wind Facility Sheffield Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Energy Purchaser Burlington Electric Department / Vermont Electric Cooperative Inc. / Washington Electric Cooperative Inc. Location Northern Caledonia County VT Coordinates 44.662191°, -72.103879° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":44.662191,"lon":-72.103879,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

324

Kawailoa Wind | Open Energy Information  

Open Energy Info (EERE)

Kawailoa Wind Kawailoa Wind Jump to: navigation, search Name Kawailoa Wind Facility Kawailoa Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Energy Purchaser Hawaii Electric Co Location Haleiwa HI Coordinates 21.62376064°, -158.063736° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":21.62376064,"lon":-158.063736,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

325

Kahuku Wind | Open Energy Information  

Open Energy Info (EERE)

Kahuku Wind Kahuku Wind Jump to: navigation, search Name Kahuku Wind Facility Kahuku Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Energy Purchaser Hawaiian Electric Co Inc Location Adjacent to Kahuku HI Coordinates 21.684095°, -157.982372° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":21.684095,"lon":-157.982372,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

326

Rollins Wind | Open Energy Information  

Open Energy Info (EERE)

Rollins Wind Rollins Wind Jump to: navigation, search Name Rollins Wind Facility Rollins Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Energy Purchaser Maine Public Utilities Commission / Central Maine Power / Bangor Hydro Electric Location East of Lincoln ME Coordinates 45.412708°, -68.370867° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.412708,"lon":-68.370867,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

327

Commonwealth Wind Commercial Wind Program | Department of Energy  

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

and business planning) Through the Commonwealth Wind Incentive Program - Commercial Wind Initiative the Massachusetts Clean Energy Center (MassCEC) offers site assessment...

328

Mid-range energy-forecasting system: structure, forecasts, and critique  

SciTech Connect

The Mid-Range Energy Forecasting System (MEFS) is a large-scale, interdisciplinary model of the US energy system maintained by the US Department of Energy. MEFS provides long-run regional forecasts of delivered prices for electricity, coal, gasoline, residual, distillate, and natural gas. A number of sets of MEFS forecasts are usually issued, each set corresponding to a different scenario. Because it forecasts prices and since these forecasts are regularly disseminated, MEFS is of considerable practical interest. A critical guide of the model's output for potential users is provided in this paper. The model's logic is described, the latest forecasts from MEFS are presented, and the reasonableness of both the forecasts and the methodology are critically evaluated. The manner in which MEFS interfaces with the Oil Market Simulation Model, which forecasts crude oil price, is also discussed. The evaluation concludes that while there are serious problems with MEFS, selective use can prove very helpful. 17 references, 1 figure, 2 tables.

DeSouza, G.

1980-01-01T23:59:59.000Z

329

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

330

Commercial Wind Energy Property Valuation  

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

Prior to 2007, wind energy devices generating electricity for commercial sale were assessed differently depending on where they were located. Some counties valued the entire turbine structure ...

331

Baseline Wind Energy Facility | Open Energy Information  

Open Energy Info (EERE)

Baseline Wind Energy Facility Baseline Wind Energy Facility Jump to: navigation, search Name Baseline Wind Energy Facility Facility Baseline Wind Energy Facility Sector Wind energy Facility Type Commercial Scale Wind Facility Status Proposed Owner First Wind Developer First Wind Location Gilliam County OR Coordinates 45.626863°, -120.162885° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.626863,"lon":-120.162885,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

332

Danielson Wind | Open Energy Information  

Open Energy Info (EERE)

Danielson Wind Danielson Wind Jump to: navigation, search Name Danielson Wind Facility Danielson Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Developer Juhl Wind Energy Purchaser Xcel Energy Location Near Atwater in Meeker County MN Coordinates 45.066913°, -94.738026° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.066913,"lon":-94.738026,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

333

Traer Wind | Open Energy Information  

Open Energy Info (EERE)

Traer Wind Traer Wind Jump to: navigation, search Name Traer Wind Facility Traer Wind Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Norsemen Wind Energy LLC Developer Clark Thompson Energy Purchaser Traer Municipal Electric Utility Location Traer IA Coordinates 42.15242792°, -92.46557236° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.15242792,"lon":-92.46557236,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

334

Wiota Wind | Open Energy Information  

Open Energy Info (EERE)

Wiota Wind Wiota Wind Jump to: navigation, search Name Wiota Wind Facility Wiota Wind Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Wiota Wind Energy LLC Energy Purchaser Farmers Electric Cooperative Coordinates 41.39149137°, -94.87689972° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.39149137,"lon":-94.87689972,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

335

Wind Energy Technologies Available for Licensing - Energy ...  

Site Map; Printable Version; Share this resource. Send a link to Wind Energy Technologies Available for Licensing - Energy Innovation Portalto someone by E-mail

336

Suzlon Wind Energy Corp | Open Energy Information  

Open Energy Info (EERE)

Corp Jump to: navigation, search Name Suzlon Wind Energy Corp Place Chicago, Illinois Zip 60631 Product Regional office of turbine manufacturer, Suzlon Energy. References Suzlon...

337

Wind Webinar Presentation Slides | Department of Energy  

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

Presentation Slides Wind Webinar Presentation Slides Download presentation slides from the DOE Office of Indian Energy webinar on wind renewable energy. DOE Office of Indian Energy...

338

Infinity Wind Power Inc | Open Energy Information  

Open Energy Info (EERE)

energy project developer assisting landowners to participate in the renewable energy industry, and more specifically, with wind energy projects. References Infinity Wind Power,...

339

Fairhaven Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Fairhaven Wind Facility Fairhaven Wind Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Solaya Energy / Palmer Capital / CTI Energy Developer Solaya Energy Energy Purchaser Town of Fairhaven Location Fairhaven MA Coordinates 41.63885963°, -70.87331772° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.63885963,"lon":-70.87331772,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

340

Distributed Wind | Open Energy Information  

Open Energy Info (EERE)

Distributed Wind Distributed Wind Jump to: navigation, search Distributed wind energy systems provide clean, renewable power for on-site use and help relieve pressure on the power grid while providing jobs and contributing to energy security for homes, farms, schools, factories, private and public facilities, distribution utilities, and remote locations.[1] Resources Clean Energy States Alliance. (2010). State-Based Financing Tools to Support Distributed and Community Wind Projects. Accessed September 27, 2013. This guide reviews the financing role that states, and specifically state clean energy funds, have played and can play in supporting community and distributed wind projects. Clean Energy States Alliance. (May 2010). Supporting Onsite Distributed Wind Generation Projects. Accessed September 27, 2013.

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


341

Lake Winds | Open Energy Information  

Open Energy Info (EERE)

Winds Winds Jump to: navigation, search Name Lake Winds Facility Lake Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Consumers Energy Developer Consumers Energy Energy Purchaser Consumers Energy Location Ludington MI Coordinates 43.83972728°, -86.38154984° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.83972728,"lon":-86.38154984,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

342

Verification of Mesoscale NWP Forecasts of Abrupt Cold Frontal Wind Changes  

Science Conference Proceedings (OSTI)

During a wildfire, a sharp wind change can lead to an abrupt increase in fire activity and change the rate of spread, endangering firefighters working on what had been the flank of the fire. In southeastern Australia, routine forecast of cold-...

Yimin Ma; Xinmei Huang; Graham A. Mills; Kevin Parkyn

2010-02-01T23:59:59.000Z

343

NREL: Learning - Wind Energy Basics: How Wind Turbines Work  

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

Wind Energy Basics: How Wind Turbines Work Wind Energy Basics: How Wind Turbines Work We have been harnessing the wind's energy for hundreds of years. From old Holland to farms in the United States, windmills have been used for pumping water or grinding grain. Today, the windmill's modern equivalent-a wind turbine-can use the wind's energy to generate electricity. Wind turbines, like windmills, are mounted on a tower to capture the most energy. At 100 feet (30 meters) or more aboveground, they can take advantage of the faster and less turbulent wind. Turbines catch the wind's energy with their propeller-like blades. Usually, two or three blades are mounted on a shaft to form a rotor. A blade acts much like an airplane wing. When the wind blows, a pocket of low-pressure air forms on the downwind side of the blade. The low-pressure

344

Page not found | Department of Energy  

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

than 5 Million to Support Wind Energy Development Funds to Enhance Short-Term Wind Forecasting and Accelerate Midsize Wind Turbine Development http:energy.govarticles...

345

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

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

346

Application of Radar Wind Observations for Low-Level NWP Wind Forecast Validation  

Science Conference Proceedings (OSTI)

The Finnish Meteorological Institute has produced a new numerical weather prediction model–based wind atlas of Finland. The wind atlas provides information on local wind conditions in terms of annual and monthly wind speed and direction averages. ...

Kirsti Salonen; Sami Niemelä; Carl Fortelius

2011-06-01T23:59:59.000Z

347

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

E-Print Network (OSTI)

operator (ISO) ­ Generate electricity to meet loads ­ Strive to maximize profits Independent system rules ­ Post next-day weather and load forecasts ­ Compute and post market clearing prices ­ Post unitElectric Power Market Simulations Using Individuals as Agents Guenter Conzelmann Argonne National

Kemner, Ken

348

Forecast of Icing Events at a Wind Farm in Sweden  

Science Conference Proceedings (OSTI)

This paper introduces a method to identify icing events using a physical icing model, driven by atmospheric data from the WRF model, and applies it to a wind park in Sweden. Observed wind park icing events were identified by deviation from an ...

Neil Davis; Andrea N. Hahmann; Niels-Erik Clausen; Mark Žagar

349

Time Series Models to Simulate and Forecast Wind Speed and Wind Power  

Science Conference Proceedings (OSTI)

A general approach for modeling wind speed and wind power is described. Because wind power is a function of wind speed, the methodology is based on the development of a model of wind speed. Values of wind power are estimated by applying the ...

Barbara G. Brown; Richard W. Katz; Allan H. Murphy

1984-08-01T23:59:59.000Z

350

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

351

EU Energy Wind Limited | Open Energy Information  

Open Energy Info (EERE)

company will be concentrating initially on bringing an innovative composite wind tower to market. References EU Energy (Wind) Limited1 LinkedIn Connections CrunchBase Profile No...

352

Scituate Wind | Open Energy Information  

Open Energy Info (EERE)

Scituate Wind Scituate Wind Jump to: navigation, search Name Scituate Wind Facility Scituate Wind Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Solaya Energy / Palmer Capital Developer Solaya Energy Energy Purchaser Town of Scituate Location Scituate MA Coordinates 42.17592749°, -70.72780252° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.17592749,"lon":-70.72780252,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

353

Wind Energy Myths | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Myths Wind Energy Myths Jump to: navigation, search Glacier Wind Project is located 10 miles west of Shelby, Montana, 2 miles south of Ethridge, in Glacier and Toole Counties, and is the largest wind farm in Montana. This project is comprised of 71 machines in phase 1 and 69 machines in phase 2 for a total of 140 Acciona AW-1500, capable of producing 210 MW at full capacity. Photo from Todd Spink, NREL 16521 U.S. Department of Energy. (July 10, 2011). Myths and Benefits of Wind Energy Wind Powering America hosted this webinar featuring speakers Ian Baring-Gould (National Renewable Energy Laboratory), Ed DeMeo, and Ben Hoen (Lawrence Berkeley National Laboratory). References Retrieved from "http://en.openei.org/w/index.php?title=Wind_Energy_Myths&oldid=700129"

354

Wind Energy Ordinances (Fact Sheet)  

SciTech Connect

Due to increasing energy demands in the United States and more installed wind projects, rural communities and local governments with limited or no experience with wind energy now have the opportunity to become involved in this industry. Communities with good wind resources may be approached by entities with plans to develop the resource. Although these opportunities can create new revenue in the form of construction jobs and land lease payments, they also create a new responsibility on the part of local governments to create ordinances to regulate wind turbine installations. Ordinances are laws, often found within municipal codes that provide various degrees of control to local governments. These laws cover issues such as zoning, traffic, consumer protection, and building codes. Wind energy ordinances reflect local needs and wants regarding wind turbines within county or city lines and aid the development of safe facilities that will be embraced by the community. Since 2008 when the National Renewable Energy Laboratory released a report on existing wind energy ordinances, many more ordinances have been established throughout the United States, and this trend is likely to continue in the near future as the wind energy industry grows. This fact sheet provides an overview of elements found in typical wind energy ordinances to educate state and local government officials, as well as policy makers.

2010-08-01T23:59:59.000Z

355

Federal Energy Management Program: Wind Energy Resources and...  

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

Wind Energy Resources and Technologies Photo of multiple wind turbines stand on green space in front of a mountain backdrop. The Department of Energy tests wind turbine...

356

INFOGRAPHIC: Wind Energy in America | Department of Energy  

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

INFOGRAPHIC: Wind Energy in America INFOGRAPHIC: Wind Energy in America Addthis 1 of 6 This infographic details key findings from the 2011 Wind Market Report. | Infographic by...

357

Greenfield Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Greenfield Wind Facility Greenfield Wind Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Greenfield Wind Power LLC (community owned) Energy Purchaser City of Greenfield - excess to Central Iowa Power Cooperative Location Greenfield IA Coordinates 41.29064139°, -94.48559761° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.29064139,"lon":-94.48559761,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

358

Michigan Wind II Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Wind II Wind Farm Wind II Wind Farm Jump to: navigation, search Name Michigan Wind II Wind Farm Facility Michigan Wind II Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Exelon Wind Developer Exelon Wind Energy Purchaser Consumers Energy Location Minden City MI Coordinates 43.6572421°, -82.7681278° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.6572421,"lon":-82.7681278,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

359

Mountaineer Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Mountaineer Wind Energy Center Mountaineer Wind Energy Center Jump to: navigation, search Name Mountaineer Wind Energy Center Facility Mountaineer Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer Atlantic Renewable Energy Energy Purchaser Exelon Location Thomas WV Coordinates 39.163081°, -79.554516° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.163081,"lon":-79.554516,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

360

Wind Power Forecasting Error Distributions over Multiple Timescales: Preprint  

DOE Green Energy (OSTI)

In this paper, we examine the shape of the persistence model error distribution for ten different wind plants in the ERCOT system over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution.

Hodge, B. M.; Milligan, M.

2011-03-01T23:59:59.000Z

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


361

Wind Energy Act (Maine) | Department of Energy  

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

Wind Energy Act (Maine) Wind Energy Act (Maine) Wind Energy Act (Maine) < Back Eligibility Developer Utility Agricultural Commercial Construction Fed. Government Fuel Distributor General Public/Consumer Industrial Installer/Contractor Institutional Investor-Owned Utility Local Government Low-Income Residential Multi-Family Residential Municipal/Public Utility Nonprofit Residential Retail Supplier Rural Electric Cooperative Schools State/Provincial Govt Systems Integrator Transportation Tribal Government Savings Category Wind Buying & Making Electricity Program Info State Maine Program Type Solar/Wind Access Policy Siting and Permitting The Maine Wind Energy Act is a summary of legislative findings that indicate the state's strong interest in promoting the development of wind energy and establish the state's desire to ease the regulatory process for

362

Distributed Wind Energy in Idaho  

SciTech Connect

Project Objective: This project is a research and development program aimed at furthering distributed wind technology. In particular, this project addresses some of the barriers to distributed wind energy utilization in Idaho. Background: At its core, the technological challenge inherent in Wind Energy is the transformation of a highly variable form of energy to one which is compatible with the commercial power grid or another useful application. A major economic barrier to the success of distributed wind technology is the relatively high capital investment (and related long payback periods) associated with wind turbines. This project will carry out fundamental research and technology development to address both the technological and economic barriers. � Active drive train control holds the potential to improve the overall efficiency of a turbine system by allowing variable speed turbine operation while ensuring a tight control of generator shaft speed, thus greatly simplifying power conditioning. � Recent blade aerodynamic advancements have been focused on large, utility-scale wind turbine generators (WTGs) as opposed to smaller WTGs designed for distributed generation. Because of Reynolds Number considerations, blade designs do not scale well. Blades which are aerodynamically optimized for distributed-scale WTGs can potentially reduce the cost of electricity by increasing shaft-torque in a given wind speed. � Grid-connected electric generators typically operate at a fixed speed. If a generator were able to economically operate at multiple speeds, it could potentially convert more of the wind�s energy to electricity, thus reducing the cost of electricity. This research directly supports the stated goal of the Wind and Hydropower Technologies Program for Distributed Wind Energy Technology: By 2007, reduce the cost of electricity from distributed wind systems to 10 to 15 cents/kWh in Class 3 wind resources, the same level that is currently achievable in Class 5 winds.

Gardner, John; Ferguson, James; Ahmed-Zaid, Said; Johnson, Kathryn; Haynes, Todd; Bennett, Keith

2009-01-31T23:59:59.000Z

363

Energy 101: Wind Turbines | Department of Energy  

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

Wind Turbines Wind Turbines Energy 101: Wind Turbines July 30, 2010 - 10:47am Addthis John Schueler John Schueler Former New Media Specialist, Office of Public Affairs On Tuesday, the Department announced a $117 million loan guarantee through for the Kahuku Wind Power Project in Hawaii. That's a major step forward for clean energy in the region, as it's expected to supply clean electricity to roughly 7,700 households per year, and it also invites a deceptively simple question: how exactly do wind turbines generate electricity? One thing you might not realize is that wind is actually a form of solar energy. This is because wind is produced by the sun heating Earth's atmosphere, the rotation of the earth, and the earth's surface irregularities. Wind turbines are the rotary devices that convert the

364

ABO Wind AG | Open Energy Information  

Open Energy Info (EERE)

ABO Wind AG Place Hessen, Germany Zip 65193 Sector Bioenergy, Wind energy Product German developer of wind and bioenergy generation assets. ABO Wind has no direct holding in any...

365

AeroWind Inc | Open Energy Information  

Open Energy Info (EERE)

Inc Jump to: navigation, search Name AeroWind Inc. Place Potsdam, New York Sector Wind energy Product Wind turbines manufacturer. References AeroWind Inc.1 LinkedIn...

366

Howden Wind Turbines Ltd | Open Energy Information  

Open Energy Info (EERE)

Howden Wind Turbines Ltd Jump to: navigation, search Name Howden Wind Turbines Ltd Place United Kingdom Sector Wind energy Product Howden was a manufacturer of wind turbines in the...

367

Vish Wind Infrastructure Ltd | Open Energy Information  

Open Energy Info (EERE)

Vish Wind Infrastructure Ltd Jump to: navigation, search Name Vish Wind Infrastructure Ltd Place India Sector Wind energy Product Plans to set up 4.6GW of wind power projects in...

368

Definition: Commercial Scale Wind | Open Energy Information  

Open Energy Info (EERE)

Scale Wind Commercial scale wind refers to wind energy projects greater than 100 kW. The electricity that is generated is sold.1 Also Known As Utility-Scale Wind Related Terms...

369

Cielo Wind Power LLC | Open Energy Information  

Open Energy Info (EERE)

Cielo Wind Power LLC Jump to: navigation, search Name Cielo Wind Power LLC Place Austin, Texas Zip 78701 2459 Sector Wind energy Product Currently the largest wind power developer...

370

Devon Wind Power Ltd | Open Energy Information  

Open Energy Info (EERE)

Devon Wind Power Ltd Jump to: navigation, search Name Devon Wind Power Ltd Place Exeter, United Kingdom Zip EX1 1TL Sector Wind energy Product Wind project developer - has proposed...

371

Wind Walkers | Open Energy Information  

Open Energy Info (EERE)

Walkers Walkers Jump to: navigation, search Name Wind Walkers Facility Wind Walkers Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner 5045 Wind Partners Developer 5045 Wind Partners Energy Purchaser Alliant Energy Location Waukon IA Coordinates 43.2655101°, -91.4863848° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.2655101,"lon":-91.4863848,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

372

Maiden Winds | Open Energy Information  

Open Energy Info (EERE)

Maiden Winds Maiden Winds Jump to: navigation, search Name Maiden Winds Facility Maiden Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Edison Mission Group owns majority Developer Dan Juhl Energy Purchaser Xcel Energy Location West Pipestone MN Coordinates 44.000815°, -96.340445° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":44.000815,"lon":-96.340445,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

373

We Energy Wind Farm | Open Energy Information  

Open Energy Info (EERE)

We Energy Wind Farm We Energy Wind Farm Jump to: navigation, search Name We Energy Wind Farm Facility We Energy Wind Farm Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Energy Purchaser WE Energies Location South of Fond du Lac WI Coordinates 43.657512°, -88.439004° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.657512,"lon":-88.439004,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

374

Searsburg Wind Energy Facility | Open Energy Information  

Open Energy Info (EERE)

Searsburg Wind Energy Facility Searsburg Wind Energy Facility Jump to: navigation, search Name Searsburg Wind Energy Facility Facility Searsburg Wind Energy Facility Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner EnXco Developer GE Energy Energy Purchaser Green Mountain Power Location Searsburg VT Coordinates 42.861356°, -72.964445° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.861356,"lon":-72.964445,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

375

POWER4 Amstel Wind Energy | Open Energy Information  

Open Energy Info (EERE)

POWER4 Amstel Wind Energy Jump to: navigation, search Name POWER4 Amstel Wind Energy Place Bangalore, Karnataka, India Zip 560034 Sector Wind energy Product Bangalore-based wind...

376

Wind Energy Resource Atlas of Armenia  

DOE Green Energy (OSTI)

This wind energy resource atlas identifies the wind characteristics and distribution of the wind resource in the country of Armenia. The detailed wind resource maps and other information contained in the atlas facilitate the identification of prospective areas for use of wind energy technologies for utility-scale power generation and off-grid wind energy applications. The maps portray the wind resource with high-resolution (1-km2) grids of wind power density at 50-m above ground. The wind maps were created at the National Renewable Energy Laboratory (NREL) using a computerized wind mapping system that uses Geographic Information System (GIS) software.

Elliott, D.; Schwartz, M.; Scott, G.; Haymes, S.; Heimiller, D.; George, R.

2003-07-01T23:59:59.000Z

377

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

Science Conference Proceedings (OSTI)

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

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

2010-02-21T23:59:59.000Z

378

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

DOE Green Energy (OSTI)

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

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

2010-02-21T23:59:59.000Z

379

Wind Energy Ordinances | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Ordinances Wind Energy Ordinances Jump to: navigation, search Photo from First Wind, NREL 17545 Due to increasing energy demands in the United States and more installed wind projects, rural communities and local governments with limited or no experience with wind energy now have the opportunity to become involved in this industry. Communities with good wind resources may be approached by entities with plans to develop the resource. Although these opportunities can create new revenue in the form of construction jobs and land lease payments, they also create a new responsibility on the part of local governments to create ordinances to regulate wind turbine installations. Ordinances are laws, often found within municipal codes that provide various degrees of control to local governments. These laws cover issues

380

Bravo Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Bravo Wind Facility Bravo Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status Proposed Developer Bravo Wind LLC Location Cassia County ID Coordinates 42.460351°, -113.474564° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.460351,"lon":-113.474564,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

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


381

Energy forecasting: the troubled past of looking the future  

SciTech Connect

Energy forecasts have hardly been distinguished by their accuracy. Why forecasts go awry, and the impact these prominent tools have, is explored. A brief review of the record is given. Because of their allure, their popularity in he media, and their usefulness as tools in political battles, forecasts have played a significant role so far. The danger is that they represent and enhance a fix 'em up, tinkering approach, to the detriment of more efficient free-market policies.

Kutler, E.

1986-01-01T23:59:59.000Z

382

Technical Report - Cuba Wind Energy Resource Assessment  

Open Energy Info (EERE)

Cuba Wind Energy Resource Assessment (Abstract):  This document describes the development of detailed high-resolution (1 km2) wind energy resource maps for...

383

Technical Report - China Wind Energy Resource Assessment  

Open Energy Info (EERE)

China Wind Energy Resource Assessment (Abstract):  This document describes the development of detailed high-resolution (1 km2) wind energy resource maps for...

384

Technical Report - Ghana Wind Energy Resource Assessment  

Open Energy Info (EERE)

Ghana Wind Energy Resource Assessment (Abstract):  This document describes the development of detailed high-resolution (1 km2) wind energy resource maps for...

385

Wales Wind Energy Project | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Project Wind Energy Project Jump to: navigation, search Name Wales Wind Energy Project Facility Wales Wind Energy Project Sector Wind energy Facility Type Small Scale Wind Facility Status In Service Owner Alaska Village Electric Coop Developer Kotzebue Electric Assoc. Energy Purchaser Alaska Village Electric Coop Location Wales AK Coordinates 65.6113°, -168.091° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":65.6113,"lon":-168.091,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

386

Recent wind resource characterization activities at the National Renewable Energy Laboratory  

DOE Green Energy (OSTI)

The wind resource characterization team at the National Renewable Energy Laboratory (NREL) is working to improve the characterization of the wind resource in many key regions of the world. Tasks undertaken in the past year include: updates to the comprehensive meteorological and geographic data bases used in resource assessments in the US and abroad; development and validation of an automated wind resource mapping procedure; support in producing wind forecasting tools useful to utilities involved in wind energy generation; continued support for recently established wind measurement and assessment programs in the US.

Elliott, D.L.; Schwartz, M.N.

1997-07-01T23:59:59.000Z

387

Galactic Wind | Open Energy Information  

Open Energy Info (EERE)

Galactic Wind Galactic Wind Jump to: navigation, search Name Galactic Wind Facility Galactic Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Epic Systems Developer Epic Systems Energy Purchaser Epic Systems Location Waunakee WI Coordinates 43.17297°, -89.560688° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.17297,"lon":-89.560688,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

388

Manzana Winds | Open Energy Information  

Open Energy Info (EERE)

Manzana Winds Manzana Winds Jump to: navigation, search Name Manzana Winds Facility Manzana Winds Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Iberdrola Renewables Developer Iberdrola Renewables Energy Purchaser San Diego Gas and Electric / City of Santa Clara Silicon Valley Power Location Mojave CA Coordinates 34.932662°, -118.46105° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":34.932662,"lon":-118.46105,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

389

Harbor Wind | Open Energy Information  

Open Energy Info (EERE)

Harbor Wind Harbor Wind Facility Harbor Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Harbor Wind LLC Developer Revolution Energy Location Corpus Christi TX Coordinates 27.83061326°, -97.43418217° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":27.83061326,"lon":-97.43418217,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

390

Garnet Wind | Open Energy Information  

Open Energy Info (EERE)

Garnet Wind Garnet Wind Jump to: navigation, search Name Garnet Wind Facility Garnet Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Azusa Light & Water Developer Azusa Light & Water Energy Purchaser Azusa Light & Water Location Palm Springs CA Coordinates 33.918267°, -116.701076° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":33.918267,"lon":-116.701076,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

391

Willmar Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Willmar Wind Facility Willmar Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Willmar Municipal Utilities Developer Willmar Municipal Utilities Energy Purchaser Willmar Municipal Utilities Location Willmar MN Coordinates 45.158659°, -95.007498° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.158659,"lon":-95.007498,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

392

Alaska/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Alaska/Wind Resources Alaska/Wind Resources < Alaska Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Alaska Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

393

Wyoming/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Wyoming/Wind Resources Wyoming/Wind Resources < Wyoming Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Wyoming Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

394

Nevada/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Nevada/Wind Resources Nevada/Wind Resources < Nevada Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Nevada Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

395

Kansas/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Kansas/Wind Resources Kansas/Wind Resources < Kansas Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Kansas Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

396

Washington/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Washington/Wind Resources Washington/Wind Resources < Washington Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Washington Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid?

397

Louisiana/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Louisiana/Wind Resources Louisiana/Wind Resources < Louisiana Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Louisiana Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

398

Oregon/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Oregon/Wind Resources Oregon/Wind Resources < Oregon Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Oregon Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

399

Kentucky/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Kentucky/Wind Resources Kentucky/Wind Resources < Kentucky Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Kentucky Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

400

Nebraska/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Nebraska/Wind Resources Nebraska/Wind Resources < Nebraska Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Nebraska Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

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


401

Alabama/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Alabama/Wind Resources Alabama/Wind Resources < Alabama Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Alabama Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

402

Florida/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Florida/Wind Resources Florida/Wind Resources < Florida Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Florida Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

403

Vermont/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Vermont/Wind Resources Vermont/Wind Resources < Vermont Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Vermont Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

404

Wisconsin/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Wisconsin/Wind Resources Wisconsin/Wind Resources < Wisconsin Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Wisconsin Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

405

Idaho/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Idaho/Wind Resources Idaho/Wind Resources < Idaho Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Idaho Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

406

Missouri/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Missouri/Wind Resources Missouri/Wind Resources < Missouri Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Missouri Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

407

Iowa/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Iowa/Wind Resources Iowa/Wind Resources < Iowa Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Iowa Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

408

Maryland/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Maryland/Wind Resources Maryland/Wind Resources < Maryland Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Maryland Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

409

Massachusetts/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Massachusetts/Wind Resources Massachusetts/Wind Resources < Massachusetts Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Massachusetts Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid?

410

Minnesota/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Minnesota/Wind Resources Minnesota/Wind Resources < Minnesota Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Minnesota Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

411

Pennsylvania/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Pennsylvania/Wind Resources Pennsylvania/Wind Resources < Pennsylvania Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Pennsylvania Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid?

412

Hawaii/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Hawaii/Wind Resources Hawaii/Wind Resources < Hawaii Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Hawaii Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid?

413

Wind Energy Resource Atlas of Oaxaca  

DOE Green Energy (OSTI)

The Oaxaca Wind Resource Atlas, produced by the National Renewable Energy Laboratory's (NREL's) wind resource group, is the result of an extensive mapping study for the Mexican State of Oaxaca. This atlas identifies the wind characteristics and distribution of the wind resource in Oaxaca. The detailed wind resource maps and other information contained in the atlas facilitate the identification of prospective areas for use of wind energy technologies, both for utility-scale power generation and off-grid wind energy applications.

Elliott, D.; Schwartz, M.; Scott, G.; Haymes, S.; Heimiller, D.; George, R.

2003-08-01T23:59:59.000Z

414

Federal Energy Management Program: Wind Energy Resources and Technologies  

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

Wind Energy Wind Energy Resources and Technologies to someone by E-mail Share Federal Energy Management Program: Wind Energy Resources and Technologies on Facebook Tweet about Federal Energy Management Program: Wind Energy Resources and Technologies on Twitter Bookmark Federal Energy Management Program: Wind Energy Resources and Technologies on Google Bookmark Federal Energy Management Program: Wind Energy Resources and Technologies on Delicious Rank Federal Energy Management Program: Wind Energy Resources and Technologies on Digg Find More places to share Federal Energy Management Program: Wind Energy Resources and Technologies on AddThis.com... Energy-Efficient Products Technology Deployment Renewable Energy Federal Requirements Renewable Resources & Technologies Solar

415

Wind Energy Status and Future Wind Engineering Challenges: Preprint  

DOE Green Energy (OSTI)

This paper describes the current status of wind energy technology, the potential for future wind energy development and the science and engineering challenges that must be overcome for the technology to meet its potential.

Thresher, R.; Schreck, S.; Robinson, M.; Veers, P.

2008-08-01T23:59:59.000Z

416

WIND ENERGY PROGRAM - Home - Energy Innovation Portal  

Wind Energy Program Investment Philosophy Since the ’80s, DOE has used cost-shared partnerships to work with businesses DOE partnership has encouraged development ...

417

Bayonne Wind Energy Project | Open Energy Information  

Open Energy Info (EERE)

Bayonne Wind Energy Project Bayonne Wind Energy Project Jump to: navigation, search Name Bayonne Wind Energy Project Facility Bayonne Wind Energy Project Sector Wind energy Facility Type Community Wind Facility Status In Service Owner Bayonne Municipal Utility Authority Developer Bayonne Municipal Utility Authority Location Bayonne NJ Coordinates 40.65277771°, -74.11774993° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":40.65277771,"lon":-74.11774993,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

418

Dunlap Wind Energy Project | Open Energy Information  

Open Energy Info (EERE)

Dunlap Wind Energy Project Dunlap Wind Energy Project Jump to: navigation, search Name Dunlap Wind Energy Project Facility Dunlap Wind Energy Project Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner PacifiCorp Developer PacifiCorp Location North of Medicine Bow in Carbon County WY Coordinates 42.013591°, -106.21419° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":42.013591,"lon":-106.21419,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

419

Offshore Wind Energy | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit History Facebook icon Twitter icon » Offshore Wind Energy Jump to: navigation, search The Middelgrunden Wind Farm was established as a collaboration between Middelgrunden Wind Turbine Cooperative and Copenhagen Energy, each installing 10 2-MW Bonus wind turbines. The farm is located off the coast of Denmark, east of the northern tip of Amager. Photo from H.C. Sorensen, NREL 17856 Offshore wind energy is a clean, domestic, renewable resource that can help the United States meet its critical energy, environmental, and economic challenges. By generating electricity from offshore wind turbines, the nation can reduce its greenhouse gas emissions, diversify its energy supply, provide cost-competitive electricity to key coastal regions, and help revitalize key sectors of its economy, including manufacturing.

420

Havoco Wind Energy LLC | Open Energy Information  

Open Energy Info (EERE)

Havoco Wind Energy LLC Havoco Wind Energy LLC Jump to: navigation, search Name Havoco Wind Energy LLC Place Dallas, Texas Zip 75206 Sector Wind energy Product Wind developer of Altamont Pass wind farms. Subsidiary of G3 Energy, the Babcock and Brown subsidiary. Coordinates 32.778155°, -96.795404° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":32.778155,"lon":-96.795404,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

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


421

QuikSCAT Impacts on Coastal Forecasts and Warnings: Operational Utility of Satellite Ocean Surface Vector Wind Data  

Science Conference Proceedings (OSTI)

This study reports on the operational utility of ocean surface vector wind (SVW) data from Quick Scatterometer (QuikSCAT) observations in the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Weather Forecast ...

Ralph F. Milliff; Peter A. Stamus

2008-10-01T23:59:59.000Z

422

The Impact of Radar Data on Short-Term Forecasts of Surface Temperature, Dewpoint Depression, and Wind Speed  

Science Conference Proceedings (OSTI)

A statistical system that uses surface observations and radar data to provide 1-, 3-, and 6-h forecasts of temperature, dewpoint depression, and wind speed is developed. Application of the system to independent data demonstrates that the radar ...

Emily K. Grover-Kopec; J. Michael Fritsch

2003-12-01T23:59:59.000Z

423

Rhaglen Ynni Gwynt Wind Energy Programme  

E-Print Network (OSTI)

Rhaglen Ynni Gwynt Wind Energy Programme 1 WEP Internet Calculations Explained | 20/02/2013 Calculations supporting indicative figures used for the Wind Energy Programme Wind Energy (page) "The energy.2 Therefore 4.5kWh/d/p = approximately 160 cups of tea per day per person. Wind Energy Programme (page

424

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

Open Energy Info (EERE)

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

425

Michigan Wind I Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Wind I Wind Farm Wind I Wind Farm Jump to: navigation, search Name Michigan Wind I Wind Farm Facility Michigan Wind I Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner John Deere Wind Developer Noble Environmental Power Energy Purchaser Consumers Energy Location Huron County MI Coordinates 43.7099°, -82.9388° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":43.7099,"lon":-82.9388,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

426

Mogul Energy Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Mogul Energy Wind Farm Mogul Energy Wind Farm Jump to: navigation, search Name Mogul Energy Wind Farm Facility Mogul Energy Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Developer Mogul Energy Energy Purchaser Southern California Edison Co Location Tehachapi CA Coordinates 35.07665°, -118.25529° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.07665,"lon":-118.25529,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

427

Weatherford Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Weatherford Wind Energy Center Weatherford Wind Energy Center Facility Weatherford Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser American Electric Power Location Weatherford OK Coordinates 35.559414°, -98.742992° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.559414,"lon":-98.742992,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

428

Minco Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Center Wind Energy Center Facility Minco Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser Public Service Company of Oklahoma Location South of Minco OK Coordinates 35.294204°, -97.926081° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":35.294204,"lon":-97.926081,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

429

Oliver Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Center Wind Energy Center Facility Oliver Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser Minnesota Power Location Oliver County ND Coordinates 47.180446°, -101.225116° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":47.180446,"lon":-101.225116,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

430

Ohio/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit History Facebook icon Twitter icon » Ohio/Wind Resources < Ohio Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Ohio Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid?

431

Small Wind Guidebook | Open Energy Information  

Open Energy Info (EERE)

Small Wind Guidebook Small Wind Guidebook Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid? * State Information Portal * Glossary of Terms

432

Montana/Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit History Facebook icon Twitter icon » Montana/Wind Resources < Montana Jump to: navigation, search Print PDF Print Full Version WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home OpenEI Home >> Wind >> Small Wind Guidebook >> Montana Wind Resources WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid?

433

Wind Easements | Department of Energy  

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

Wind Easements Wind Easements Wind Easements < Back Eligibility Agricultural Fed. Government Institutional Local Government Nonprofit Residential Schools State Government Tribal Government Savings Category Wind Buying & Making Electricity Program Info State South Dakota Program Type Solar/Wind Access Policy Provider S.D. Energy Management Office Any South Dakota property owner may grant a wind easement with the same effect as a conveyance of an interest in real property. Easements must be established in writing, and must be filed, recorded and indexed in the office of the register of deeds of the county in which they are granted. The maximum term of an easement is 50 years. Any payments associated with an easement must be made on an annual basis to the owner of the real property. An easement must include the following information:

434

Royal Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name Royal Wind Place Denver, Colorado Sector Wind energy Product Vertical Wind Turbines Year founded 2008 Website http://www.RoyalWindTurbines.c Coordinates 39.7391536°, -104.9847034° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":39.7391536,"lon":-104.9847034,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

435

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

436

Wind Energy & Manufacturing | Open Energy Information  

Open Energy Info (EERE)

Wind Energy & Manufacturing Wind Energy & Manufacturing Jump to: navigation, search Blades manufactured at Gamesa's factory in Ebensburg, Pennsylvania, await delivery for development of wind farms across the country in the United States. Photo from Gamesa, NREL 16001 Wind power creates new high-paying jobs in a wide variety of industries. This includes direct jobs installing, operating, and maintaining wind turbines, as well as jobs at manufacturing facilities that produce wind turbines, blades, electronic components, gearboxes, generators, towers, and other equipment. Indirect jobs in the industries that support these activities are also created.[1] In 2012, 72% of the wind turbine equipment (including towers, blades, and gears) installed in the United States during the year was made in

437

New England Wind Forum: New England Wind Energy Education Project  

Wind Powering America (EERE)

Webinars Webinars Conference Historic Wind Development in New England State Activities Projects in New England Building Wind Energy in New England Newsletter Perspectives Events Quick Links to States CT MA ME NH RI VT Bookmark and Share New England Wind Energy Education Project The New England Wind Energy Education Project (NEWEEP) is designed to complement the New England Wind Forum website and newsletter as a comprehensive source of objective information on wind energy issues in the New England region. The project, funded by the U.S. Department of Energy's (DOE's) former Wind Powering America Initiative under a 2-year grant, began as an eight-part webinar series and a conference. The NEWEEP webinar series provides the public with objective information to allow informed decisions about proposed wind energy projects throughout the New England region.

438

Altech Energy Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Altech Energy Wind Farm Altech Energy Wind Farm Jump to: navigation, search Name Altech Energy Wind Farm Facility Altech Energy Ltd Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner SeaWest Developer SeaWest Energy Purchaser Pacific Gas & Electric Co Location Altamont Pass CA Coordinates 37.7347°, -121.652° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.7347,"lon":-121.652,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

439

Philippines Wind Energy Resource Atlas Development  

DOE Green Energy (OSTI)

This paper describes the creation of a comprehensive wind energy resource atlas for the Philippines. The atlas was created to facilitate the rapid identification of good wind resource areas and understanding of the salient wind characteristics. Detailed wind resource maps were generated for the entire country using an advanced wind mapping technique and innovative assessment methods recently developed at the National Renewable Energy Laboratory.

Elliott, D.

2000-11-29T23:59:59.000Z

440

Lime Wind | Open Energy Information  

Open Energy Info (EERE)

Lime Wind Lime Wind Facility Lime Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Joseph Millworks Inc Developer Joseph Millworks Inc Energy Purchaser Idaho Power Location Huntington OR Coordinates 44.406667°, -117.310278° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":44.406667,"lon":-117.310278,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

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


441

Pacific Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Facility Pacific Wind Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner enXco Developer EnXco Energy Purchaser San Diego Gas & Electric Location Rosamond CA Coordinates 34.94448806°, -118.3886719° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":34.94448806,"lon":-118.3886719,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

442

BP Wind | Open Energy Information  

Open Energy Info (EERE)

Wind Wind Jump to: navigation, search Name BP Wind Place Houston, Texas Zip 77002-2700 Sector Wind energy Product Department of BP Alternative Energy that deals with BP's interest in wind power. Coordinates 29.76045°, -95.369784° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.76045,"lon":-95.369784,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

443

Stateline Wind Energy Project | Open Energy Information  

Open Energy Info (EERE)

Energy Project Energy Project Jump to: navigation, search Name Stateline Wind Energy Project Facility Stateline Wind Energy Project Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner NextEra Energy Resources Developer NextEra Energy Resources Energy Purchaser PPM Energy Inc Location Walla Walla County Coordinates 46.012769°, -118.751528° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":46.012769,"lon":-118.751528,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

444

Energy from Offshore Wind: Preprint  

DOE Green Energy (OSTI)

This paper provides an overview of the nascent offshore wind energy industry including a status of the commercial offshore industry and the technologies that will be needed for full market development.

Musial, W.; Butterfield, S.; Ram, B.

2006-02-01T23:59:59.000Z

445

Stetson Wind Expansion Wind Farm | Open Energy Information  

Open Energy Info (EERE)

Stetson Wind Expansion Wind Farm Stetson Wind Expansion Wind Farm Jump to: navigation, search Name Stetson Wind Expansion Wind Farm Facility Stetson Wind Expansion Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner First Wind Developer First Wind Location Washington County ME Coordinates 45.595833°, -67.928628° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":45.595833,"lon":-67.928628,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

446

High Resolution Atmospheric Modeling for Wind Energy Applications  

SciTech Connect

The ability of the WRF atmospheric model to forecast wind speed over the Nysted wind park was investigated as a function of time. It was found that in the time period we considered (August 1-19, 2008), the model is able to predict wind speeds reasonably accurately for 48 hours ahead, but that its forecast skill deteriorates rapidly after 48 hours. In addition, a preliminary analysis was carried out to investigate the impact of vertical grid resolution on the forecast skill. Our preliminary finding is that increasing vertical grid resolution does not have a significant impact on the forecast skill of the WRF model over Nysted wind park during the period we considered. Additional simulations during this period, as well as during other time periods, will be run in order to validate the results presented here. Wind speed is a difficult parameter to forecast due the interaction of large and small length scale forcing. To accurately forecast the wind speed at a given location, the model must correctly forecast the movement and strength of synoptic systems, as well as the local influence of topography / land use on the wind speed. For example, small deviations in the forecast track or strength of a large-scale low pressure system can result in significant forecast errors for local wind speeds. The purpose of this study is to provide a preliminary baseline of a high-resolution limited area model forecast performance against observations from the Nysted wind park. Validating the numerical weather prediction model performance for past forecasts will give a reasonable measure of expected forecast skill over the Nysted wind park. Also, since the Nysted Wind Park is over water and some distance from the influence of terrain, the impact of high vertical grid spacing for wind speed forecast skill will also be investigated.

Simpson, M; Bulaevskaya, V; Glascoe, L; Singer, M

2010-03-18T23:59:59.000Z

447

New England Wind Forum: Building Wind Energy in New England  

Wind Powering America (EERE)

Projects in New England Building Wind Energy in New England Wind Resource Wind Power Technology Economics Markets Siting Policy Technical Challenges Issues Small Wind Large Wind Newsletter Perspectives Events Quick Links to States CT MA ME NH RI VT Bookmark and Share Building Wind Energy in New England Many factors influence the ability to develop wind power in the New England region. A viable project requires the right site and the right technology for the application. It must provide suitable revenue or economic value to justify investment in this capital-intensive but zero-fuel technology. Policy initiatives are in place throughout the region to support the expansion of wind power's role in the regional supply mix. However, issues affecting public acceptance of wind projects in host communities must be addressed. Information on topics affecting wind power development in New England can be found by using the navigation to the left.

448

Technology Overview Fundamentals of Wind Energy (Presentation)  

SciTech Connect

A presentation that describes the technology, costs and trends, and future development of wind energy technologies.

Butterfield, S.

2005-05-01T23:59:59.000Z

449

Paul S. Veers Wind Energy Technology Department  

E-Print Network (OSTI)

Paul S. Veers Wind Energy Technology Department Sandia National Laboratories Thursday, April 8th 3 Y WIND ENERGY SEMINAR SERIES Wind energy is a growing electricity source around the world, providing. The rapid expansion of wind is largely due to its relative similarity in levelized cost of energy to fossil

Ginzel, Matthew

450

New England Wind Forum: New England Wind Energy Education Project  

Wind Powering America (EERE)

New England Wind Energy Education Project Conference and Workshop New England Wind Energy Education Project Conference and Workshop The New England Wind Energy Education Project (NEWEEP) held its one-day Conference and Workshop on June 7, 2011 in Marlborough, Massachusetts. The conference and workshop focused on presenting objective information relevant to issues of importance to individuals affected by wind energy proposals throughout New England. The conference was featured on the website of the Department of Energy's former Wind Powering America initiative: NEWEEP Convenes Conference and Workshop to Advance Social Acceptance of Well-Sited Wind Projects in New England: A Wind Powering America Success Story. Session I: Opening Plenary: Welcoming Remarks and Overview of New England Wind Project Development Activity

451

Wind Energy Myths; Wind Powering America Fact Sheet Series  

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

wind energy provided the lowest cost of any new generation resource submitted to an Xcel Energy solicitation bidding process (except for one small hydro plant). The commission...

452

Community Renewable Energy Success Stories: Wind Energy in Urban...  

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

Stories: Wind Energy in Urban Environments Webinar (text version) Community Renewable Energy Success Stories: Wind Energy in Urban Environments Webinar (text version) Below is the...

453

Wisconsin Wind Resources | Open Energy Information  

Open Energy Info (EERE)

Wind Resources Wind Resources Jump to: navigation, search Print PDF WIND ENERGY STAKEHOLDER ENGAGEMENT & OUTREACHSmall Wind Guidebook Home WindTurbine-icon.png Small Wind Guidebook * Introduction * First, How Can I Make My Home More Energy Efficient? * Is Wind Energy Practical for Me? * What Size Wind Turbine Do I Need? * What Are the Basic Parts of a Small Wind Electric System? * What Do Wind Systems Cost? * Where Can I Find Installation and Maintenance Support? * How Much Energy Will My System Generate? * Is There Enough Wind on My Site? * How Do I Choose the Best Site for My Wind Turbine? * Can I Connect My System to the Utility Grid? * Can I Go Off-Grid? * State Information Portal * Glossary of Terms * For More Information Wisconsin Wind Resources WisconsinMap.jpg Retrieved from

454

Vantage Wind Energy Center | Open Energy Information  

Open Energy Info (EERE)

Vantage Wind Energy Center Vantage Wind Energy Center Facility Vantage Wind Energy Center Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner Invenergy Developer Invenergy Energy Purchaser Pacific Gas & Electric Co Location East of Ellensburg between Vantage Highway and I90 Coordinates 46.965336°, -120.245204° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":46.965336,"lon":-120.245204,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

455

Wind Energy 101 | Open Energy Information  

Open Energy Info (EERE)

Energy 101 Energy 101 Jump to: navigation, search The 63-MW Dry Lake Wind Power Project in Arizona is the first utility-scale power project. The Salt River Project is purchasing 100% of the power from the Phase I of this project for the next 20 years. Photo from Iberdrola Renewables, NREL 16692 Wind is a form of solar energy and is a result of the uneven heating of the atmosphere by the sun, the irregularities of the earth's surface, and the rotation of the earth. Wind flow patterns and speeds vary greatly across the United States and are modified by bodies of water, vegetation, and differences in terrain. Humans use this wind flow, or motion energy, for many purposes: sailing, flying a kite, and even generating electricity.[1] The following links provide more information about wind energy basics.

456

JD Wind 4 Wind Farm | Open Energy Information  

Open Energy Info (EERE)

4 Wind Farm 4 Wind Farm Jump to: navigation, search Name JD Wind 4 Wind Farm Facility JD Wind 4 Sector Wind energy Facility Type Commercial Scale Wind Facility Status In Service Owner John Deere Wind Developer DWS/John Deere Wind Energy Purchaser Xcel Energy Location Hansford County TX Coordinates 36.398384°, -101.376997° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":36.398384,"lon":-101.376997,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

457

1. Sector Description Wind Energy  

E-Print Network (OSTI)

Wind power is today’s most rapidly growing renewable power source. In the United States, new wind farms were the second-largest source of new power generation in 2005, after new natural gas power plants. In 2005, 2,431 megawatts (MW) of new capacity were installed in 22 states, increasing total wind generating capacity by more than a third to 9,149 MW, or enough to power 2.3 million average American households. Wind energy is a clean, domestic, renewable resource. It often displaces electricity that would otherwise have been produced by natural gas, thus helping to reduce gas demand and limit gas price hikes (DOE 2006a). It also can serve as a partial replacement for the electricity produced by the aging U.S. coal-fired power plant fleet. In the future, surplus wind power can be used for desalination and hydrogen production, and may be stored as hydrogen for use in fuel cells or gas turbines to generate electricity, leveling supply when winds are variable. Last February, the President said that wind energy could provide as much as 20 % of our electricity demands, up from less than 1 % today. Dozens of states have passed renewable portfolio standards setting goals similar to that stated by the President, giving broad-based public support for development of wind resources.

unknown authors

2006-01-01T23:59:59.000Z

458

Cost forecasts: Euyropean International High-Energy Physics facilities - Million Swiss Francs at 1966 prices  

E-Print Network (OSTI)

Cost forecasts: Euyropean International High-Energy Physics facilities - Million Swiss Francs at 1966 prices

ECFA meeting

1966-01-01T23:59:59.000Z

459

Offshore Wind Energy Update  

Wind Powering America (EERE)

wind farms are already operating in 10 countries. Almost 1,700 turbines are in the water. We're probably beyond 5,000 megawatts in nameplate right now and that's just going to...

460

WIND ENERGY Wind Energ. 2013; 16:7790  

E-Print Network (OSTI)

marine energy systems to supply part of the global energy demand. However, there are many advances be achieved by using the existing knowledge and experience from offshore and wind energy industry energy industry lags far behind the wind energy industry, it has the potential to become a role player

Papalambros, Panos

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


461

WREF 2012: THE PAST AND FUTURE COST OF WIND ENERGY  

E-Print Network (OSTI)

Renewable Energy Outlook 2030 – Energy Watch Group GlobalTargets for 2020 and 2030. Brussels, Belgium: European Wind2008). 20% Wind Energy by 2030: Increasing Wind Energy's

Wiser, Ryan

2013-01-01T23:59:59.000Z

462

Nass Wind SAS | Open Energy Information  

Open Energy Info (EERE)

renewable energy holding company, primary involved in the French onshore and offshore wind market as project developers. References Nass & Wind SAS1 LinkedIn...

463

Wind Derivatives: Modeling and Pricing  

Science Conference Proceedings (OSTI)

Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed, the wind direction and the wind duration. This risk ... Keywords: Forecasting, Pricing, Wavelet networks, Weather derivatives, Wind derivatives

A. Alexandridis; A. Zapranis

2013-03-01T23:59:59.000Z

464

The Value of Seasonal Climate Forecasts in Managing Energy Resources  

Science Conference Proceedings (OSTI)

Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the ...

Edith Brown Weiss

1982-04-01T23:59:59.000Z

465

Definition: Wind power | Open Energy Information  

Open Energy Info (EERE)

Wind power Wind power Jump to: navigation, search Dictionary.png Wind power The amount of power available to a wind turbine depends on: air density, wind speed and the swept area of the rotor. While the power is proportional to air density and swept area, it varies with the cube of wind speed, so small changes in wind speed can have a relatively large impact on wind power.[1] View on Wikipedia Wikipedia Definition Wind power is the conversion of wind energy into a useful form of energy, such as using wind turbines to make electrical power, windmills for mechanical power, windpumps for water pumping or drainage, or sails to propel ships. Large wind farms consist of hundreds of individual wind turbines which are connected to the electric power transmission network. Offshore