WorldWideScience

Sample records for energy forecasting lief

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1992-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1992-02-01

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

  3. Guidelines for forecasting energy demand

    International Nuclear Information System (INIS)

    Sonino, T.

    1976-11-01

    Four methodologies for forecasting energy demand are reviewed here after considering the role of energy in the economy and the analysis of energy use in different economic sectors. The special case of Israel is considered throughout, and some forecasts for energy demands in the year 2000 are presented. An energy supply mix that may be considered feasible is proposed. (author)

  4. Global Energy Forecasting Competition 2012

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2014-01-01

    The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...... on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish...

  5. Energy forecasts, perspectives and methods

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, J E; Mogren, A

    1984-01-01

    The authors have analyzed different methods for long term energy prognoses, in particular energy consumption forecasts. Energy supply and price prognoses are also treated, but in a less detailed manner. After defining and discussing the various methods/models used in forecasts, a generalized discussion of the influence on the prognoses from the perspectives (background factors, world view, norms, ideology) of the prognosis makers is given. Some basic formal demands that should be asked from any rational forecast are formulated and discussed. The authors conclude that different forecasting methodologies are supplementing each other. There is no best method, forecasts should be accepted as views of the future from differing perspectives. The primary prognostic problem is to show the possible futures, selecting the wanted future is a question of political process.

  6. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  7. Can energy forecasts be improved?

    International Nuclear Information System (INIS)

    Rech, O.; Alban, P.

    2000-01-01

    Within the present day context of energy, characterized by the gap between short term trends and long term risks, forecasting takes on particular interest. We based our study on the evaluation of the results of some of these long term (2020) and very long term (2050) forecasts. This article looks at the overall demand for energy, whereas the evolution of each primary energy will be handled in a future article. We are restricting our analysis to a global level despite the inherent limitations of such a choice. Our approach mainly concentrates on the dynamics of the phenomena. Thus, we have noticed a simultaneous slowing down since the 1960's of the demography, economy and energy. The revenue and energy consumption per capita do not elude this tendency. At the same time, energy production leads a steep downward tendency. All in all, the forecasts have a tendency to conflict more or less with these changes. In the majority of the scenarios the anticipated rhythms of economic change and energy consumption would indicate a sudden and abrupt inverse of current dynamics. We have noticed that the single use of the average annual rate of change is insufficient to clearly present the long term tendencies that follow curved and not linear paths. Diagnostic errors made in past analyses are likely to affect the models for forecasting, for which the inferred dynamics have not been fully apprehended

  8. Automation of energy demand forecasting

    Science.gov (United States)

    Siddique, Sanzad

    Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

  9. Energy reference forecast for 2014

    International Nuclear Information System (INIS)

    Schlesinger, Michael; Lutz, Christian

    2014-01-01

    The German Federal Ministry for Economic Affairs and Energy has commissioned three reputed institutions to prepare an energy reference forecast as well as a target scenario up to the year 2050. The results of this survey evidence a substantial need for political action if the goals of the Federal Government's energy concept are to be achieved as planned. In view of the wide range of interests among the players involved as well as the complexity of the demands facing the political leadership from diverse areas of life it appears unlikely that the targets laid down in the energy concept can be realised.

  10. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  11. Improving weather forecasts for wind energy applications

    Science.gov (United States)

    Kay, Merlinde; MacGill, Iain

    2010-08-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  12. Improving weather forecasts for wind energy applications

    International Nuclear Information System (INIS)

    Kay, Merlinde; MacGill, Iain

    2010-01-01

    Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

  13. monthly energy consumption forecasting using wavelet analysis

    African Journals Online (AJOL)

    User

    ABSTRACT. Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electric- ity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet ...

  14. Energy reference forecast for 2014; Energiereferenzprognose 2014

    Energy Technology Data Exchange (ETDEWEB)

    Schlesinger, Michael [Prognos AG, Basel (Switzerland). Energie, Infrastruktur; Lindenberger, Dietmar [Koeln Univ. (Germany). Energiewirtschaftliches Inst. (EWI); Lutz, Christian [GWS mbH, Osnabrueck (Germany). Energie und Klima

    2014-10-15

    The German Federal Ministry for Economic Affairs and Energy has commissioned three reputed institutions to prepare an energy reference forecast as well as a target scenario up to the year 2050. The results of this survey evidence a substantial need for political action if the goals of the Federal Government's energy concept are to be achieved as planned. In view of the wide range of interests among the players involved as well as the complexity of the demands facing the political leadership from diverse areas of life it appears unlikely that the targets laid down in the energy concept can be realised.

  15. Energy Consumption Forecasting for University Sector Buildings

    Directory of Open Access Journals (Sweden)

    Khuram Pervez Amber

    2017-10-01

    Full Text Available Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type. A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed.

  16. Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

    International Nuclear Information System (INIS)

    Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

    2009-01-01

    The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

  17. Energy Forecasts and their Attendant Risks

    International Nuclear Information System (INIS)

    Greggio, Rodolphe; Maffei, Benoit

    2017-01-01

    As Jean-Marie Chevalier stresses in this issue, it is currently quite tricky to pronounce on how energy prices will move over time or to predict how energy production systems will change. Further support for that view comes from this article by Rodolphe Greggio and Benoit Maffei. They have looked into the way long-term energy forecasts are made and their conclusion is that, as things stand, they are doomed to fail. The main underlying reason for this is the difficulty of making reliable predictions about how energy demand will evolve, since it is the product of exogenous developments that are unknowable in the long term (demographic growth, economic growth, productivity, energy efficiency etc.). A number of forecasting errors with regard to technological breaks have also played a role: a 'golden age' for natural gas was forecast too early; the date of 'peak oil' has shifted around wildly; a peak with regard to ore deposits has not had the impact originally anticipated; and the decline of nuclear power has turned out to require qualification. Ultimately, all this is the product of various political and geopolitical factors linked to the respective energy assets of the different countries and their strategies for achieving energy independence: e.g. the USA and shale gas, France and nuclear power, Germany and renewables. Quite clearly, the current context suggests that a gradual transition from carbon-based to renewable energies is the order of the day, but it is far from easy to predict on precisely what timescale, in what proportions and on what geographical scale this might occur. (authors)

  18. Probabilistic Forecasting of the Wave Energy Flux

    DEFF Research Database (Denmark)

    Pinson, Pierre; Reikard, G.; Bidlot, J.-R.

    2012-01-01

    Wave energy will certainly have a significant role to play in the deployment of renewable energy generation capacities. As with wind and solar, probabilistic forecasts of wave power over horizons of a few hours to a few days are required for power system operation as well as trading in electricit......% and 70% in terms of Continuous Rank Probability Score (CRPS), depending upon the test case and the lead time. It is finally shown that the log-Normal assumption can be seen as acceptable, even though it may be refined in the future....

  19. Energy forecast. Final report; Energiudsigten. Slutrapport

    Energy Technology Data Exchange (ETDEWEB)

    2010-04-15

    A number of instruments, i.e. Internet, media campaigns, boxes displaying electricity prices (SEE1) and spot contract has been tested for households to shift their electricity consumption to times when prices are low. Of the implemented media campaigns, only the daily viewing of Energy forecast on TV had an impact. Consumers gained greater knowledge of electricity prices and electricity consumption loads, but only showed little interest in shifting electricity consumption. However, a measurable effect appeared at night with the group that had both concluded a spot contract and received an SEE1. These factors increase the awareness of the price of electricity and the possibility of shifting electricity consumption. (Energy 10)

  20. Forecast errors in IEA-countries' energy consumption

    DEFF Research Database (Denmark)

    Linderoth, Hans

    2002-01-01

    Every year Policy of IEA Countries includes a forecast of the energy consumption in the member countries. Forecasts concerning the years 1985,1990 and 1995 can now be compared to actual values. The second oil crisis resulted in big positive forecast errors. The oil price drop in 1986 did not have...... the small value is often the sum of large positive and negative errors. Almost no significant correlation is found between forecast errors in the 3 years. Correspondingly, no significant correlation coefficient is found between forecasts errors in the 3 main energy sectors. Therefore, a relatively small...

  1. Net load forecasting for high renewable energy penetration grids

    International Nuclear Information System (INIS)

    Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

    2016-01-01

    We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

  2. 3D characterisation of the gaseous and liquid phase using laser-induced exciplex fluorescence (LIEF) tomography; Dreidimensionale Charakterisierung der Gas- und Fluessigphase mittels laserinduzierter Exciplexfluoreszenz (LIEF) Tomografie

    Energy Technology Data Exchange (ETDEWEB)

    Rogler, P.; Grzeszik, R.; Arndt, S. [Robert Bosch GmbH, Stuttgart (Germany); Waidmann, C. [Fachhochschule Aalen (Germany); Aigner, M. [DLR, Stuttgart (Germany). Inst. fuer Verbrennungstechnik

    2007-07-01

    The quality of mixture formation in gasoline engines has a significant influence on combustion, emissions and mileage. The measurement technique used for mixture formation analysis of both liquid and vapor phase is laser induced exciplex fluorescence (LIEF), where the aromatics TMPD and naphthalene are added to a non-fluorescing model fuel free of aromatic compounds. For spatially high-resolved measurements of liquid and vapor phase this technique was extended from planar to volume information via tomography. This new approach requires fundamental studies of tracer behavior and new evaluation algorithms for reliable signal interpretation of fluid dynamics in sprays. Using this measurement technique GDI sprays are investigated at a typical stratified engine operation point at part load. For the different injector types, e.g. multi hole and annular orifice injector, an optimal illumination is crucial. With the collected data the spray volume can be calculated, mixture homogeneity can be evaluated and, using a few assumptions, the air/fuel ratio {lambda} can be computed. (orig.)

  3. Forecast of useful energy for the TIMES-Norway model

    International Nuclear Information System (INIS)

    Rosenberg, Eva

    2012-01-01

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

  4. Forecast of useful energy for the TIMES-Norway model

    Energy Technology Data Exchange (ETDEWEB)

    Rosenberg, Eva

    2012-07-25

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

  5. Dynamical dark energy: Current constraints and forecasts

    Science.gov (United States)

    Upadhye, Amol; Ishak, Mustapha; Steinhardt, Paul J.

    2005-09-01

    We consider how well the dark energy equation of state w as a function of redshift z will be measured using current and anticipated experiments. We use a procedure which takes fair account of the uncertainties in the functional dependence of w on z, as well as the parameter degeneracies, and avoids the use of strong prior constraints. We apply the procedure to current data from the Wilkinson Microwave Anisotropy Probe, Sloan Digital Sky Survey, and the supernova searches, and obtain results that are consistent with other analyses using different combinations of data sets. The effects of systematic experimental errors and variations in the analysis technique are discussed. Next, we use the same procedure to forecast the dark energy constraints achievable by the end of the decade, assuming 8 years of Wilkinson Microwave Anisotropy Probe data and realistic projections for ground-based measurements of supernovae and weak lensing. We find the 2σ constraints on the current value of w to be Δw0(2σ)=0.20, and on dw/dz (between z=0 and z=1) to be Δw1(2σ)=0.37. Finally, we compare these limits to other projections in the literature. Most show only a modest improvement; others show a more substantial improvement, but there are serious concerns about systematics. The remaining uncertainty still allows a significant span of competing dark energy models. Most likely, new kinds of measurements, or experiments more sophisticated than those currently planned, are needed to reveal the true nature of dark energy.

  6. Power and energy balances. Forecast 2008

    International Nuclear Information System (INIS)

    2005-01-01

    Both the energy and power balance in 2008 is slightly better than the former Nordel estimate for 2007. This is due to additional investments in new generation capacity, new interconnections of total 1 000 MW to outside Nordel and reduced demand forecast in Sweden. The Nordic electricity system is able to meet the estimated consumption and the corresponding typical power demand pattern in average conditions. In long term the market is expected to maintain a reasonable balance between supply, imports and demand. Lower precipitation or colder temperature result in higher market prices that give incentives for increased imports, demand response and investments. This is expected to maintain the balance between supply and demand in the short and long term even in extreme situations. Allocation between imports and demand response in reality depends on the prevailing market prices and available generation resources outside Nordel. The interconnection capacities are expected to enable import volumes that can meet the increased peak demand. Some Nordic areas can be exposed to a risk for rationing or other measures because of extremely low precipitation. Nordic transmission capacities may prevent full utilization of Nordic thermal power in certain areas. The planned reinforcements in the 'five prioritised cross-sections' will improve the situation. The power balance and the internal bottlenecks in the continental Europe can have an effect on the import possibilities to the Nordic countries. The annual energy consumption in the Nordic market is estimated to grow by 20 TWh by year 2008 (1.2%la) from 395 TWh in 2004 (temperature corrected). In the three year period investments in power generation is expected to increase the available generation capacity and capability by 1500 MW and 10 TWhla in average conditions. Iceland is not included in the figures. The annual energy consumption in Iceland is estimated to grow by about 6.8 TWh by year 2008 (15 %la) due to two new aluminium

  7. Scenarios for energy forecasting: papers of the symposium

    International Nuclear Information System (INIS)

    1987-01-01

    Energy planning is important for every developed country and therefore also for South Africa. However, during 1984 it was felt by interested parties that the work in this field should be coordinated through mutual discussion. With this in mind a 'Task Team for Energy Forecasting' was formed with the task to generate acceptable forecasts of the energy set-up in South Africa. Knowledge of the relationship between energy and variables such as the economy and the population is necessary to the Task Team. However, the Task Team also needs some insight into the future paths of such variables if it has to generate energy forecasts. It is the purpose of this symposium to improve this insight through having experts in all relevant fields to set out and develop their possible future scenarios independently of energy forecasting

  8. Research on energy supply, demand and economy forecasting in Japan

    International Nuclear Information System (INIS)

    Shiba, Tsuyoshi; Kamezaki, Hiroshi; Yuyama, Tomonori; Suzuki, Atsushi

    1999-10-01

    This project aims to do research on forecasts of energy demand structure and electricity generation cost in each power plant in Japan in the 21st century, considering constructing successful FBR scenario. During the process of doing research on forecasts of energy demand structure in Japan, documents published from organizations in inside and outside of Japan were collected. These documents include prospects of economic growth rate, forecasts of amount for energy supply and demand, the maximum amount of introducing new energy resources, CO2 regulation, and evaluation of energy best mixture. Organizations in Japan such as Economic Council and Japan Energy Economic Research Institute have provided long-term forecasts until the early 21st century. Meanwhile, organizations overseas have provided forecasts of economic structure, and demand and supply for energy in OECD and East Asia including Japan. In connection with forecasts of electricity generation cost in each power plant, views on the ultimate reserves and cost of resources are reviewed in this report. According to some views on oil reserves, making assumptions based on reserves/production ratio, the maximum length of the time that oil reserves will last is 150 years. In addition, this report provides summaries of cost and potential role of various resources, including solar energy and wind energy; and views on waste, safety, energy security-related externality cost, and the price of transferring CO2 emission right. (author)

  9. Forecast of the energy final consumption for Minas Gerais State

    International Nuclear Information System (INIS)

    Almeida, P.E.F. de; Bechtlufft, P.C.T.; Araujo, M.E.A.; Vasconcelos, E.C.; Las Casas, H.B. de; Monteiro, M.A.G.

    1990-01-01

    This paper is included among the activities of the Energy Planning of Minas Gerais State and presents a forecast of the energy final consumption for the State up to year 2010. Two Scenarios are presented involving brazilian economy's evolution, the State's demography and its sectors: residential, services, transportation, agriculture and cattle-breeding and industry. Finally, it shows two forecast on energy final consumption for Minas Gerais State. (author)

  10. Wave energy potential: A forecasting system for the Mediterranean basin

    International Nuclear Information System (INIS)

    Carillo, Adriana; Sannino, Gianmaria; Lombardi, Emanuele

    2015-01-01

    ENEA is performing ocean wave modeling activities with the aim of both characterizing the Italian sea energy resource and providing the information necessary for the experimental at sea and operational phases of energy converters. Therefore a forecast system of sea waves and of the associated energy available has been developed and has been operatively running since June 2013. The forecasts are performed over the entire Mediterranean basin and, at a higher resolution, over ten sub-basins around the Italian coasts. The forecast system is here described along with the validation of the wave heights, performed by comparing them with the measurements from satellite sensors. [it

  11. Mid-term report on Renewable Energy Forecasting System

    International Nuclear Information System (INIS)

    Brand, A.J.; Hegberg, T.; Van der Borg, N.J.C.M.; Kok, J.K.; Van Selow, E.R.; Kamphuis, I.G.; De Noord, M.; Van Sambeek, E.J.W.

    2001-04-01

    The most important conclusions on the economical and technical feasibility of renewable energy forecasting systems are presented, next to recommendations to be followed in order to introduce such a system in the Dutch electricity market. 11 refs

  12. The Value of Seasonal Climate Forecasts in Managing Energy Resources.

    Science.gov (United States)

    Brown Weiss, Edith

    1982-04-01

    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 management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

  13. Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2013-09-01

    Full Text Available The small medium large system (SMLsystem is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs, which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC—heating, ventilation and air conditioning—system consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%–38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system.

  14. Forecast of wind energy production and ensuring required balancing power

    International Nuclear Information System (INIS)

    Merkulov, M.

    2010-01-01

    The wind energy is gaining larger part of the energy mix around the world as well as in Bulgaria. Having in mind the irregularity of the wind, we are in front of a challenge for management of the power grid in new unknown conditions. The world's experience has proven that there could be no effective management of the grid without forecasting tools, even with small scale of wind power penetration. Application of such tools promotes simple management of large wind energy production and reduction of the quantities of required balancing powers. The share of the expenses and efforts for forecasting of the wind energy is incomparably small in comparison with expenses for keeping additional powers in readiness. The recent computers potential allow simple and rapid processing of large quantities of data from different sources, which provides required conditions for modeling the world's climate and producing sophisticated forecast. (author)

  15. Application and verification of ECMWF seasonal forecast for wind energy

    Science.gov (United States)

    Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

    2015-04-01

    A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

  16. Energy demand forecasting method based on international statistical data

    International Nuclear Information System (INIS)

    Glanc, Z.; Kerner, A.

    1997-01-01

    Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs

  17. Energy demand forecasting method based on international statistical data

    Energy Technology Data Exchange (ETDEWEB)

    Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)

    1997-09-01

    Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.

  18. The Application of TAPM for Site Specific Wind Energy Forecasting

    Directory of Open Access Journals (Sweden)

    Merlinde Kay

    2016-02-01

    Full Text Available The energy industry uses weather forecasts for determining future electricity demand variations due to the impact of weather, e.g., temperature and precipitation. However, as a greater component of electricity generation comes from intermittent renewable sources such as wind and solar, weather forecasting techniques need to now also focus on predicting renewable energy supply, which means adapting our prediction models to these site specific resources. This work assesses the performance of The Air Pollution Model (TAPM, and demonstrates that significant improvements can be made to only wind speed forecasts from a mesoscale Numerical Weather Prediction (NWP model. For this study, a wind farm site situated in North-west Tasmania, Australia was investigated. I present an analysis of the accuracy of hourly NWP and bias corrected wind speed forecasts over 12 months spanning 2005. This extensive time frame allows an in-depth analysis of various wind speed regimes of importance for wind-farm operation, as well as extreme weather risk scenarios. A further correction is made to the basic bias correction to improve the forecast accuracy further, that makes use of real-time wind-turbine data and a smoothing function to correct for timing-related issues. With full correction applied, a reduction in the error in the magnitude of the wind speed by as much as 50% for “hour ahead” forecasts specific to the wind-farm site has been obtained.

  19. Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

    OpenAIRE

    Huang, Junbing; Tang, Yuee; Chen, Shuxing

    2018-01-01

    Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to ...

  20. Wind Energy: Forecasting Challenges for its Operational Management

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2013-01-01

    with the generation of forecasts tailored to the various operational decision problems involved. Indeed, while wind energy may be seen as an environmentally friendly source of energy, full benefits from its usage can only be obtained if one is able to accommodate its variability and limited predictability. Based...... on a short presentation of its physical basics, the importance of considering wind power generation as a stochastic process is motivated. After describing representative operational decision-making problems for both market participants and system operators, it is underlined that forecasts should be issued...

  1. Energy production forecasting, experiences from Lillgrund. Lillgrund Pilot Project

    Energy Technology Data Exchange (ETDEWEB)

    Johansson, Lasse; Schelander, Peter; Haakansson, Maans; Hansson, Johan (Vattenfall Vindkraft AB, Stockholm (Sweden))

    2010-01-15

    Forecasts of energy production at Lillgrund are being made with the prediction tool WPPT. The forecasts are updated every hour with observed wind- and production data. WPPT combines statistical and physical methods and the nature of the model changes with time. In the very short range, the observed data is the dominant factor predicting energy production while the physical methods, e.g. the weather forecasts, gradually are given more weight as we go further away from the production hour. Until recently Vattenfall has relied solely on weather forecasts from one institute, namely DMI (The Danish Meteorological Institute), in predicting the energy produced at Lillgrund. The uncertainty in the forecast has been given some attention but since only one source of information has been available the possibilities of a comprehensive uncertainty analysis has been limited. To meet the growing demand for quality and delivery reliability, Vattenfall has begun to purchase additional weather data from the Swedish supplier WeatherTech Scandinavia. These data will be used together with data from DMI. You get a kind of ensemble forecast approach. The difference in structure, configuration and physical approaches of the models presumably makes the model related forecast errors uncorrelated. This lays the path for quality improvements when the different forecasts are combined optimally. WPPT has been used in forecasting the energy production at Lillgrund since production began in 2007. The average absolute error in the production forecast / turbine is 0.17 MW. If WPPT only relied on a persistence forecast for the next 24 hours the error will become almost three times as high. So far WPPT has a skill score of 86% in the 24-hour forecasts compared to an assumption of persistence. There is a clearly visible pattern that WPPT underestimates production in situations with strong winds and conversely overestimate production when winds are weak. This is also typical for pure persistence

  2. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  3. Forecasting US renewables in the national energy modelling system

    International Nuclear Information System (INIS)

    Diedrich, R.; Petersik, T.W.

    2001-01-01

    The Energy information Administration (EIA) of the US Department of Energy (DOE) forecasts US renewable energy supply and demand in the context of overall energy markets using the National Energy Modelling System (NEMS). Renewables compete with other supply and demand options within the residential, commercial, industrial, transportation, and electricity sectors of the US economy. NEMS forecasts renewable energy for grid-connected electricity production within the Electricity Market Module (EM), and characterizes central station biomass, geothermal, conventional hydroelectric, municipal solid waste, solar thermal, solar photovoltaic, and wind-powered electricity generating technologies. EIA's Annual Energy Outlook 1998, projecting US energy markets, forecasts marketed renewables to remain a minor part of US energy production and consumption through to 2020. The USA is expected to remain primarily a fossil energy producer and consumer throughout the period. An alternative case indicates that biomass, wind, and to some extent geothermal power would likely increase most rapidly if the US were to require greater use of renewables for power supply, though electricity prices would increase somewhat. (author)

  4. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors

    International Nuclear Information System (INIS)

    Liu, Xiuli; Moreno, Blanca; García, Ana Salomé

    2016-01-01

    A combined forecast of Grey forecasting method and neural network back propagation model, which is called Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO model), is proposed. A real case of energy consumption forecast is used to validate the effectiveness of the proposed model. The GNF-IO model predicts coal, crude oil, natural gas, renewable and nuclear primary energy consumption volumes by Spain's 36 sub-sectors from 2010 to 2015 according to three different GDP growth scenarios (optimistic, baseline and pessimistic). Model test shows that the proposed model has higher simulation and forecasting accuracy on energy consumption than Grey models separately and other combination methods. The forecasts indicate that the primary energies as coal, crude oil and natural gas will represent on average the 83.6% percent of the total of primary energy consumption, raising concerns about security of supply and energy cost and adding risk for some industrial production processes. Thus, Spanish industry must speed up its transition to an energy-efficiency economy, achieving a cost reduction and increase in the level of self-supply. - Highlights: • Forecasting System Using Grey Models combined with Input-Output Models is proposed. • Primary energy consumption in Spain is used to validate the model. • The grey-based combined model has good forecasting performance. • Natural gas will represent the majority of the total of primary energy consumption. • Concerns about security of supply, energy cost and industry competitiveness are raised.

  5. Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks.

    Science.gov (United States)

    Wang, Xue; Ma, Jun-Jie; Ding, Liang; Bi, Dao-Wei

    2007-11-15

    An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.

  6. The SEEC United Kingdom energy demand forecast (1993-2000)

    Energy Technology Data Exchange (ETDEWEB)

    Fouquet, R; Hawdon, D; Pearson, P; Robinson, C; Stevens, P

    1993-12-16

    The aims of this paper are to present the underlying determinants of fuel consumption, such as economic activity and prices, develop a series of simple yet reliable sectoral models of energy demand, which incorporate recent modelling developments; provide forecasts of energy demand and its environmental consequences; examine the effects of VAT on domestic fuel and increased competition in the electricity sector; and aid the present debate on energy markets. The paper analyses world oil prices, with a particular focus on Iraq's role, reviews energy policy in the UK and discusses SEEC's expectations about UK fuel prices in coming years and how they vary among sectors. It forecasts final user demand in the domestic, iron and steel, other industry, transport, agricultural, public administration and defence and miscellaneous sectors. The paper also examines the major changes that are underway in electricity generators' demand for fuel, and primary energy consumption and its environmental implications.

  7. A forecast of energy requirements in South Africa

    International Nuclear Information System (INIS)

    Kotze, D.J.

    1975-01-01

    The aim of this paper is to evaluate the adequacy of South Africa's energy resources relative to projected demands. The forecasting procedure embraces the construction of suitable energy balances and the development of econometric demand models. An energy balance is employed which integrates supply and demand data on all forms of energy for a particular year. The demand side of the balance is divided into both final demand and demand by the conversion sector. Useful energy consumption in each sector is estimated by applying utilisation efficiency co-efficients to the physics energy content of each energy form. Total final demand is determined by developing sub-models for each sector of final demand including households, industry, mining and transport. In these sub-models, economic series representing the type of activity in the particular sub-sector, are used as explanatory variables. Further relationships, quantifying the contributions of each form of energy to the sectorial totals, are constructed. Having established the future value of final useful energy demand, total future production and final consumption is obtained. The forecast of primary energy requirements is therefore made via a reversed calculation from the final energy demand through all conversion processes to the primary energy stage. Once the future distribution of energy by source, form and end use sector is known it is possible to plan the optimum allocation of energy resources in the country. It is also possible to evaluate the life of indigenous energy resources, their adequacy, and import requirements

  8. Photovoltaics (PV System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions

    Directory of Open Access Journals (Sweden)

    Kristijan Brecl

    2018-05-01

    Full Text Available When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s. While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertainty of the results is relatively high. However, in the presented approach, irradiance is calculated from discrete weather conditions and with correlation of forecasted meteorological data, an RMS error of 65%, and a R2 correlation factor of 0.85 is feasible.

  9. Forecasting forest chip energy production in Finland 2008-2014

    International Nuclear Information System (INIS)

    Linden, Mikael

    2011-01-01

    Energy policy measures aim to increase energy production from forest chips in Finland to 10 TWh by year 2010. However, on the regional level production differences are large, and the regional estimates of the potential base of raw materials for the production of forest chips are heterogeneous. In order to analyse the validity of the above target, two methods are proposed to derive forecasts for region-level energy production from forest chips in Finland in the years 2008-2014. The plant-level data from 2003-2007 gives a starting point for a detailed statistical analysis of present and future region-level forest chip production. Observed 2008 regional levels are above the estimated prediction 95% confidence intervals based on aggregation of plant-level time averages. A simple time trend model with fixed-region effects provides accurate forecasts for the years 2008-2014. Forest chip production forecast confidence intervals cover almost all regions for the 2008 levels and the estimates of potential production levels for 2014. The forecast confidence intervals are also derived with re-sampling methods, i.e. with bootstrap methods, to obtain more reliable results. Results confirm that a general materials shortfall is not expected in the near future for forest chip energy production in Finland.

  10. Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Yang, Rui [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Kaiqing [University of Illinois Urbana-Champaign; Zhang, Jun Jason [University of Denver

    2017-08-17

    For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

  11. Forecasting of the energy consumption; Zamke prognoziranja potrosnje energije

    Energy Technology Data Exchange (ETDEWEB)

    Hill, Z [Zagreb (Croatia)

    1997-12-31

    Urged by earlier continuous failures in forecasting the consumption of energy in the world, essentially characterized by megalomania, the author presents his views on causes of such occurrences. Fundamental cause is considered - logic of a circle - insensitive to social and economic effects on the humanity in general and particularly to the energy consumption. Besides, a lethal influence of voluntarism has been specially studied as well. (author). 13 refs.

  12. Energy and electricity demand forecasting for nuclear power planning in developing countries

    International Nuclear Information System (INIS)

    1988-07-01

    This Guidebook is designed to be a reference document to forecast energy and electricity demand. It presents concepts and methodologies that have been developed to make an analytical approach to energy/electricity demand forecasting as part of the planning process. The Guidebook is divided into 6 main chapters: (Energy demand and development, energy demand analysis, electric load curve analysis, energy and electricity demand forecasting, energy and electricity demand forecasting tools used in various organizations, IAEA methodologies for energy and electricity demand forecasting) and 3 appendices (experience with case studies carried out by the IAEA, reference technical data, reference economic data). A bibliography and a glossary complete the Guidebook. Refs, figs and tabs

  13. Energy price forecast by market analysis

    International Nuclear Information System (INIS)

    Jongepier, A.G.

    2000-01-01

    A power trader benefits from accurate price predictions. Based on market analyses, KEMA Connect has developed - in cooperation with Essent Energy Trading - a market model, enhancing the insight into market operation and one's own actions and thus resulting in accurate price predictions

  14. Advancing solar energy forecasting through the underlying physics

    Science.gov (United States)

    Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

    2017-12-01

    As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

  15. Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

    Science.gov (United States)

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  16. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China: A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    Directory of Open Access Journals (Sweden)

    Xiuli Zhao

    2014-01-01

    Full Text Available The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

  17. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-03-28

    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.

  18. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P D [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1996-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  19. An improved market penetration model for wind energy technology forecasting

    International Nuclear Information System (INIS)

    Lund, P.D.

    1995-01-01

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  20. Energy Forecasting Models Within the Department of the Navy.

    Science.gov (United States)

    1982-06-01

    standing the climatic conditions responsible for the results. Both models have particular advantages in parti- cular applications and will be examined...and moving average processes. A similar notation for a model with seasonality . .- considerations will be ARIMA (p d j)(P Q) 3=12, where the upper...AD-A12l 950 ENERGY FORECASTING MODELS WITHIN THE DEPARTMENT OF THE 1/4 NAYY(U) NAVAL POSTGRADUATE SCHOOL MONTEREY CA L &I BUTTOIPH JUN 82

  1. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  2. Episode forecasting in bipolar disorder: Is energy better than mood?

    Science.gov (United States)

    Ortiz, Abigail; Bradler, Kamil; Hintze, Arend

    2018-01-22

    Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders. We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series. Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically. Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. A Smart Forecasting Approach to District Energy Management

    Directory of Open Access Journals (Sweden)

    Baris Yuce

    2017-07-01

    Full Text Available This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs (parallel ANNs based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA and Multiple Regression Analysis (MRA methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

  4. Structural change and forecasting long-run energy prices

    International Nuclear Information System (INIS)

    Bernard, J.T.; Khalaf, L.

    2004-01-01

    Fluctuating energy prices have a significant impact on the economies of industrialized nations. A recent study has shown a strong non-linear relationship between changes in oil prices and growth in gross domestic product (GDP). In order to forecast the behaviour of energy prices, a complete model must take into account domestic and international supply and demand conditions, market regulations, technological advances and geopolitics. In 1999, Pindyck suggested that for long-term forecasting, a simple model should be adopted where prices grow in real terms and at a fixed rate. This paper tests the statistical significance of Pindyck's suggested class of econometric equations that model the behaviour of long-run real energy prices. The models assume mean-reverting prices with continuous and random changes in their level and trend. They are estimated using Kalman filtering. The authors used simulation-based procedures to address the issue of non-standard test statistics and nuisance parameters. Results were reported for a standard Monte Carlo test and a maximized Monte Carlo test. Results shown statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Various models were differentiated using out-of-sample forecasting exercises. 25 refs., 3 tabs

  5. An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

    OpenAIRE

    Syed Aziz Ur Rehman; Yanpeng Cai; Rizwan Fazal; Gordhan Das Walasai; Nayyar Hussain Mirjat

    2017-01-01

    Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fo...

  6. Forecasting energy demand and CO{sub 2}-emissions from energy production in the forest industry

    Energy Technology Data Exchange (ETDEWEB)

    Malinen, H

    1998-12-31

    The purpose of this study was to develops new energy forecasting methods for the forest industry energy use. The scenarios have been the most commonly used forecasts, but they require a lot of work. The recent scenarios, which are made for the forest industry, give a wide range of results; e.g. from 27,8 TWh to 38 TWh for electricity use in 2010. There is a need for more simple and accurate methods for forecasting. The time scale for the study is from 1975 to 2010, i.e. 36 years. The basic data for the study is collected from time period 1975 - 1995. It includes the wood use, production of main product categories and energy use in the forest industry. The factors affecting energy use at both industry level and at mill level are presented. The most probable technology trends, which can have an effect on energy production and use and CO{sub 2}-emissions are studied. Recent forecasts for the forest industry energy use till the year 2010 are referred and analysed. Three alternative forecasting methods are studied more closely. These methods are (a) Regression analysis, (b) Growth curves and (c) Delphi-method. Total electricity demand, share of purchased electricity, total fuel demand and share of process-based biofuels are estimated for the time period 1996 - 2010. The results from the different methods are compared to each other and to the recent scenarios. The comparison is made for the results concerning the energy use and the usefulness of the methods in practical work. The average energy consumption given by the forecasts for electricity was 31,6 TWh and for fuel 6,2 Mtoe in 2010. The share of purchased electricity totalled 73 % and process based fuels 77 %. The figures from 1995 are 22,8 TWh, 5,5 Mtoe, 64 % and 68 % respectively. All three methods were suitable for forecasting. All the methods required less working hours and were easier to use than scenarios. The methods gave results with a smaller deviation than scenarios, e.g. with electricity use in 2010 from

  7. Forecasting energy demand and CO{sub 2}-emissions from energy production in the forest industry

    Energy Technology Data Exchange (ETDEWEB)

    Malinen, H.

    1997-12-31

    The purpose of this study was to develops new energy forecasting methods for the forest industry energy use. The scenarios have been the most commonly used forecasts, but they require a lot of work. The recent scenarios, which are made for the forest industry, give a wide range of results; e.g. from 27,8 TWh to 38 TWh for electricity use in 2010. There is a need for more simple and accurate methods for forecasting. The time scale for the study is from 1975 to 2010, i.e. 36 years. The basic data for the study is collected from time period 1975 - 1995. It includes the wood use, production of main product categories and energy use in the forest industry. The factors affecting energy use at both industry level and at mill level are presented. The most probable technology trends, which can have an effect on energy production and use and CO{sub 2}-emissions are studied. Recent forecasts for the forest industry energy use till the year 2010 are referred and analysed. Three alternative forecasting methods are studied more closely. These methods are (a) Regression analysis, (b) Growth curves and (c) Delphi-method. Total electricity demand, share of purchased electricity, total fuel demand and share of process-based biofuels are estimated for the time period 1996 - 2010. The results from the different methods are compared to each other and to the recent scenarios. The comparison is made for the results concerning the energy use and the usefulness of the methods in practical work. The average energy consumption given by the forecasts for electricity was 31,6 TWh and for fuel 6,2 Mtoe in 2010. The share of purchased electricity totalled 73 % and process based fuels 77 %. The figures from 1995 are 22,8 TWh, 5,5 Mtoe, 64 % and 68 % respectively. All three methods were suitable for forecasting. All the methods required less working hours and were easier to use than scenarios. The methods gave results with a smaller deviation than scenarios, e.g. with electricity use in 2010 from

  8. Power without manpower: Forecasting labour demand for Estonian energy sector

    International Nuclear Information System (INIS)

    Meriküll, Jaanika; Eamets, Raul; Humal, Katrin; Espenberg, Kerly

    2012-01-01

    As energy demand and prices continue to grow, oil shale might help mitigate the energy crisis—it can widely be found all over the world but so far has not been widely used. Estonia is unique in the world for producing a large majority of energy out of oil shale and has been set as an example in numerous papers covering oil shale deposits, technology etc. This paper is the first to analyse oil shale energy related workforce and provides scenario forecasts of the labour demand for the Estonian energy sector in 2010–2020. The contribution of the paper is twofold. First, the paper provides a valuable insight into oil shale energy related workforce, enabling to take into consideration the educational needs in countries where oil shale industry might be set up. Second, methodology-wise, the paper relates labour demand and supply to different scenarios of energy production capacities. The results illustrate problems related to aging of the workforce in energy production. If the existing trends continue in educational attainment in Estonia, there will be a serious shortage of high-skilled engineering and manufacturing specialists. Our method provides a simple yet reliable enough way to check for such problems early enough. - Highlights: ► This paper analyses oil shale energy related workforce and provides scenario forecasts. ► This is the first study to investigate the workforce related to oil shale energy production. ► The main workforce-related problem in the sector is ageing of the workforce. ► Workers immigrating to the sector during the Soviet times are at the retirement age. ► There will be a serious shortage of engineers for energy sector in the near future.

  9. Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

    Directory of Open Access Journals (Sweden)

    Junbing Huang

    2018-01-01

    Full Text Available Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985–2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

  10. Energy Demand and Supply Analysis and Outlook - Energy Forecast for 2001 and Policy Issues

    Energy Technology Data Exchange (ETDEWEB)

    Na, In Gang; Ryu, Ji Chul [Korea Energy Economics Institute, Euiwang (Korea)

    2000-12-01

    The energy consumption in Korea has grown at impressive rates during the last 3 decades, along with the economic growth. The global concern about the environment issue and the restructuring in Korea energy industry has an effect on the pattern and trend of energy demand in Korea. Under the situation, this research are focusing on the analysis of energy consumption and forecast of energy demand. First of all, we analyze the trends and major characteristics of energy consumption, beginning with 1970s and up to the third quarter of 2000. In the analysis of energy consumption by energy types, we also perform qualitative analysis on the trends and characteristics of each energy types, including institutional analysis. In model section, we start with the brief description of synopsis and outline the survey on empirical models for energy demand. The econometric model used in KEEI's short-term energy forecast is outlined, followed by the result of estimations. The 2001 energy demand forecast is predicted in detail by sectors and energy types. In the year 2001, weak demand is projected to continue through the First Half, and pick up its pace of growth only in the Second Half. Projected total demand is 201.3 million TOE or 4.4% growth. In the last section, the major policy issues are summarized in three sub-sections: the restructuring in energy industry, the security of energy demand and supply, international energy cooperation including south-north energy cooperation. (author). 86 refs., 43 figs., 73 tabs.

  11. Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

    Science.gov (United States)

    Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

    2018-02-01

    In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

  12. Growing an emerging energy workforce: forecasting labour demand and gaining access to emerging energy skills

    International Nuclear Information System (INIS)

    Thomsen, V.

    2006-01-01

    This paper discusses the needs of emerging energies sector in terms of growing an emerging energy workforce, forecasting labour demands and gaining access to emerging energy skills. It will require industrial renewal and innovation and not just selling our resources. It will also require educating ourselves to utilise our own finished products. Conservation is a key element in a sustainable energy future. finally, a market for renewable energy has been established in Canada

  13. EnerFuture: Long Term Energy Scenarios 'Understanding our energy future'. Key graphs and analysis, Enerdata - Global Energy Forecasting

    International Nuclear Information System (INIS)

    2011-01-01

    Enerdata analyses 4 future energy scenarios accounting for 2 economic growth assumptions combined with 2 alternative carbon emission mitigation policies. In this study, a series of analyses supported by graphs assess the energy consumption and intensity forecasts in emerging and developed markets. In particular, one analysis is dedicated to energies competition, including gas, coal and renewable energies. (authors)

  14. The long-run forecasting of energy prices using the model of shifting trend

    International Nuclear Information System (INIS)

    Radchenko, Stanislav

    2005-01-01

    Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To

  15. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  16. Meteorological Forecasting for renewable energy plants. A case study of two energy plants in Spain

    OpenAIRE

    López, Andrés Robalino; Mena-Nieto, Ángel

    2015-01-01

    Energy resources are the engines that drive every economy [1], [4], [14], Therefore, it is necessary to develop their exploitation in a friendlier, environmentally and sustainable way indeed it is a critically needed nowadays. Then, it is necessary to improve efficiency and optimize renewable energy in order that replace polluting energy sources. This work aims to relate the use of forecasting on meteorological variables such as wind speed, wind direction, solar radiation, among others, obtai...

  17. Forecasting of Hourly Photovoltaic Energy in Canarian Electrical System

    Science.gov (United States)

    Henriquez, D.; Castaño, C.; Nebot, R.; Piernavieja, G.; Rodriguez, A.

    2010-09-01

    The Canarian Archipelago face similar problems as most insular region lacking of endogenous conventional energy resources and not connected to continental electrical grids. A consequence of the "insular fact" is the existence of isolated electrical systems that are very difficult to interconnect due to the considerable sea depths between the islands. Currently, the Canary Islands have six isolated electrical systems, only one utility generating most of the electricity (burning fuel), a recently arrived TSO (REE) and still a low implementation of Renewable Energy Resources (RES). The low level of RES deployment is a consequence of two main facts: the weakness of the stand-alone grids (from 12 MW in El Hierro up to only 1 GW in Gran Canaria) and the lack of space to install RES systems (more than 50% of the land protected due to environmental reasons). To increase the penetration of renewable energy generation, like solar or wind energy, is necessary to develop tools to manage them. The penetration of non manageable sources into weak grids like the Canarian ones causes a big problem to the grid operator. There are currently 104 MW of PV connected to the islands grids (Dec. 2009) and additional 150 MW under licensing. This power presents a serious challenge for the operation and stability of the electrical system. ITC, together with the local TSO (Red Eléctrica de España, REE) started in 2008 and R&D project to develop a PV energy prediction tool for the six Canarian Insular electrical systems. The objective is to supply reliable information for hourly forecast of the generation dispatch programme and to predict daily solar radiation patterns, in order to help program spinning reserves. ITC has approached the task of weather forecasting using different numerical model (MM5 and WRF) in combination with MSG (Meteosat Second Generation) images. From the online data recorded at several monitored PV plants and meteorological stations, PV nominal power and energy produced

  18. The energy markets to 1995 - sector demand forecasts and summary. [United Kingdom

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, J

    1983-01-01

    Energy demand forecasts are often based on assumptions which are uncertain and dependent upon both political and economic factors. However, there is a need for long-term energy forecasting for the benefit of industry and commerce. CIRS (Cambridge Information and Research Services Limited) have tried to fulfill this need, based on forecasts of useful heat demand sector by sector which are then converted to heat energy supply and primary requirements. The first such forecast was produced in 1975. This 1983 updated projection examines coal, oil and gas supplies in the UK to the year 1995.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-10-01

    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

  20. How accurate are forecasts of costs of energy? A methodological contribution

    International Nuclear Information System (INIS)

    Siddons, Craig; Allan, Grant; McIntyre, Stuart

    2015-01-01

    Forecasts of the cost of energy are typically presented as point estimates; however forecasts are seldom accurate, which makes it important to understand the uncertainty around these point estimates. The scale of the differences between forecasts and outturns (i.e. contemporary estimates) of costs may have important implications for government decisions on the appropriate form (and level) of support, modelling energy scenarios or industry investment appraisal. This paper proposes a methodology to assess the accuracy of cost forecasts. We apply this to levelised costs of energy for different generation technologies due to the availability of comparable forecasts and contemporary estimates, however the same methodology could be applied to the components of levelised costs, such as capital costs. The estimated “forecast errors” capture the accuracy of previous forecasts and can provide objective bounds to the range around current forecasts for such costs. The results from applying this method are illustrated using publicly available data for on- and off-shore wind, Nuclear and CCGT technologies, revealing the possible scale of “forecast errors” for these technologies. - Highlights: • A methodology to assess the accuracy of forecasts of costs of energy is outlined. • Method applied to illustrative data for four electricity generation technologies. • Results give an objective basis for sensitivity analysis around point estimates.

  1. Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences

    International Nuclear Information System (INIS)

    Mamatzakis, E.; Koutsomanoli-Filippaki, A.

    2014-01-01

    This paper examines the rationality of the price forecasts for energy commodities of the United States Department of Energy's (DOE), departing from the common assumption in the literature that DOE's forecasts are based on a symmetric underlying loss function with respect to positive vs. negative forecast errors. Instead, we opt for the methodology of Elliott et al. (2005) that allows testing the joint hypothesis of an asymmetric loss function and rationality and reveals the underlying preferences of the forecaster. Results indicate the existence of asymmetries in the shape of the loss function for most energy categories with preferences leaning towards optimism. Moreover, we also examine whether there is a structural break in those preferences over the examined period, 1997–2012. - Highlights: • Examine the rationality of DOE energy forecasts. • Departing from a symmetric underlying loss function. • Asymmetries exist in most energy prices. • Preferences lean towards optimism. • Examine structural breaks in those preferences

  2. Very Short-term Nonparametric Probabilistic Forecasting of Renewable Energy Generation - with Application to Solar Energy

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Gooi, Hoay Beng

    2016-01-01

    Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only...... approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power...... generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts...

  3. Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

    Directory of Open Access Journals (Sweden)

    Shailendra Singh

    2018-02-01

    Full Text Available Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM and Multi-Layer Perceptron (MLP at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

  4. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  5. An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

    Directory of Open Access Journals (Sweden)

    Syed Aziz Ur Rehman

    2017-11-01

    Full Text Available Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA, Holt-Winter and Long-range Energy Alternate Planning (LEAP model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16% followed by natural gas (36.57%, electricity (16.22%, coal (7.52% and LPG (1.52% in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

  6. Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

    Science.gov (United States)

    Pinson, Pierre

    2016-04-01

    The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

  7. How important is getting the land surface energy exchange correct in WRF for wind energy forecasting?

    Science.gov (United States)

    Wharton, S.; Simpson, M.; Osuna, J. L.; Newman, J. F.; Biraud, S.

    2013-12-01

    Wind power forecasting is plagued with difficulties in accurately predicting the occurrence and intensity of atmospheric conditions at the heights spanned by industrial-scale turbines (~ 40 to 200 m above ground level). Better simulation of the relevant physics would enable operational practices such as integration of large fractions of wind power into power grids, scheduling maintenance on wind energy facilities, and deciding design criteria based on complex loads for next-generation turbines and siting. Accurately simulating the surface energy processes in numerical models may be critically important for wind energy forecasting as energy exchange at the surface strongly drives atmospheric mixing (i.e., stability) in the lower layers of the planetary boundary layer (PBL), which in turn largely determines wind shear and turbulence at heights found in the turbine rotor-disk. We hypothesize that simulating accurate a surface-atmosphere energy coupling should lead to more accurate predictions of wind speed and turbulence at heights within the turbine rotor-disk. Here, we tested 10 different land surface model configurations in the Weather Research and Forecasting (WRF) model including Noah, Noah-MP, SSiB, Pleim-Xiu, RUC, and others to evaluate (1) the accuracy of simulated surface energy fluxes to flux tower measurements, (2) the accuracy of forecasted wind speeds to observations at rotor-disk heights, and (3) the sensitivity of forecasting hub-height rotor disk wind speed to the choice of land surface model. WRF was run for four, two-week periods covering both summer and winter periods over the Southern Great Plains ARM site in Oklahoma. Continuous measurements of surface energy fluxes and lidar-based wind speed, direction and turbulence were also available. The SGP ARM site provided an ideal location for this evaluation as it centrally located in the wind-rich Great Plains and multi-MW wind farms are rapidly expanding in the area. We found significant differences in

  8. Ensemble and probabilistic forecasting of (u,v)-wind for the energy application

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2011-01-01

    and probabilistic forecasts are becoming increasingly popular among the actors of the power system and electricity markets. The energy application is particularly interesting since covering a variety of decision-making problems requiring different types of input forecasts. A few of them will be reviewed...

  9. Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

    Directory of Open Access Journals (Sweden)

    Xingsheng Gu

    2013-03-01

    Full Text Available he accurate forecasting of carbon dioxide (CO2 emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS algorithm-based discounted mean square forecast error (DMSFE combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE and mean absolute error (MAE are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

  10. A high resolution WRF model for wind energy forecasting

    Science.gov (United States)

    Vincent, Claire Louise; Liu, Yubao

    2010-05-01

    The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the

  11. An Electrical Energy Consumption Monitoring and Forecasting System

    Directory of Open Access Journals (Sweden)

    J. L. Rojas-Renteria

    2016-10-01

    Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

  12. Forecast demand and supply of energy in the short period. Its forecast and sensitivity analysis until the 2004 fiscal year

    International Nuclear Information System (INIS)

    Yamashita, Yukari; Suehiro, Shigeru; Yanagisawa, Akira; Imaeda, Toshiya; Komiyama, Ryouichi

    2004-01-01

    The object of this report is forecast demand and supply of energy in the 2003 and 2004 fiscal year, which correspond to a business recovery period. A macroeconomics model and an energy supply model are calculated by changing actual GNP, crude oil rate and the rerunning period of nuclear power plants. The calculation results are compared with the reference case. In the first chapter, forecast Japanese economy until the 2004 fiscal year is explained. In the second chapter, the results of energy demand and supply in the first chapter are investigated by the home supply and consumption of primary energy (the reference case) and each energy resources. The sensitivity analytical results of actual GNP, consumer price index, home supply of the primary energy, energy expenditure, sales account of electric power, city gas and fuel by five cases such as reference, increase and decrease of oil cost and increase and decrease of economic growth are investigated. The effects of fast rerunning period of nuclear power plant and atmosphere temperature on these above demands of energies are indicated in the third chapter. (S.Y.)

  13. Maximising the commercial value of wind energy through forecasting

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2000-07-01

    The aim of this project, initiated by the DTI, was to advise the electricity industry on the possibility of using weather forecasting to improve the commercial position of both inland and off-shore wind farms under the New Electricity Trading Arrangements (NETA) and to develop appropriate strategies for the use of forecasting. The work has clearly shown that, by using forecasting, wind generators can make money on the Short-Term Power Exchange, increasing their revenue over and above that achieved in the cash-out market. For inland sites, the average annual increased earnings are estimated around 5.8%, rising to 7.5% off-shore. The forecast value methodology developed by the Meteorological Office during the project has proven to be a valuable tool for analysing wind farm trading under NETA. The methodology has the potential to be used by wind farm operators and suppliers wishing to actively trade wind on the Short-Term Power Exchange. It is recommended that further verification of the methodology and development for active use is required. Specifically, a lack of 'true' off-shore wind data has been identified. It appears that off-shore wind farms stand to gain most from forecasting and the report calls for off-shore wind observation data to be made available to allow better verification of the off-shore forecasting models to be undertaken. (author)

  14. Modeling and forecasting energy consumption in China: Implications for Chinese energy demand and imports in 2020

    International Nuclear Information System (INIS)

    Adams, F. Gerard; Shachmurove, Yochanan

    2008-01-01

    The Chinese economy is in a stage of energy transition: from low efficiency solid fuels to oil, gas, and electric power, from agriculture to urbanization and industrialization, from heavy industry to lighter and high tech industry, from low motorization to rapid growth of the motor vehicle population. Experts fear that continued rapid economic growth in China will translate into a massive need to expand imports of oil, coal, and gas. We build an econometric model of the Chinese energy economy based on the energy balance. We use that model to forecast Chinese energy consumption and imports to 2020. The study suggests that China will, indeed, require rapidly growing imports of oil, coal, and gas. This growth is not so sensitive to the rate of economic growth as to increases in motorization. It can be offset, but probably only in small part, by increasing domestic energy production or by improvements in the efficiency of use, particularly in the production of electric power. (author)

  15. Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea

    Directory of Open Access Journals (Sweden)

    Chong Wei Zheng

    2016-01-01

    Full Text Available The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18 wave model driven by T213 (WW3-T213 and T639 (WW3-T639 wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD, “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.

  16. The daily hour forecasting of the electrical energy production from renewable energy sources – a required condition for the operation of the new energy market model

    International Nuclear Information System (INIS)

    Kalpachka, Gergana; Kalpachki, Georgi

    2011-01-01

    The report presented the new energy market model in Bulgaria and the main attention is directed to a daily hour forecasting of the electrical energy production from renewable energy sources. The need of development of a methodology and the development of the most precise methods for predicting is reviewed and some of the used methods at the moment are presented. An analysis of the problems related to the daily hour forecasting is done using data from the producers of electrical energy from renewable energy sources in the territory of western Bulgaria. Keywords: Renewable energy sources, daily hour forecasting, electrical energy

  17. Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms

    International Nuclear Information System (INIS)

    Piltan, Mehdi; Shiri, Hiva; Ghaderi, S.F.

    2012-01-01

    Highlights: ► Investigating different fitness functions for evolutionary algorithms in energy forecasting. ► Energy forecasting of Iranian metal industry by value added, energy prices, investment and employees. ► Using real-coded instead of binary-coded genetic algorithm decreases energy forecasting error. - Abstract: Developing energy-forecasting models is known as one of the most important steps in long-term planning. In order to achieve sustainable energy supply toward economic development and social welfare, it is required to apply precise forecasting model. Applying artificial intelligent models for estimation complex economic and social functions is growing up considerably in many researches recently. In this paper, energy consumption in industrial sector as one of the critical sectors in the consumption of energy has been investigated. Two linear and three nonlinear functions have been used in order to forecast and analyze energy in the Iranian metal industry, Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) are applied to attain parameters of the models. The Real-Coded Genetic Algorithm (RCGA) has been developed based on real numbers, which is introduced as a new approach in the field of energy forecasting. In the proposed model, electricity consumption has been considered as a function of different variables such as electricity tariff, manufacturing value added, prevailing fuel prices, the number of employees, the investment in equipment and consumption in the previous years. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) are the four functions which have been used as the fitness function in the evolutionary algorithms. The results show that the logarithmic nonlinear model using PSO algorithm with 1.91 error percentage has the best answer. Furthermore, the prediction of electricity consumption in industrial sector of Turkey and also Turkish industrial sector

  18. Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach

    International Nuclear Information System (INIS)

    Lü, Xiaoshu; Lu, Tao; Kibert, Charles J.; Viljanen, Martti

    2015-01-01

    Highlights: • This paper presents a new modeling method to forecast energy demands. • The model is based on physical–statistical approach to improving forecast accuracy. • A new method is proposed to address the heterogeneity challenge. • Comparison with measurements shows accurate forecasts of the model. • The first physical–statistical/heterogeneous building energy modeling approach is proposed and validated. - Abstract: Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy

  19. On the market impact of wind energy forecasts

    International Nuclear Information System (INIS)

    Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

  20. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

    2008-01-01

    This paper presents an artificial neural network (ANN) approach for annual electricity consumption in high energy consumption industrial sectors. Chemicals, basic metals and non-metal minerals industries are defined as high energy consuming industries. It is claimed that, due to high fluctuations of energy consumption in high energy consumption industries, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual consumption in such industries. Furthermore, the ANN approach based on a supervised multi-layer perceptron (MLP) is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) industries in Iran from 1979 to 2003 is used to illustrate the applicability of the ANN approach. This study shows the advantage of the ANN approach through analysis of variance (ANOVA). Furthermore, the ANN forecast is compared with actual data and the conventional regression model through ANOVA to show its superiority. This is the first study to present an algorithm based on the ANN and ANOVA for forecasting long term electricity consumption in high energy consuming industries

  1. Commercial demand for energy: a disaggregated approach. [Model validation for 1970-1975; forecasting to 2000

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, J.R.; Cohn, S.; Cope, J.; Johnson, W.S.

    1978-04-01

    This report describes the structure and forecasting accuracy of a disaggregated model of commercial energy use recently developed at Oak Ridge National Laboratory. The model forecasts annual commercial energy use by ten building types, five end uses, and four fuel types. Both economic (utilization rate, fuel choice, capital-energy substitution) and technological factors (equipment efficiency, thermal characteristics of buildings) are explicitly represented in the model. Model parameters are derived from engineering and econometric analysis. The model is then validated by simulating commercial energy use over the 1970--1975 time period. The model performs well both with respect to size of forecast error and ability to predict turning points. The model is then used to evaluate the energy-use implications of national commercial buildings standards based on the ASHRAE 90-75 recommendations. 10 figs., 12 tables, 14 refs.

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

    Energy Technology Data Exchange (ETDEWEB)

    Stoffel, T.

    2012-06-01

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

  3. Models for forecasting energy use in the US farm sector

    Science.gov (United States)

    Christensen, L. R.

    1981-07-01

    Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.

  4. Influence of wind energy forecast in deterministic and probabilistic sizing of reserves

    Energy Technology Data Exchange (ETDEWEB)

    Gil, A.; Torre, M. de la; Dominguez, T.; Rivas, R. [Red Electrica de Espana (REE), Madrid (Spain). Dept. Centro de Control Electrico

    2010-07-01

    One of the challenges in large-scale wind energy integration in electrical systems is coping with wind forecast uncertainties at the time of sizing generation reserves. These reserves must be sized large enough so that they don't compromise security of supply or the balance of the system, but economic efficiency must be also kept in mind. This paper describes two methods of sizing spinning reserves taking into account wind forecast uncertainties, deterministic using a probabilistic wind forecast and probabilistic using stochastic variables. The deterministic method calculates the spinning reserve needed by adding components each of them in order to overcome one single uncertainty: demand errors, the biggest thermal generation loss and wind forecast errors. The probabilistic method assumes that demand forecast errors, short-term thermal group unavailability and wind forecast errors are independent stochastic variables and calculates the probability density function of the three variables combined. These methods are being used in the case of the Spanish peninsular system, in which wind energy accounted for 14% of the total electrical energy produced in the year 2009 and is one of the systems in the world with the highest wind penetration levels. (orig.)

  5. Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors

    International Nuclear Information System (INIS)

    Frías-Paredes, Laura; Mallor, Fermín; Gastón-Romeo, Martín; León, Teresa

    2017-01-01

    Highlights: • A new method to match time series is defined to assess energy forecasting accuracy. • This method relies in a new family of step patterns that optimizes the MAE. • A new definition of the Temporal Distortion Index between two series is provided. • A parametric extension controls both the temporal distortion index and the MAE. • Pareto optimal transformations of the forecast series are obtained for both indexes. - Abstract: Recent years have seen a growing trend in wind and solar energy generation globally and it is expected that an important percentage of total energy production comes from these energy sources. However, they present inherent variability that implies fluctuations in energy generation that are difficult to forecast. Thus, forecasting errors have a considerable role in the impacts and costs of renewable energy integration, management, and commercialization. This study presents an important advance in the task of analyzing prediction models, in particular, in the timing component of prediction error, which improves previous pioneering results. A new method to match time series is defined in order to assess energy forecasting accuracy. This method relies on a new family of step patterns, an essential component of the algorithm to evaluate the temporal distortion index (TDI). This family minimizes the mean absolute error (MAE) of the transformation with respect to the reference series (the real energy series) and also allows detailed control of the temporal distortion entailed in the prediction series. The simultaneous consideration of temporal and absolute errors allows the use of Pareto frontiers as characteristic error curves. Real examples of wind energy forecasts are used to illustrate the results.

  6. Energy operations and planning decision support for systems using weather forecast information

    International Nuclear Information System (INIS)

    Altalo, M.G.

    2004-01-01

    Hydroelectric utilities deal with uncertainties on a regular basis. These include uncertainties in weather, policy and markets. This presentation outlined regional studies to define uncertainty, sources of uncertainty and their affect on power managers, power marketers, power insurers and end users. Solutions to minimize uncertainties include better forecasting and better business processes to mobilize action. The main causes of uncertainty in energy operations and planning include uncaptured wind, precipitation and wind events. Load model errors also contribute to uncertainty in energy operations. This presentation presented the results of a 2002-2003 study conducted by the National Oceanic and Atmospheric Administration (NOAA) on the impact uncertainties in northeast energy weather forecasts. The study demonstrated the cost of seabreeze error on transmission and distribution. The impact of climate scale events were also presented along with energy demand implications. It was suggested that energy planners should incorporate climate change parameters into planning, and that models should include probability distribution forecasts and ensemble forecasting methods that incorporate microclimate estimates. It was also suggested that seabreeze, lake effect, fog, afternoon thunderstorms and frontal passage should be incorporated into forecasts. tabs., figs

  7. Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

    Directory of Open Access Journals (Sweden)

    Anand Krishnan Prakash

    2018-04-01

    Full Text Available Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error.

  8. Solar energy prediction and verification using operational model forecasts and ground-based solar measurements

    International Nuclear Information System (INIS)

    Kosmopoulos, P.G.; Kazadzis, S.; Lagouvardos, K.; Kotroni, V.; Bais, A.

    2015-01-01

    The present study focuses on the predictions and verification of these predictions of solar energy using ground-based solar measurements from the Hellenic Network for Solar Energy and the National Observatory of Athens network, as well as solar radiation operational forecasts provided by the MM5 mesoscale model. The evaluation was carried out independently for the different networks, for two forecast horizons (1 and 2 days ahead), for the seasons of the year, for varying solar elevation, for the indicative energy potential of the area, and for four classes of cloud cover based on the calculated clearness index (k_t): CS (clear sky), SC (scattered clouds), BC (broken clouds) and OC (overcast). The seasonal dependence presented relative rRMSE (Root Mean Square Error) values ranging from 15% (summer) to 60% (winter), while the solar elevation dependence revealed a high effectiveness and reliability near local noon (rRMSE ∼30%). An increment of the errors with cloudiness was also observed. For CS with mean GHI (global horizontal irradiance) ∼ 650 W/m"2 the errors are 8%, for SC 20% and for BC and OC the errors were greater (>40%) but correspond to much lower radiation levels (<120 W/m"2) of consequently lower energy potential impact. The total energy potential for each ground station ranges from 1.5 to 1.9 MWh/m"2, while the mean monthly forecast error was found to be consistently below 10%. - Highlights: • Long term measurements at different atmospheric cases are needed for energy forecasting model evaluations. • The total energy potential at the Greek sites presented ranges from 1.5 to 1.9 MWh/m"2. • Mean monthly energy forecast errors are within 10% for all cases analyzed. • Cloud presence results of an additional forecast error that varies with the cloud cover.

  9. The economic benefit of short-term forecasting for wind energy in the UK electricity market

    International Nuclear Information System (INIS)

    Barthelmie, R.J.; Murray, F.; Pryor, S.C.

    2008-01-01

    In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around pound 5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators. (author)

  10. Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods

    DEFF Research Database (Denmark)

    Reikard, Gordon; Pinson, Pierre; Bidlot, Jean

    2011-01-01

    Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several...... days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts...... (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...

  11. Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network

    International Nuclear Information System (INIS)

    Zeng, Yu-Rong; Zeng, Yi; Choi, Beomjin; Wang, Lin

    2017-01-01

    Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction. - Highlights: • Enhanced back-propagation neural network (ADE-BPNN) for energy consumption forecasting. • ADE-BPNN outperforms the current best models for two comparative cases. • Mean impact value approach explores socio-economic factors' relative importance. • ADE-BPNN's adjusted goodness-of-fit is 99.2% for China's energy consumption forecasting.

  12. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

    International Nuclear Information System (INIS)

    Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang

    2012-01-01

    Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

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

    Energy Technology Data Exchange (ETDEWEB)

    Penning, Julie [Navigant Consulting Inc., Washington, DC (United States); Stober, Kelsey [Navigant Consulting Inc., Washington, DC (United States); Taylor, Victor [Navigant Consulting Inc., Washington, DC (United States); Yamada, Mary [Navigant Consulting Inc., Washington, DC (United States)

    2016-09-01

    The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is a biannual report which models the adoption of LEDs in the U.S. general-lighting market, along with associated energy savings, based on the full potential DOE has determined to be technically feasible over time. This version of the report uses an updated 2016 U.S. lighting-market model that is more finely calibrated and granular than previous models, and extends the forecast period to 2035 from the 2030 limit that was used in previous editions.

  14. Analysis of recurrent neural networks for short-term energy load forecasting

    Science.gov (United States)

    Di Persio, Luca; Honchar, Oleksandr

    2017-11-01

    Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

  15. A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available The photovoltaic (PV systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

  16. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    2014-01-01

    energy. The present study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by downscaling global numerical weather prediction (NWP) data from the National Centers for Environmental Prediction Global Forecast System (GFS) using the limited-area NWP model described...

  17. Forecast for nuclear energy: Clear skies or stormy weather?

    Science.gov (United States)

    Ferguson, Charles D.

    2018-01-01

    During the last decade many people in the nuclear industry were forecasting a renaissance in construction of nuclear power plants, especially in light of the near-zero greenhouse gas emissions of nuclear power and the global need for such cleaner electricity sources. While the accident in March 2011 at the Fukushima Daiichi Nuclear Power Station in Japan resulted in dozens of reactor shutdowns in Japan and reconsideration of new nuclear power plants in several countries, other countries are continuing to build new plants but not at a fast enough rate yet to make a significant further reduction in greenhouse gas emissions. Even before this accident, the prospects for major growth in nuclear power were dim. To explicate the present situation and potential future scenarios for nuclear power, this paper examines the issue of who bears the financial risk especially during the construction phase, the roles of governments in financial interventions such as loan guarantees, tax credits, and prices on greenhouse gas emissions, the effects of regulated versus market-based utility systems, the competition with relatively cheap natural gas, the roles of various governments around the world in determining the use of nuclear power, the interdependent nature of the nuclear industry with companies both competing and cooperating with each other, and the issue of whether small modular reactors or advanced nuclear reactors could result in many more plants being constructed in the United States and worldwide.

  18. Short-Term Forecasting of Electric Energy Generation for a Photovoltaic System

    Directory of Open Access Journals (Sweden)

    Dinh V.T.

    2018-01-01

    Full Text Available This article presents a short-term forecast of electric energy output of a photovoltaic (PV system towards Tomsk city, Russia climate variations (module temperature and solar irradiance. The system is located at Institute of Non-destructive Testing, Tomsk Polytechnic University. The obtained results show good agreement between actual data and prediction values.

  19. Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1 (GM (1, 1, back propagation (BP and LSSVM, in terms of prediction accuracy and forecasting risk.

  20. A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

    Directory of Open Access Journals (Sweden)

    Mingwu Wang

    2014-01-01

    Full Text Available The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole.

  1. Short-Term Load Forecast in Electric Energy System in Bulgaria

    Directory of Open Access Journals (Sweden)

    Irina Asenova

    2010-01-01

    Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.

  2. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    Science.gov (United States)

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

  3. Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters

    OpenAIRE

    Fusco, Francesco; Ringwood, John

    2010-01-01

    Real-time control of wave energy converters requires knowledge of future incident wave elevation in order to approach optimal efficiency of wave energy extraction. We present an approach where the wave elevation is treated as a time series and it is predicted only from its past history. A comparison of a range of forecasting methodologies on real wave observations from two different locations shows how the relatively simple linear autoregressive model, which implicitly models the cyclical beh...

  4. Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

    International Nuclear Information System (INIS)

    González-Aparicio, I.; Zucker, A.

    2015-01-01

    Highlights: • Reduction wind power forecasting uncertainty for day ahead and intraday markets. • Statistical relationship between total load and wind power generation. • Accurately forecast expected revenues from wind producer’s perspective. - Abstract: The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator’s position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010–2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind.

  5. UD-WCMA: An Energy Estimation and Forecast Scheme for Solar Powered Wireless Sensor Networks

    KAUST Repository

    Dehwah, Ahmad H.

    2017-04-11

    Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes.

  6. Geological Structure, Seismic energy Release and Forecasting of Rockburst Occurrence

    Czech Academy of Sciences Publication Activity Database

    Rudajev, Vladimír; Číž, Radim; Lokajíček, Tomáš; Vilhelm, Jan

    2000-01-01

    Roč. 118, č. 16 (2000), s. 171-173 ISSN 1211-1910. [Czech - Polish - Slovak Symposium on Mining Geophysics /27./. Ramzová, 05.10.1999-07.10.1999] Institutional research plan: CEZ:AV0Z3046908 Keywords : seismoacustic emission * energy -frequency distribution * multichannel statistic extrapolation Subject RIV: DC - Siesmology, Volcanology, Earth Structure

  7. Forecasting long-term energy demand of Croatian transport sector

    DEFF Research Database (Denmark)

    Pukšec, Tomislav; Krajačić, Goran; Lulić, Zoran

    2013-01-01

    predictions for the Croatian transport sector are presented. Special emphasis is given to different influencing mechanisms, both legal and financial. The energy demand predictions presented in this paper are based on an end-use simulation model developed and tested with Croatia as a case study. The model...

  8. Monthly electric energy demand forecasting with neural networks and Fourier series

    International Nuclear Information System (INIS)

    Gonzalez-Romera, E.; Jaramillo-Moran, M.A.; Carmona-Fernandez, D.

    2008-01-01

    Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose. (author)

  9. Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

    OpenAIRE

    Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

    2014-01-01

    The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use...

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

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Cathy [WindLogics, St. Paul, MN (United States)

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  11. Forecasting household transport energy demand in South African cities

    CSIR Research Space (South Africa)

    Mokonyama, Mathetha T

    2009-11-01

    Full Text Available in South Africa have over the recent past increased at a rate more than any other household expenditure item (StasSA, 2008). Transport energy from fuel, forms a large component of the transport costs for both private car and public transport trips... by the Constitution to plan and manage the provision of services to communities in a sustainable manner. The services include water, sanitation, electricity and transport. Some of the management instruments used by local government include Integrated Development...

  12. Distributed energy resources scheduling considering real-time resources forecast

    DEFF Research Database (Denmark)

    Silva, M.; Sousa, T.; Ramos, S.

    2014-01-01

    grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper......, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used....

  13. Fitting and forecasting coupled dark energy in the non-linear regime

    Energy Technology Data Exchange (ETDEWEB)

    Casas, Santiago; Amendola, Luca; Pettorino, Valeria; Vollmer, Adrian [Institut für Theoretische Physik, Ruprecht-Karls-Universität Heidelberg, Philosophenweg 16, Heidelberg, 69120 Germany (Germany); Baldi, Marco, E-mail: casas@thphys.uni-heidelberg.de, E-mail: l.amendola@thphys.uni-heidelberg.de, E-mail: mail@marcobaldi.it, E-mail: v.pettorino@thphys.uni-heidelberg.de, E-mail: vollmer@thphys.uni-heidelberg.de [Dipartimento di Fisica e Astronomia, Alma Mater Studiorum Università di Bologna, viale Berti Pichat, 6/2, Bologna, I-40127 Italy (Italy)

    2016-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β{sup 2}, with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications.

  14. Fitting and forecasting coupled dark energy in the non-linear regime

    International Nuclear Information System (INIS)

    Casas, Santiago; Amendola, Luca; Pettorino, Valeria; Vollmer, Adrian; Baldi, Marco

    2016-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range 0z=–1.6 and wave modes below 0k=1 h/Mpc. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and weak lensing (WL). We find that by using information in the non-linear power spectrum, and combining the GC and WL probes, we can constrain the dark matter-dark energy coupling constant squared, β 2 , with precision smaller than 4% and all other cosmological parameters better than 1%, which is a considerable improvement of more than an order of magnitude compared to corresponding linear power spectrum forecasts with the same survey specifications

  15. Development of a novel market forecasting tool and its application to hydrogen energy production in Scotland

    International Nuclear Information System (INIS)

    Houghton, T.; Cruden, A.

    2010-01-01

    The authors propose a novel model for forecasting the deployment of hydrogen energy systems based on a company value maximisation algorithm, designed to assist governments and other industry players in decision-making and the development of appropriate policy instruments. Current cost-minimisation approaches, such as MARKAL, have limitations particularly where price arbitrage between energy streams exists. A theoretical relationship between market sector valuations and investment activity is developed and the model is subsequently applied to the Scottish hydrogen energy market. Through the utilisation of net present value, revenue and profitability based valuations, the impact of investing in hydrogen energy infrastructure projects on three key market competitors is considered. It is shown that the three methods for calculating the value impact render different results suggesting that the use of a single method to assess forecast development scenarios, whether cost or value-based methods, may be misleading and that the holistic approach proposed is more realistic. The archivable value of this paper is to demonstrate the impact that investor expectations can have on investment decisions, a facet not captured in traditional methods of forecasting. (author)

  16. Forecasted Changes in West Africa Photovoltaic Energy Output by 2045

    Directory of Open Access Journals (Sweden)

    Serge Dimitri Yikwe Buri Bazyomo

    2016-10-01

    Full Text Available The impacts of climate change on photovoltaic (PV output in the fifteen countries of the Economic Community of West African States (ECOWAS was analyzed in this paper. Using a set of eight climate models, the trends of solar radiation and temperature between 2006–2100 were examined. Assuming a lifetime of 40 years, the future changes of photovoltaic energy output for the tilted plane receptor compared to 2006–2015 were computed for the whole region. The results show that the trends of solar irradiation are negative except for the Irish Centre for High-End Computing model which predicts a positive trend with a maximum value of 0.17 W/m2/year for Cape Verde and the minimum of −0.06 W/m2/year for Liberia. The minimum of the negative trend is −0.18 W/m2/year predicted by the Model for Interdisciplinary Research on Climate (MIROC, developed at the University of Tokyo Center for Climate System Research for Cape Verde. Furthermore, temperature trends are positive with a maximum of 0.08 K/year predicted by MIROC for Niger and minimum of 0.03 K/year predicted by Nature Conservancy of Canada (NCC, Max Planck Institute (MPI for Climate Meteorology at Hamburg, French National Meteorological Research Center (CNRM and Canadian Centre for Climate Modelling and Analysis (CCCMA for Cape Verde. Photovolataic energy output changes show increasing trends in Sierra Leone with 0.013%/year as the maximum. Climate change will lead to a decreasing trend of PV output in the rest of the countries with a minimum of 0.032%/year in Niger.

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

    Energy Technology Data Exchange (ETDEWEB)

    none,

    2014-08-29

    With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

  18. The long-term forecast of Taiwan's energy supply and demand: LEAP model application

    International Nuclear Information System (INIS)

    Huang, Yophy; Bor, Yunchang Jeffrey; Peng, Chieh-Yu

    2011-01-01

    The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research highlights: → The LEAP model is useful for international energy policy comparison. → Nuclear power plants have significant, positive impacts on CO 2 emission. → The most effective energy policy is to adopt demand-side management. → Reasonable energy pricing provides incentives for energy efficiency and conservation. → Financial crisis has less impact on energy demand than aggressive energy policy.

  19. The use of economic forecasts in Danish economic policy, with special emphasis on energy and the environment

    International Nuclear Information System (INIS)

    Nielsen, Lise

    1998-01-01

    This article discusses the use of economic forecasts in Danish economic policy, with special emphasis on energy and the environment. Two different approaches have been used to forecast energy consumption and its effects on environment in Denmark and other countries. These are the macro economic and the technical approaches. The technical approach is based on technical expertise related to energy production and energy consumption, and the article asks whether the forecasts produced by this approach are superior to macro economic forecasts of energy consumption. This question is interesting because the implications for policy resulting from the two approaches seem to be different. The analysis may have relevance to other areas outside the main economic field. (au) 22 refs

  20. Near-term Forecasting of Solar Total and Direct Irradiance for Solar Energy Applications

    Science.gov (United States)

    Long, C. N.; Riihimaki, L. D.; Berg, L. K.

    2012-12-01

    Integration of solar renewable energy into the power grid, like wind energy, is hindered by the variable nature of the solar resource. One challenge of the integration problem for shorter time periods is the phenomenon of "ramping events" where the electrical output of the solar power system increases or decreases significantly and rapidly over periods of minutes or less. Advance warning, of even just a few minutes, allows power system operators to compensate for the ramping. However, the ability for short-term prediction on such local "point" scales is beyond the abilities of typical model-based weather forecasting. Use of surface-based solar radiation measurements has been recognized as a likely solution for providing input for near-term (5 to 30 minute) forecasts of solar energy availability and variability. However, it must be noted that while fixed-orientation photovoltaic panel systems use the total (global) downwelling solar radiation, tracking photovoltaic and solar concentrator systems use only the direct normal component of the solar radiation. Thus even accurate near-term forecasts of total solar radiation will under many circumstances include inherent inaccuracies with respect to tracking systems due to lack of information of the direct component of the solar radiation. We will present examples and statistical analyses of solar radiation partitioning showing the differences in the behavior of the total/direct radiation with respect to the near-term forecast issue. We will present an overview of the possibility of using a network of unique new commercially available total/diffuse radiometers in conjunction with a near-real-time adaptation of the Shortwave Radiative Flux Analysis methodology (Long and Ackerman, 2000; Long et al., 2006). The results are used, in conjunction with persistence and tendency forecast techniques, to provide more accurate near-term forecasts of cloudiness, and both total and direct normal solar irradiance availability and

  1. Combining high frequency data with non-linear models for forecasting energy market volatility

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Křehlík, Tomáš

    2016-01-01

    Roč. 55, č. 1 (2016), s. 222-242 ISSN 0957-4174 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : artificial neural networks * realized volatility * multiple-step-ahead forecasts * energy markets Subject RIV: AH - Economics Impact factor: 3.928, year: 2016 http://library.utia.cas.cz/separaty/2016/E/barunik-0456185.pdf

  2. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien; Tsai, Chung-Ming

    2011-01-01

    This paper examines the dynamic relationships between pollutant emissions, energy consumption, and the output for Brazil during 1980-2007. The Grey prediction model (GM) is applied to predict three variables during 2008-2013. In the long-run equilibrium emissions appear to be both energy consumption and output inelastic, but energy is a more important determinant of emissions than output. This may be because Brazilian unsustainable land use and forestry contribute most to the country's greenhouse gas emissions. The findings of the inverted U-shaped relationships of both emissions-income and energy consumption-income imply that both environmental damage and energy consumption firstly increase with income, then stabilize, and eventually decline. The causality results indicate that there is a bidirectional strong causality running between income, energy consumption and emissions. In order to reduce emissions and to avoid a negative effect on the economic growth, Brazil should adopt the dual strategy of increasing investment in energy infrastructure and stepping up energy conservation policies to increase energy efficiency and reduce wastage of energy. The forecasting ability of GM is compared with the autoregressive integrated moving average (ARIMA) model over the out-of-sample period between 2002 and 2007. All of the optimal GMs and ARIMAs have a strong forecasting performance with MAPEs of less than 3%. -- Highlights: → Emissions are energy consumption and output inelastic, but energy is a more important determinant of emissions than output. → The relationship between emissions and income is an inverted U-shaped curve. → The relationship between consumption and income is an inverted U-shaped curve. → The causality results indicate that there is a bidirectional strong causality running between income, energy consumption and emissions. → The Grey prediction model is applied to predict emissions, energy consumption and output during 2008-2013.

  3. International wind energy development. World market update 1997. Forecast 1998-2002

    International Nuclear Information System (INIS)

    1998-03-01

    This is the third issue of the annual World Market Update from BTM Consult ApS, covering the year 1997. All figures in the status part refer to end of the year 1997, the past 3 years development is also assessed and the forecast looks 5 years ahead. The annual installation of new wind power capacity increased by 21% resulting in a cumulative installation by the end of 1997 of 7,636 MW. Approx. 84% of the new capacity (1,566 MW), was installed in Europe emphasizing this region as the leading market regarding utilisation of wind energy. India remains halted (since 1996) and it has been very difficult to get reliable figures from this market. The US market is still very slow, but some very big projects are under construction. The first two years of the five year forecast has been adjusted downwards compared to forecast presented last year. The main reason is due to the economic situation in Asia. The cumulative MW in the five year forecast shows a slight increase compared to last years 5 year forecast, justified by higher expectations to other markets. The surprising pace in the commercialization of MW-turbines and their projected use for offshore applications few years ahead is assessed in the report. A total of 129 turbines of 1-1.65 MW are already in operation - most of them in Germany. On the international arena it is expected, that the wind power development will gain benefits from the Kyoto-Protocol (December 1997) and the 'White Paper' from the EU commission, although it will take some years to transfer these political targets into operational schemes. This report can be found on Internet Web-pages: http://home4.inet.tele.dk/btmcwind/index.html. (EG)

  4. Forecasting of future earthquakes in the northeast region of India considering energy released concept

    Science.gov (United States)

    Zarola, Amit; Sil, Arjun

    2018-04-01

    This study presents the forecasting of time and magnitude size of the next earthquake in the northeast India, using four probability distribution models (Gamma, Lognormal, Weibull and Log-logistic) considering updated earthquake catalog of magnitude Mw ≥ 6.0 that occurred from year 1737-2015 in the study area. On the basis of past seismicity of the region, two types of conditional probabilities have been estimated using their best fit model and respective model parameters. The first conditional probability is the probability of seismic energy (e × 1020 ergs), which is expected to release in the future earthquake, exceeding a certain level of seismic energy (E × 1020 ergs). And the second conditional probability is the probability of seismic energy (a × 1020 ergs/year), which is expected to release per year, exceeding a certain level of seismic energy per year (A × 1020 ergs/year). The logarithm likelihood functions (ln L) were also estimated for all four probability distribution models. A higher value of ln L suggests a better model and a lower value shows a worse model. The time of the future earthquake is forecasted by dividing the total seismic energy expected to release in the future earthquake with the total seismic energy expected to release per year. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in zone Z.12, zone Z.16 and zone Z.15, respectively and that are the identified seismic source zones in the study area which show that the proposed techniques and models yield good forecasting accuracy.

  5. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2008-04-01

    Full Text Available Due to the wide potential applications of wireless sensor networks, this topic has attracted great attention. The strict energy constraints of sensor nodes result in great challenges for energy efficiency. This paper proposes an energy-efficient organization method. The organization of wireless sensor networks is formulated for target tracking. Target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Combining autoregressive moving average (ARMA model and radial basis function networks (RBFNs, robust target position forecasting is performed. Moreover, an energyefficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks are awakened in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of ARMA model and RBFN can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.

  6. Baseline energy forecasts and analysis of alternative strategies for airline fuel conservation

    Energy Technology Data Exchange (ETDEWEB)

    1976-07-01

    To evaluate the impact of fuel conservation strategies, baseline forecasts of airline activity and energy consumption to 1990 were developed. Alternative policy options to reduce fuel consumption were identified and analyzed for three baseline levels of aviation activity within the framework of an aviation activity/energy consumption model. By combining the identified policy options, a strategy was developed to provide incentives for airline fuel conservation. Strategies and policy options were evaluated in terms of their impact on airline fuel conservation and the functioning of the airline industry as well as the associated social, environmental, and economic costs. (GRA)

  7. Short-term forecasts of energy use and energy supply 2012-2014. Spring 2013; Kortsiktsprognos - Oever energianvaendning och energitillfoersel 2012-2014, Vaaren 2013

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-03-15

    This report provides a description of the Swedish energy system in 2011 and an assessment of its development between 2012 - 2014. The forecast shall be interpreted as a consequence of the limitations and assumptions underlying it. Thus, it is important to remember that if any of the conditions or assumptions change, the forecast's results will also change. The forecast is based on economic conditions that have been developed from the National Institute of Economic Trend. Other conditions such as electricity prices, fuel prices, outdoor temperature and inflow into reservoirs are based on information available up to January 2013, when forecasting began.

  8. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    Directory of Open Access Journals (Sweden)

    Zhaoxuan Li

    2016-01-01

    Full Text Available We evaluate and compare two common methods, artificial neural networks (ANN and support vector regression (SVR, for predicting energy productions from a solar photovoltaic (PV system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE, mean absolute error (MAE, root mean square error (RMSE, relative MBE (rMBE, mean percentage error (MPE and relative RMSE (rRMSE. This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.

  9. Energy Systems Scenario Modelling and Long Term Forecasting of Hourly Electricity Demand

    DEFF Research Database (Denmark)

    Alberg Østergaard, Poul; Møller Andersen, Frits; Kwon, Pil Seok

    2015-01-01

    . The results show that even with a limited short term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrate wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant...... or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model...... effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps...

  10. Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Computer Science; Simmhan, Yogesh [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering; Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Electrical Engineering

    2011-12-11

    The rising global demand for energy is best addressed by adopting and promoting sustainable methods of power consumption. We employ an informatics approach towards forecasting the energy consumption patterns in a university campus micro-grid which can be used for energy use planning and conservation. We use novel indirect indicators of energy that are commonly available to train regression tree models that can predict campus and building energy use for coarse (daily) and fine (15-min) time intervals, utilizing 3 years of sensor data collected at 15min intervals from 170 smart power meters. We analyze the impact of individual features used in the models to identify the ones best suited for the application. Our models show a high degree of accuracy with CV-RMSE errors ranging from 7.45% to 19.32%, and a reduction in error from baseline models by up to 53%.

  11. Forecasting of Energy Expenditure of Induced Seismicity with Use of Artificial Neural Network

    Science.gov (United States)

    Cichy, Tomasz; Banka, Piotr

    2017-12-01

    Coal mining in many Polish mines in the Upper Silesian Coal Basin is accompanied by high levels of induced seismicity. In mining plants, the methods of shock monitoring are improved, allowing for more accurate localization of the occurring phenomena and determining their seismic energy. Equally important is the development of ways of forecasting seismic hazards that may occur while implementing mine design projects. These methods, depending on the length of time for which the forecasts are made, can be divided into: longterm, medium-term, short-term and so-called alarm. Long-term forecasts are particularly useful for the design of seam exploitations. The paper presents a method of predicting changes in energy expenditure of shock using a properly trained artificial neural network. This method allows to make long-term forecasts at the stage of the mine’s exploitation design, thus enabling the mining work plans to be reviewed to minimize the potential for tremors. The information given at the input of the neural network is indicative of the specific energy changes of the elastic deformation occurring in the selected, thick, resistant rock layers (tremor-prone layers). Energy changes, taking place in one or more tremor-prone layers are considered. These indicators describe only the specific energy changes of the elastic deformation accumulating in the rock as a consequence of the mining operation, but does not determine the amount of energy released during the destruction of a given volume of rock. In this process, the potential energy of elastic strain transforms into other, non-measurable energy types, including the seismic energy of recorded tremors. In this way, potential energy changes affect the observed induced seismicity. The parameters used are characterized by increases (declines) of specific energy with separation to occur before the hypothetical destruction of the rock and after it. Additional input information is an index characterizing the rate of

  12. Environmental data processing by clustering methods for energy forecast and planning

    Energy Technology Data Exchange (ETDEWEB)

    Di Piazza, Annalisa [Dipartimento di Ingegneria Idraulica e Applicazioni Ambientali (DIIAA), viale delle Scienze, Universita degli Studi di Palermo, 90128 Palermo (Italy); Di Piazza, Maria Carmela; Ragusa, Antonella; Vitale, Gianpaolo [Consiglio Nazionale delle Ricerche Istituto di Studi sui Sistemi Intelligenti per l' Automazione (ISSIA - CNR), sezione di Palermo, Via Dante, 12, 90141 Palermo (Italy)

    2011-03-15

    This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems. (author)

  13. A computationally efficient electricity price forecasting model for real time energy markets

    International Nuclear Information System (INIS)

    Feijoo, Felipe; Silva, Walter; Das, Tapas K.

    2016-01-01

    Highlights: • A fast hybrid forecast model for electricity prices. • Accurate forecast model that combines K-means and machine learning techniques. • Low computational effort by elimination of feature selection techniques. • New benchmark results by using market data for year 2012 and 2015. - Abstract: Increased significance of demand response and proliferation of distributed energy resources will continue to demand faster and more accurate models for forecasting locational marginal prices. This paper presents such a model (named K-SVR). While yielding prediction accuracy comparable with the best known models in the literature, K-SVR requires a significantly reduced computational time. The computational reduction is attained by eliminating the use of a feature selection process, which is commonly used by the existing models in the literature. K-SVR is a hybrid model that combines clustering algorithms, support vector machine, and support vector regression. K-SVR is tested using Pennsylvania–New Jersey–Maryland market data from the periods 2005–6, 2011–12, and 2014–15. Market data from 2006 has been used to measure performance of many of the existing models. Authors chose these models to compare performance and demonstrate strengths of K-SVR. Results obtained from K-SVR using the market data from 2012 and 2015 are new, and will serve as benchmark for future models.

  14. The relationship between energy intensity and income levels: Forecasting long term energy demand in Asian emerging countries

    International Nuclear Information System (INIS)

    Galli, R.; Univ. della Svizzera Italiana, Lugano

    1998-01-01

    This paper analyzes long-term trends in energy intensity for ten Asian emerging countries to test for a non-monotonic relationship between energy intensity and income in the author's sample. Energy demand functions are estimated during 1973--1990 using a quadratic function of log income. The long-run coefficient on squared income is found to be negative and significant, indicating a change in trend of energy intensity. The estimates are then used to evaluate a medium-term forecast of energy demand in the Asian countries, using both a log-linear and a quadratic model. It is found that in medium to high income countries the quadratic model performs better than the log-linear, with an average error of 9% against 43% in 1995. For the region as a whole, the quadratic model appears more adequate with a forecast error of 16% against 28% in 1995. These results are consistent with a process of dematerialization, which occurs as a result of a reduction of resource use per unit of GDP once an economy passes some threshold level of GDP per capita

  15. Using Support Vector Machine to Forecast Energy Usage of a Manhattan Skyscraper

    Science.gov (United States)

    Winter, R.; Boulanger, A.; Anderson, R.; Wu, L.

    2011-12-01

    As our society gains a better understanding of how humans have negatively impacted the environment, research related to reducing carbon emissions and overall energy consumption has become increasingly important. One of the simplest ways to reduce energy usage is by making current buildings less wasteful. By improving energy efficiency, this method of lowering our carbon footprint is particularly worthwhile because it actually reduces energy costs of operating the building, unlike many environmental initiatives that require large monetary investments. In order to improve the efficiency of the heating and air conditioning (HVAC) system of a Manhattan skyscraper, 345 Park Avenue, a predictive computer model was designed to forecast the amount of energy the building will consume. This model uses support vector machine (SVM), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. This pure dependence on historical data makes the model very easily applicable to different types of buildings with few model adjustments. The SVM model was built to predict a week of future energy usage based on past energy, temperature, and dew point temperature data. The predictive model closely approximated the actual values of energy usage for the spring and less closely for the winter. The prediction may be improved with additional historical data to help the model account for seasonal variability. This model is useful for creating a close approximation of future energy usage and predicting ways to diminish waste.

  16. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

    Directory of Open Access Journals (Sweden)

    Mauro Castelli

    2015-01-01

    Full Text Available Energy consumption forecasting (ECF is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

  17. Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

    Science.gov (United States)

    Palchak, David

    Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

  18. Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

    Science.gov (United States)

    Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

    2010-09-01

    The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

  19. Predicting the local impacts of energy development: a critical guide to forecasting methods and models

    Energy Technology Data Exchange (ETDEWEB)

    Sanderson, D.; O' Hare, M.

    1977-05-01

    Models forecasting second-order impacts from energy development vary in their methodology, output, assumptions, and quality. As a rough dichotomy, they either simulate community development over time or combine various submodels providing community snapshots at selected points in time. Using one or more methods - input/output models, gravity models, econometric models, cohort-survival models, or coefficient models - they estimate energy-development-stimulated employment, population, public and private service needs, and government revenues and expenditures at some future time (ranging from annual to average year predictions) and for different governmental jurisdictions (municipal, county, state, etc.). Underlying assumptions often conflict, reflecting their different sources - historical data, comparative data, surveys, and judgments about future conditions. Model quality, measured by special features, tests, exportability and usefulness to policy-makers, reveals careful and thorough work in some cases and hurried operations with insufficient in-depth analysis in others.

  20. Forecasting and decision-making in electricity markets with focus on wind energy

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi

    This thesis deals with analysis, forecasting and decision making in liberalised electricity markets. Particular focus is on wind power, its interaction with the market and the daily decision making of wind power generators. Among recently emerged renewable energy generation technologies, wind power...... derivation of practically applicable tools for decision making highly relevant. The main characteristics of wind power differ fundamentally from those of conventional thermal power. Its effective generation capacity varies over time and is directly dependent on the weather. This dependency makes future...... has become the global leader in terms of installed capacity and advancement. This makes wind power an ideal candidate to analyse the impact of growing renewable energy generation capacity on the electricity markets. Furthermore, its present status of a significant supplier of electricity makes...

  1. Forecasting of Energy and Petroleum Consumption by Motor Transport in the Regions of the Russian Federation

    Directory of Open Access Journals (Sweden)

    Leontiy Viktorovich Eder

    2017-09-01

    Full Text Available The paper offers the directions for the improvement of methodological approach to forecasting the energy consumption in transport, taking into account special features of Russian regions. The authors developed a multivariate model allowing to predict the motor vehicle rate specified for the regions of the Russian Federation depending on the economic, social and institutional features. We formalized the dynamic (trend model for predicting the effectiveness of energy consumption per unit of the vehicle in Russia with details on Federal districts. In the study, in predicting the number of motor transport, the authors applied the methods of economic and mathematical simulation modelling based on the results of the econometric analysis for the calculation of the population having motor transport. In determining the potential specific energy consumption, we have aggregated trending patterns and convergence. The study has shown that by 2040, the number of passenger cars in Russia will grow to 57.1 million, and the total number of all types of road transport will grow by 14.9 million units to 66.2 million. The highest growth rates are predicted in the Central regions of Russia and in some areas of Siberia. The smallest growth rates are expected in the Chukotka Autonomous District, Kamchatka and Primorsky regions. Energy efficiency in transport and active introduction of alternative motor fuels, primarily methane, will reduce the consumption of gasoline and diesel fuel by motor transport. Thus, in the forecast period of 2018–2040, the consumption of petroleum products by motor transport will be reduced by 8.9 million tons: from 61,9 million tons of oil to 51.7 million tons of oil. The results of the study can be applied for the formulation of proposals on the creation of scientific and methodological apparatus to predict the development of transport sector and oil products supply in of the regions of Russia.

  2. Efficient use of energy by means of Weather Forecast Control. When the weather forecast controls the heating; Efficienter energiegebruik met Weather Forecast Control. Als de weersverwachting de verwarming aanstuurt

    Energy Technology Data Exchange (ETDEWEB)

    Crijns, H. [Crijns Energy Controlling, Malden (Netherlands)

    2012-06-15

    As of late 2007, three government buildings in the German federal state of Nordrhein-Westfalen have been equipped with a Weather Forecast Control (VVFC) system, a new application in the building control system that should create a more healthy indoor climate at significantly lower energy costs than currently feasible. The result of three years of measurement: a noticeably increase in comfort level of the indoor climate and an average saving on energy cost of 12 percent. [Dutch] In de Duitse deelstaat Nordrhein-Westfalen zijn vanaf eind 2007 drie overheidsgebouwen uitgerust met Weather Forecast Control (VVFC), een nieuwe applicatie van het gebouwbeheersysteem dat een gezonder binnenklimaat moet creeren met beduidend lagere energiekosten dan momenteel haalbaar is. Het resultaat na drie jaar meten: een merkbaar comfortabeler binnenklimaat en gemiddeld 12 procent besparing op de energiekosten.

  3. A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

    2008-01-01

    This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to

  4. Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

    Directory of Open Access Journals (Sweden)

    Hai Lan

    2017-02-01

    Full Text Available Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN and hybrid multi-objective particle swarm optimization (HMOPSO to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

  5. A hybrid method for forecasting the energy output of photovoltaic systems

    International Nuclear Information System (INIS)

    Ramsami, Pamela; Oree, Vishwamitra

    2015-01-01

    Highlights: • We propose a novel hybrid technique for predicting the daily PV energy output. • Multiple linear regression, FFNN and GRNN artificial neural networks are used. • Stepwise regression is used to select the most relevant meteorological parameters. • SR-FFNN reduces the average dispersion and overall bias in prediction errors. • Accuracy metrics of hybrid models are better than those of single-stage models. - Abstract: The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. Predicting the output of photovoltaic systems is therefore essential for managing the operation and assessing the economic performance of power systems. This paper presents a new technique for forecasting the 24-h ahead stochastic energy output of photovoltaic systems based on the daily weather forecasts. A comparison of the performances of the hybrid technique with conventional linear regression and artificial neural network models has also been reported. Initially, three single-stage models were designed, namely the generalized regression neural network, feedforward neural network and multiple linear regression. Subsequently, a hybrid-modeling approach was adopted by applying stepwise regression to select input variables of greater importance. These variables were then fed to the single-stage models resulting in three hybrid models. They were then validated by comparing the forecasts of the models with measured dataset from an operational photovoltaic system. The accuracy of the each model was evaluated based on the correlation coefficient, mean absolute error, mean bias error and root mean square error values. Simulation results revealed that the hybrid models perform better than their corresponding single-stage models. Stepwise regression-feedforward neural network hybrid model outperformed the other models with root mean square error, mean absolute error, mean bias error and

  6. Forecasting jobs in the supply chain for investments in residential energy efficiency retrofits in Florida

    Science.gov (United States)

    Fobair, Richard C., II

    This research presents a model for forecasting the numbers of jobs created in the energy efficiency retrofit (EER) supply chain resulting from an investment in upgrading residential buildings in Florida. This investigation examined material supply chains stretching from mining to project installation for three product types: insulation, windows/doors, and heating, ventilating, and air conditioning (HVAC) systems. Outputs from the model are provided for the project, sales, manufacturing, and mining level. The model utilizes reverse-estimation to forecast the numbers of jobs that result from an investment. Reverse-estimation is a process that deconstructs a total investment into its constituent parts. In this research, an investment is deconstructed into profit, overhead, and hard costs for each level of the supply chain and over multiple iterations of inter-industry exchanges. The model processes an investment amount, the type of work and method of contracting into a prediction of the number of jobs created. The deconstruction process utilizes data from the U.S. Economic Census. At each supply chain level, the cost of labor is reconfigured into full-time equivalent (FTE) jobs (i.e. equivalent to 40 hours per week for 52 weeks) utilizing loaded labor rates and a typical employee mix. The model is sensitive to adjustable variables, such as percentage of work performed per type of product, allocation of worker time per skill level, annual hours for FTE calculations, wage rate, and benefits. This research provides several new insights into job creation. First, it provides definitions that can be used for future research on jobs in supply chains related to energy efficiency. Second, it provides a methodology for future investigators to calculate jobs in a supply chain resulting from an investment in energy efficiency upgrades to a building. The methodology used in this research is unique because it examines gross employment at the sub-industry level for specific

  7. Comparative Analysis of NOAA REFM and SNB3GEO Tools for the Forecast of the Fluxes of High-Energy Electrons at GEO

    Science.gov (United States)

    Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, Homayon; Sibeck, D. G.; Billings, S. A.

    2016-01-01

    Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field B(sub z) observations at L1. The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

  8. Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO

    Science.gov (United States)

    Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, H.; Sibeck, D. G.; Billings, S. A.

    2016-01-01

    Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

  9. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-01-01

    Full Text Available One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE and Cumulative Variation of Root Mean Square Error (CV-RMSE are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

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

    International Nuclear Information System (INIS)

    Ardakani, F.J.; Ardehali, M.M.

    2014-01-01

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

  11. Energy systems scenario modelling and long term forecasting of hourly electricity demand

    Directory of Open Access Journals (Sweden)

    Poul Alberg Østergaard

    2015-06-01

    Full Text Available The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles.

  12. Prior Flaring as a Complement to Free Magnetic Energy for Forecasting Solar Eruptions

    Science.gov (United States)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

    From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1,300 sunspot active regions across the 30 deg radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's full-disk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days, (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions, and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

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

    International Nuclear Information System (INIS)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F.; Khazanov, Igor

    2012-01-01

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

  14. Baseline energy forecasts and analysis of alternative strategies for airline fuel conservation

    Energy Technology Data Exchange (ETDEWEB)

    1976-01-01

    The objectives of this study were to identify measures to reduce airline fuel consumption and to evaluate the impact of these alternatives on fuel consumption through 1990. To evaluate the impact of fuel conservation strategies, baseline forecasts of airline activity and energy consumption to 1990 were developed. Alternative policy options to reduce fuel consumption were identified and analyzed for three baseline levels of aviation activity within the framework of an aviation activity/energy consumption model. By combining the identified policy options, a strategy was developed to provide incentives for airline fuel conservation. Strategies and policy options were evaluated in terms of their impact on airline fuel conservation and the functioning of the airline industry as well as the associated social, environmental, and economic costs. The need for strategies to conserve airline fuel is based on air transportation's dependence upon petroleum; the current lack of alternative energy sources; the potential for disruption of air service due to crises in fuel availability such as experienced during the OPEC oil embargo; and the overall national goal of energy independence through energy conservation in all consuming sectors. The transition from the current situation to that described by strategies and policy options may require difficult adjustments by the airline industry in the short term. In the long term, however, conservation strategies can enhance the health of the airline industry as well as its fuel efficiency.

  15. Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2018-03-01

    Full Text Available Carbon dioxide (CO2 emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO2 emissions are multiplex and the relationships between factors and CO2 emissions are complex and non-linear, a novel CO2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA to optimize the two parameters of the least squares support sector machine (LSSVM model, is proposed in this paper. The influential factors of CO2 emissions, including the gross domestic product (GDP, population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM, and the back propagation (BP neural network model, on CO2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO2 emissions forecasting. CO2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO2 emissions of China in 2020 shows that structural factors significantly affect CO2 emission forecasting results. The average annual growth of CO2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.

  16. Evaluation of energy fluxes in the NCEP climate forecast system version 2.0 (CFSv2)

    Science.gov (United States)

    Rai, Archana; Saha, Subodh Kumar

    2018-01-01

    The energy fluxes at the surface and top of the atmosphere (TOA) from a long free run by the NCEP climate forecast system version 2.0 (CFSv2) are validated against several observation and reanalysis datasets. This study focuses on the annual mean energy fluxes and tries to link it with the systematic cold biases in the 2 m air temperature, particularly over the land regions. The imbalance in the long term mean global averaged energy fluxes are also evaluated. The global averaged imbalance at the surface and at the TOA is found to be 0.37 and 6.43 Wm-2, respectively. It is shown that CFSv2 overestimates the land surface albedo, particularly over the snow region, which in turn contributes to the cold biases in 2 m air temperature. On the other hand, surface albedo is highly underestimated over the coastal region around Antarctica and that may have contributed to the warm bias over that oceanic region. This study highlights the need for improvements in the parameterization of snow/sea-ice albedo scheme for a realistic simulation of surface temperature and that may have implications on the global energy imbalance in the model.

  17. International wind energy development. World market update 2011. Forecast 2012-2016

    Energy Technology Data Exchange (ETDEWEB)

    2012-03-15

    The World Market Update 2011 is BTM Consult's seventeenth edition of this annual wind energy market report. The report includes more than 80 tables, charts and graphs illustrating global wind market development, as well as a wind market forecast for 2012 - 2016 and predictions for the wind market through 2021. The report delivers several views on the fast-growing wind market, including: 1) Record installation of 41.7 GW. 2) Strong presence of four Chinese wind turbine suppliers in the Top 10 list. 3) China maintains the No. 1 market position in the world, with 17.6 GW of new capacity. 4) Offshore wind is on track for increased contribution to wind power in Europe. 5) Market value will grow from Euro 52.2 billion in 2011 to Euro 86.3 billion in 2016. 6) Direct drive turbines now account for 21.2% of the world's supply of wind power capacity. 7) Wind power will deliver 2.26% of the world's electricity in 2012. 8) Forecasts and predictions to 2021 indicate that wind power can meet 8.0% of the world's consumption of electricity by 2021. International Wind Energy Development - World Update 2011 includes individual country wind market assessments, incentives around the world, and detailed analysis of both the demand and supply sides of the wind market in 2011. This year's report reviews the latest developments in hydraulic drivetrains, identifies the pros and cons, and compares the hydraulic technology to the industry's three currently established drivetrain technologies: conventional gear-, direct and hybrid-drivetrains. (Author)

  18. Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ziyi Yin

    2018-03-01

    Full Text Available Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP, the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR was introduced for comparison with the ANN. The results show the following: (a The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

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

    Energy Technology Data Exchange (ETDEWEB)

    Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)

    2014-04-30

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

  20. International wind energy development. World marked update 1999. Forecast 2000-2004

    International Nuclear Information System (INIS)

    2000-03-01

    This is the fifth issue of the annual World Market Update by BTM Consult ApS, covering the year 1999. All figures in the status refer to the end of year 1999. It is the last update from the 20th century, in which wind energy developed during the last two decades to become a very serious part of the world electricity supply. As in previous reports, the past 3 years' development in the wind energy sector is assessed, and the forecast looks 5 years ahead. Wind power is the world's fastest growing energy source, with an average annual growth rate of 40 % over the last five years. Wind energy is a clean and abundant energy source, and it is becomming a preferred source of energy not only due to the environmental benefits, but also because it has become increasingly cost competitive in the world energy markets. One of the most significant figures and trends from this fast growing market during 1999 was that the annual installation of new wind power capacity increased by 51 %, resulting in a cumulative installation by the end of 1999 of 13,932 MW. The growth rates in the wind industry can easily be compared to the growth rates in the IT sector, although the growth differ much from country to country. The high growth rates are still very much influenced by political and economical issues, but the continuously improved technology and thus also the redused cost of energy becomes more and more significant, and there are hardly any arguments left why wind energy should not play a very significant role in the electricity supply. Approximately 81 % of the new capacity of 3,922 were installed in Europe, emphasizing that this region is still the major market place. The US market picked up close to the PTC expiry date (Production Tax Credit) on June 30, 1999. In terms of single markets it was, however, the German market which once again took the lead with installed capacity of 1,568 MW. Germany thereby consolidated the position as the leading wind energy country in the world. Spain

  1. International wind energy development. World market update 1998. Forecast 1999-2003

    International Nuclear Information System (INIS)

    1999-03-01

    This is the fourth issue of the annual World Market Update from BTM Consult ApS, covering the year 1998. All figures in the status part refer to end of the year 1998, the past 3 years development is also assessed and the forecast looks 5 years ahead. The most significant figures and trends in 1998 were: The marketplace - The annual installation of new wind power capacity increased by 55% resulting in a cumulative installation by the end of 1998 of 10.153 MW. 1.766 MW was installed in Europe and the region is still the leading market regarding utilization of wind energy. The US market took a rapid pace and installed 577 MW during the year. The large Enron Wind Corp has taken the larger part of this market. On the supply side Danish NEG Micon A/S has consolidated the position as being the supplier of the most MW wind capacity in the world and the company has a world market share of 23,5 per cent. The company acquired the Danish Wind World af 1997 A/S which was among the larger companies in 1997. Also the Dutch manufacturer NedWind B.V. was acquired by NEG Micon A/S curing 1998. The group of 'other' manufactureres represents a minor percentage of deliveries than earlier and concentration in the industry seems to continue. The liberalized Energy Market and how to position the industry in this different economic environment will be a challenge for the wind industry way into the next century. In Europe, the European Commission's draft Directive with proposal for an outline of common rules for support of among other renewables wind energy has been set on another route which seems to delay the paper. In the US there are still hopes for a new period with PTC (Production Tax Credit). There are in some States hopes among the wind energy people that the 'Green Market Programs' will play a more dominant role in the future. In Asia the crises seems to halt the wind power development. Forecast and Technical trends - Based on the positive trends in the markets for wind power

  2. Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

    Science.gov (United States)

    Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

    2012-12-01

    Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

  3. Forecast of energy demand in China and introduction of nuclear power using the clean development mechanism

    International Nuclear Information System (INIS)

    Ikemoto, Ichiro

    2003-01-01

    As an economic energy source with low greenhouse gas emissions and essentially no resource limitations, nuclear power is a promising option for meeting the rapidly growing energy demands of China that is being driven by rapid population and economic growth. This paper examines an introduction scenario for nuclear power in China by using the clean development mechanism, based on quantitative evaluation of energy demand forecasts and the nuclear fuel cycle through 2100. The results of the case study concluded that in the short to mid term, large-scale light water reactors will primarily be sited in coastal areas where infrastructure development is advanced. In the future, as dispersed power sources in inland areas, small scale FBRs will be preferred due to their promising safety, operation and maintenance characteristics, ease of transportation of plant equipment and plant construction and the possibility of on-site nuclear fuel cycle. Evaluation of nuclear fuel cycle showed that this introduction scenario is feasible considering natural Uranium demand, Uranium enrichment capacity and reprocessing capacity. (author)

  4. Forecasting energy consumption and energy related CO2 emissions in Greece. An evaluation of the consequences of the Community Support Framework II and natural gas penetration

    International Nuclear Information System (INIS)

    Christodoulakis, N.M.; Kalyvitis, S.C.; Lalas, D.P.; Pesmajoglou, S.

    2000-01-01

    This study seeks to assess the future demand for energy and the trajectory of CO2 emissions level in Greece, taking into account the impact of the Community Support Framework (CSF) II on the development process and the penetration of natural gas, which is one of the major CSF II interventions, in the energy system. Demand equations for each sector of economic activity (traded, non-traded, public and agricultural sector) and for each type of energy (oil, electricity and solid fuels) are derived. The energy system is integrated into a fully developed macroeconometric model, so that all interactions between energy, prices and production factors are properly taken into account. Energy CO2 forecasts are then derived based on alternative scenarios for the prospects of the Greek economy. According to the main findings of the paper the growth pattern of forecast total energy consumption closely follows that of forecast output showing no signs of decoupling. As regards CO2 emissions, they are expected to increase with an annual average rate, which is higher than world forecasts. 17 refs

  5. A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting

    Directory of Open Access Journals (Sweden)

    KAMPOUROPOULOS, K.

    2014-02-01

    Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.

  6. Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption

    International Nuclear Information System (INIS)

    Azadeh, A.; Tarverdian, S.

    2007-01-01

    This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures. First, time-series model is developed as a benchmark for GA and simulation. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The GA and simulated-based GA models are then developed by the selected time-series model. Therefore, there are four treatments to be considered in analysis of variance (ANOVA) which are actual data, time series, GA and simulated-based GA. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan Multiple Range Test (DMRT) method of paired comparison is used to select the optimum model, which could be time series, GA or simulated-based GA. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best-fit GA model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm

  7. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S. F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  8. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S.F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  9. Potential of Offshore Wind Energy and Extreme Wind Speed Forecasting on the West Coast of Taiwan

    Directory of Open Access Journals (Sweden)

    Pei-Chi Chang

    2015-02-01

    Full Text Available It is of great importance and urgency for Taiwan to develop offshore wind power. However, relevant data on offshore wind energy resources are limited. This study imported wind speeds measured by a tidal station and a buoy into the software WAsP to estimate the high-altitude wind speeds in the two areas. A light detection and ranging (Lidar system was set up near the tidal station and buoy. High-altitude wind speeds measured by the Lidar system were compared with the WAsP-estimated values, and it was discovered that the two data sets were consistent. Then, long-term wind speed data observed by buoys and tidal stations at various locations were imported into WAsP to forecast wind speeds at heights of 55–200 m on the west coast of Taiwan. The software WAsP Engineering was used to analyze the extreme wind speeds in the same areas. The results show that wind speeds at 100 m are approximately 9.32–11.24 m/s, which means that the coastal areas of west Taiwan are rich in wind energy resources. When a long-term 10-min average wind speed is used, the extreme wind speed on the west coast is estimated to be between 36.4 and 55.3 m/s.

  10. Forecasting model for energy consumption in South Africa correlated with the income

    Energy Technology Data Exchange (ETDEWEB)

    Siti, M.W.; Nicolae, D.V.; Jimoh, A.A. [Tshwane Univ. of Technology, Pretoria (South Africa). Dept. of Electrical Engineers

    2008-07-01

    Demand-side-management (DSM) programs are used to influence customer electricity usage and reduce capital and operating costs for electric utilities. Escalating fuel costs and regulatory pressure are now causing some municipalities to consider demand-side options as alternatives to traditional resource planning. A mathematical model for forecasting energy consumption in South Africa was presented in this paper. The model used data from an energy consumption audit conducted in South Africa, and was correlated to the income of consumers. The model was used to study the impact of society, personality, and fixed contribution indexes on electricity consumption. Results of the modelling study showed that a higher fixed contribution factor indicates a more developed economic infrastructure and higher electrical expenditure. The personality index influences dynamic expenditures that are likely to be improved by electricity awareness programs. The study also showed that small changes in the society index can have a significant impact on electricity consumption. The model can be extrapolated to predict load profiles for particular localities or communities based on household income data. The model can also be used to validate load shaping, profiling, and prediction approaches. 6 refs., 4 tabs., 6 figs.

  11. Modeling and forecasting energy flow between national power grid and a solar–wind–pumped-hydroelectricity (PV–WT–PSH) energy source

    International Nuclear Information System (INIS)

    Jurasz, Jakub

    2017-01-01

    Highlights: • A MINLP model for grid connected PV-WT-PSH is proposed. • A method for simulating and forecasting energy flow has been developed. • A probabilistic model is compared to artificial neural network approach. - Abstract: The structure of modern energy systems has evolved based on the assumption that it is the demand side which is variable, whilst the supply side must adjust to forecasted (or unforecasted) changes. But the increasing role of variable renewable energy sources (VRES) has led to a situation in which the supply side is also becoming more and more unpredictable. To date, various approaches have been proposed to overcome this impediment. This paper aims to combine mixed integer modeling with an Artificial Neural Networks (ANN) forecasting method in order to predict the volume of energy flow between a local balancing area which is using PV–WT–PSH and the national power system (NPS). Calculations has been performed based on the hourly time series of wind speed, irradiation and energy demand. The results indicate that both probabilistic and ANN models generate comparably accurate forecasts; however, the opportunity for improvement in the former appears to be significantly greater. The mean prediction error (for a one hour ahead forecasts) for the best model was 0.15 MW h, which amounts to less than 0.2% of a mean hourly energy demand of the considered energy consumer. The proposed approach has huge potential to reduce the impact of VRES on the NPS operation as well as can be used to facilitate the process of their integration and increase their share in covering energy demand.

  12. International wind energy development. World market update 2002. Forecast 2003-2007

    International Nuclear Information System (INIS)

    2003-03-01

    Systems maintained its position as No. 1 supplier, with a market share of 22.2%, followed by Enercon with 18.5%. The four leading companies - Vestas (DK), Enercon (GE), NEG Micon (DK) and Gamesa (SP) - together accounted for two thirds of the total supply of wind power capacity. The most significant trend in the market place was the continuing increase in the size of commercial wind turbines installed. The forecast covers the wind energy development over the next five years up to the end of 2007. This year's forecast indicates overall expansion, with an average growth rate of 11.2% p.a. for new installed capacity. This growth is not, however, evenly distributed over the period. The highest rate is expected during 2003, with 24%, followed by two modest years and then, by the end of the forecast period, a growth of 13.7% and 11.3% in 2006 and 2007 respectively. The special topic in this World Market Update focus on upscaling of wind turbines to multi-MW size. (BA)

  13. Gas and electric power 2003. Evolution of the energy panorama in Europe: evaluation, forecasting and precautions

    International Nuclear Information System (INIS)

    Boigegrain, R.; Tran Thiet, J.P.; Givry, L.; Lapierre, A.; Vivies, P. de; Brelle, B.; Vedrenne, Ph.; Didier, E.; Munch, P.; Rodrigues, St.; Lermusieau, Ph.; Macchiati, A.; Lamboley, Ph.; Bouchard, G.; Canetti, J.; Bresson, Th. de; Chevalier, J.M.; Saint Andre, B.; Werquin, A.; Mouton, F.R.; Boulanger, Ph.; Vivies, P. de; Terzian, P.

    2003-11-01

    This 12. international congress on gas and electricity covers the following topics: 1 - change in the energy panorama in Europe: statement, forecasting and precautions: fusions, acquisitions, partnerships and their consequences; evolution of the regulation: actors, decisions and time delays (regulation of electricity and gas in Europe - convergencies and divergences; specificities of the French gas and electricity markets and their perspectives of evolution; focus on the 2003 highlights: the January 3, 2003 law, about 20 new decrees and the 2. gas directive); market regulation: new missions, powers and limitations of the different actors (mission, power and place of regulation authorities today and their possible evolution, status competences extension towards gas; consequences of the introduction of the adjustment mechanism, new stakes of power transportation networks; stakes for gas transport and storage in France and in Europe); spot markets and suitable solutions for industrialists (short-term management of risks; juridical and legal precautions to take before starting a power trade activity; short- and medium-term risk management possibilities; 2 - markets opening and new strategies of energy purchase and selling: strategies of foreign actors in France and opportunities for French actors abroad (market opening and its stakes, specificities and opportunities of the energy market in Italy; challenges and opportunities of gas markets opening in Europe: the Ruhrgas approach; stakes of the French market opening: experience feedback of Endesa Europe); repositioning of activities (the new position of Gaz de France (GdF); the deregulated market: risks and opportunities); changing of supplier or partnerships power (towards commercial repositioning); round table: regulators, suppliers, purchasers: are you ready? The congress ends with a practical training course emceed by Endesa: the French electricity and gas markets in the European context; the management of the

  14. International wind energy development. World market update 2010. Forecast 2011-2015

    Energy Technology Data Exchange (ETDEWEB)

    2011-03-15

    This is the sixteenth edition of the annual World Market Update produced by BTM Consult ApS - a part of Navigant Consulting, and covers developments in the wind energy sector during 2010. As in previous editions, the report also assesses important changes over the last three years and forecasts progress for five years ahead. The special topic in this year's WMU is a review of Direct-Drive concept versus traditional Drive Train with gearbox. The global market for wind power produced a record for new installations in 2010 of 39.4 GW installed capacity, however, with a much lower growth rate than in the period 2005 to 2009. The rapid increase in the rate of installations in both Asia and the US was already clear in 2008-09. That trend has continued in China but the US experienced a significant slow-down in 2010. Europe stayed relatively stable - old markets stagnated but new emerging markets grew. Another new reality is that most of the world's manufacturing of wind turbines now takes place in China. Companies producing wind turbines there have experienced an explosive rate of growth. As a result four Chinese companies are among the world's Top Ten turbine manufacturers. An inevitable impact of this shift is that the market shares of the traditional industry leaders from the US and Europe have decreased significantly with Vestas and Siemens as exception in 2010. At the same time a rapid expansion of manufacturing capacity by European turbine makers has taken place in the US. Europe contributed 29.9% of the newly added capacity - 10,920 MW - taking the continent's total wind power generation capacity to 87,565 MW. The growth in Asia's markets has once again been staggering. With 21,130 MW of new installations, South and East Asia accounted for 53.6% of the global total in 2010.China was the major contributor, with 18,928 MW of new capacity, 37% over that of 2009. In terms of cumulative installed wind power, China surpassed the US in 2010, with

  15. International wind energy development. World market update 2012. Forecast 2013-2017

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-03-15

    The BTM wind report, World Market Update 2012, published by Navigant Research, is the eighteenth edition of this annual wind energy market report. The report includes more than 80 tables, charts and graphs illustrating global wind market development, as well as a wind market forecast for 2013?2017 and highlighted trends for the wind market through 2022. The report delivers several views on the fast?growing wind market, including: 1) More than 285 GW of wind power now installed globally; 2) 45GW of new capacity added in 2012, including 1.1 GW from offshore wind; 3) The United States surpassed China as the largest market in terms of new installations in 2012; 4) Europe lost its position as the largest world region in terms of new installations; 5) Wind installations in the Americas grew by 12.3 percent compared with 2011; 6) Big shake?up in the top ten wind turbine supplier ranking; 7) Strong Chinese presence among top 15 wind owner?operators; 8) Wind market structures continue to evolve; 9) The penetration of wind power in the world's electricity supply has reached 2.62 percent; 10) Offshore wind more than doubled the capacity added in 2011, with more than 4 GW currently under construction. With the addition of 44,951 MW in new installations in 2012, world wind power capacity grew to around 285,700 MW, an increase in the total wind power installation base of 18.6 percent. Market growth year-over-year in 2012, though a modest 7.8 percent, was still higher than in 2011. Average annual growth for the past five years has been 17.8 percent, achieved during the aftermath of the 2008 financial crisis, with traditionally large markets for wind power in economic recession in America and Europe. The wind power industry continues to demonstrate its ability to rapidly evolve to meet new demands in markets that face a variety of challenges. The focus on product diversification grows with wind turbine vendors designing machines for maximum energy production in low wind speed

  16. International wind energy development. World market update 2009. Forecast 2010-2014

    Energy Technology Data Exchange (ETDEWEB)

    2010-03-15

    This is the fifteenth edition of the annual World Market Update produced by BTM Consult ApS, and covers developments in the wind energy sector during 2009. As in previous editions, the report also assesses important changes over the last three years and forecasts progress for five years ahead. The special topic in this year's WMU is an evaluation of the aftermath of the COP-15 climate change negotiations in relation to future wind power development. The global market for wind power not only produced a record for new installations in 2009 of 38 GW installed capacity, it also created a new order in the balance of international wind power. The rapid increase in the rate of installations in both Asia and the US was already clear in 2008; that trend has continued at a faster pace in 2009. By far the largest number of new wind projects were seen in the US and China. Another new reality is that most of the world's manufacturing of wind turbines now takes place in China. As a result three Chinese companies are among the world's top ten turbine manufacturers. At the same time a rapid expansion of manufacturing capacity by European turbine makers has taken place in the US. Europe contributed 28.2% of the newly added capacity - 10,738 MW - taking the continent's total wind power generation capacity to 76,553 MW. The growth in Asia's markets has once again been staggering. With 14,991 MW of new installations, South and East Asia accounted for 39.4% of the global total in 2009. China was the major contributor, with 13,750 MW of new capacity, more than double that installed in 2008. In terms of cumulative installed wind power, the US is still the world leader, with 35,159 MW. China overtook Germany with a margin of less than 50 MW. China now has a total of 25,853 MW, followed by Germany's 25,813 MW. A new world order in wind power has become a reality. The forecast released in this WMU shows an average growth rate of 13.5% for the period 2010

  17. Short-term forecast of energy use and energy supply 2011-2013. Spring 2012; Kortsiktsprognos oever energianvaendning och energitillfoersel 2011-2013. Vaaren 2012

    Energy Technology Data Exchange (ETDEWEB)

    Lindblom, Helen; Nilsson, Lars; Persson, Annika; Anners, Charlotte; Sahlin, Mikaela

    2012-11-01

    In 2010, the energy use was around 409 TWh, which represents an increase of 9 percent compared with 2009. The increase depends on the industry's recovery from recession in 2008-2009, and that 2010 was much colder than normal. Energy consumption is projected to decrease to 381 TWh in 2011 and then increase during the remaining forecast years to reach 391 TWh in 2012 and 393 TWh in 2013. Industrial energy use is expected in 2013 to be 143 TWh, a decrease of 3 percent compared with 2010. Energy use within the transport sector for 2013 is forecast to amount to 94 TWh, a decline of nearly 2 percent from 2010 levels. The energy consumption for residential and service sector is estimated to 156 TWh by 2013, a decrease of 6 percent compared to 2010.

  18. Long-Term Forecast 2012 - An impact analysis of existing policy instruments in energy- and climate area; Laangsiktsprognos 2012 - En konsekvensanalys av gaellande styrmedel inom energi- och klimatomraadet

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-08-01

    The Energy Agency has a mandate that under 'Ordinance on climate reporting' (SFS 2005:626) out projections for the energy sector of the European Parliament and Council Decision No 280/2004/EC concerning a 'Mechanism for monitoring the emissions of the Community greenhouse gas'. This report contains a reference trajectory until 2030, and two sensitivity scenarios. The forecast is based on existing instruments, which means that results of the report should not be regarded as a proper projection of future energy, but as the impact of current policy instruments given different conditions such as economic growth and fuel prices. The Energy Authority's long-term forecasts are studied energy system's long-term development on the basis of policy instruments and several assumed conditions. The conditions for this long-term prognosis was established in January 2012 and has its basis in the policy instruments decided until the turn of 2011/2012. The work was partially done in conjunction with the Environmental Protection Agency assignments 'Assignment to provide input to a Swedish road map for Sweden without greenhouse gas emissions in 2050' as reported in December 2012. For a short-term development of the energy system the reader is referred to the Energy Authority's short-term forecasts that extend two to three years into the future and that are produced twice a year. Energy Agency's long-term projections are impact assessments with time horizon of 10-20 years which aims to describe the energy system's future development, provided a range of assumed conditions. If any of these conditions change it will also change forecast results. Economic development is an important assumption for the assessment of future energy.

  19. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien; Fu, Hsin-Chia; Tseng, Cheng-Lung

    2012-01-01

    Analyses and forecasts of carbon emissions, energy consumption and real outputs are key requirements for clean energy economy and climate change in rapid growth market such as China. This paper employs the nonlinear grey Bernoulli model (NGBM) to predict these three indicators and proposes a numerical iterative method to optimize the parameter of NGBM. The forecasting ability of NGBM with optimal parameter model, namely NGBM−OP has remarkably improved, compared to the GM and ARIMA. The MAPEs of NGBM−OP for out-of-sample (2004–2009) are ranging from 1.10 to 6.26. The prediction results show that China’s compound annual emissions, energy consumption and real GDP growth is set to 4.47%, −0.06% and 6.67%, respectively between 2011 and 2020. The co-integration results show that the long-run equilibrium relationship exists among these three indicators and emissions appear to be real output inelastic and energy consumption elastic. The estimated values cannot support an EKC hypothesis, and real output is significantly negative impact on emissions. In order to promote economic and environmental quality, the results suggest that China should adopt the dual strategy of increasing energy efficiency, reducing the loss in power transmission and distribution and stepping up energy conservation policies to reduce any unnecessary wastage of energy. -- Highlights: ► The proposed NGBM-OP has a strong forecasting ability with MAPEs less than 6.3%. ► The NGBM-OP is applied to predict emissions, energy and output during 2009–2020. ► Prediction results show China will actively conserve resources and protect the environment. ► The long-run equilibrium relationship exists between emissions, energy and output. ► Emissions appear to be output inelastic and energy consumption elastic.

  20. Higgs-dilaton cosmology: An inflation-dark-energy connection and forecasts for future galaxy surveys

    Science.gov (United States)

    Casas, Santiago; Pauly, Martin; Rubio, Javier

    2018-02-01

    The Higgs-dilaton model is a scale-invariant extension of the Standard Model nonminimally coupled to gravity and containing just one additional degree of freedom on top of the Standard Model particle content. This minimalistic scenario predicts a set of measurable consistency relations between the inflationary observables and the dark-energy equation-of-state parameter. We present an alternative derivation of these consistency relations that highlights the connections and differences with the α -attractor scenario. We study how far these constraints allow one to distinguish the Higgs-dilaton model from Λ CDM and w CDM cosmologies. To this end we first analyze existing data sets using a Markov chain Monte Carlo approach. Second, we perform forecasts for future galaxy surveys using a Fisher matrix approach, both for galaxy clustering and weak lensing probes. Assuming that the best fit values in the different models remain comparable to the present ones, we show that both Euclid- and SKA2-like missions will be able to discriminate a Higgs-dilaton cosmology from Λ CDM and w CDM .

  1. Medium-term forecast up to 2016 as required by the Renewable Energies Law; Die EEG-Mittelfristprognose bis 2016

    Energy Technology Data Exchange (ETDEWEB)

    Reichmuth, Matthias [Leipziger Institut fuer Energie GmbH, Leipzig (Germany)

    2012-01-15

    Article 3 of the Ordinance on the Implementation of the Ordinance on the Further Development of the Federal Compensation Mechanism obliges transmission system operators to publish not only the following year's reallocation charge pursuant to the Federal Electricity Feed-in Law but also, by the 15 November of each calendar year, a forecast on the probable range of the reallocation charge in the year after next, and further of expected electricity feed-in rates and electricity sales for the following five calendar years. For this purpose they must also determine and publish the progress over time of the average compensation due to plant operators and the amounts of network charges avoided and must do so separately for each of the energy carriers promoted under Renewable Energies Law. The present article shows the results of the current feed-in forecast in compact form.

  2. Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Gianfranco Chicco

    2015-12-01

    Full Text Available Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological quantities, provide fundamental information. The weak point of the forecasts depends on variable sky conditions, when the clouds successively cover and uncover the solar disc. This causes remarkable positive and negative variations in the irradiance pattern measured at the photovoltaic (PV site location. This paper starts from 1 to 3 days-ahead solar irradiance forecasts available during one year, with a few points for each day. These forecasts are interpolated to obtain more irradiance estimations per day. The estimated irradiance data are used to classify the sky conditions into clear, variable or cloudy. The results are compared with the outcomes of the same classification carried out with the irradiance measured in meteorological stations at two real PV sites. The occurrence of irradiance spikes in “broken cloud” conditions is identified and discussed. From the measured irradiance, the Alternating Current (AC power injected into the grid at two PV sites is estimated by using a PV energy conversion model. The AC power errors resulting from the PV model with respect to on-site AC power measurements are shown and discussed.

  3. Japan's actual energy supply/demand in 1986 and background - drastically changing economic/energy situations upset plans and forecasts by a wide margin

    Energy Technology Data Exchange (ETDEWEB)

    Fujime, K

    1987-05-01

    In 1986 the value of the yen soared and there was a lowering of interest rates and a slump in crude oil prices. These drastic changes in economic/energy situations brought about a completely different picture of Japan's energy supply and demand from originally expected. Energy demand from large industrial users was lowered and impacts of price fluctuations on energy supply and demand were uneven. Topics covered in the paper are: economic/industrial trends; energy price trends; actual energy supply and demand including electricity, oil, town gas, coal and LNG (liquefied natural gas); trends of major energy-consuming industries and energy consumption including steel industry, paper/pulp industry, cement industry and petrochemical industry; plans/forecasts completely off the track due to drastically changing economic/energy situations.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-01-01

    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

  5. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  6. Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

    International Nuclear Information System (INIS)

    Hargreaves, Anthony; Cheng, Vicky; Deshmukh, Sandip; Leach, Matthew; Steemers, Koen

    2017-01-01

    Highlights: • An innovative model for testing combinations of spatial planning and decentralised energy supply. • An improved method of modelling the spatial variability of energy consumption per dwelling type. • Shows how spatial planning would affect the future carbon reduction of decentralised supply. • Forecasts the future carbon reduction and costs of retrofitting and decentralised supply. • A method of forecasting how residential space would affect the suitability of decentralised supply. - Abstract: Low carbon energy supply technologies are increasingly used at the building and community scale and are an important part of the government decarbonisation strategy. However, with their present state of development and costs, many of these decentralised technologies rely on public subsidies to be financially viable. It is questionable whether they are cost effective compared to other ways of reducing carbon emissions, such as decarbonisation of conventional supply and improving the energy efficiency of dwellings. Previous studies have found it difficult to reliably estimate the future potential of decentralised supply because this depends on the available residential space which varies greatly within a city region. To address this problem, we used an integrated modelling framework that converted the residential density forecasts of a regional model into a representation of the building dimensions and land of the future housing stock. This included a method of estimating the variability of the dwellings and residential land. We present the findings of a case study of the wider south east regions of England that forecasted the impacts of energy efficiency and decentralised supply scenarios to year 2031. Our novel and innovative method substantially improves the spatial estimates of energy consumption compared to building energy models that only use standard dwelling typologies. We tested the impact of an alternative spatial planning policy on the future

  7. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)

    2010-11-15

    There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)

  8. Forecast Model of Russia’s Gross Domestic Product Depending on Financial Instruments of Trade in Energy and Commodities

    Directory of Open Access Journals (Sweden)

    Samkov T.L.

    2018-04-01

    Full Text Available Methodology of forecasting the gross domestic product (GDP growth for complex socio-economic systems is projected on economic conditions of the Russian Federation. The most important factors affecting GDP change, development of a GDP forecast econometric model for the Russian economy and the methodology of the model use are identified. The model is used as a source of information necessary for the analysis of territorial multi-sectoral objects (ATMO model which we developed. ATMO model is the model of planning regional sectoral production systems of independent corporate participants and can predict their behavior utilizing game approach. Non-systemic "signal" information (GDP growth rate is necessary to change the strategies. The factors for predicting are the primary and secondary financial instruments of trade in energy and raw commodities. The principle of behavioral imitation is introduced for the first time in relation to a class of such tasks. Technology of decisions making corresponds to the knowledge of managers and officials rather than to that of analysts. The forecast model reflects the "ordinary" view of mentioned decision makers on the nature of GDP dependence on trade in specific goods (oil, gas, grain, gold, silver, copper, etc.. Almost any company leader will be able to use this set to forecast GDP as guidelines for further development of the enterprise. The ATMO model makes it possible to influence the economy to achieve macroeconomic goals and gives the opportunity to increase the available volumes of energy resources for export, e.g., for functioning of the emerging gas hub in EU.

  9. Investments in the Quebec energy sector: Increase of 27% in 1991 and forecast rise of 9% in 1992

    International Nuclear Information System (INIS)

    Anon.

    1992-01-01

    A compilation is presented of the sums invested in 1991 and the projected investments for 1992 in the Quebec energy sector. Historical data back to 1982 are also included. In 1991, the total investment rose to $4,328 million, or 27% more than in 1990. The year 1992 is expected to see a more modest 9% increase in energy investments. The relative value of energy investments compared to total Quebec investments was 15.6% in 1991 and is forecast to attain 16.9% in 1992. The large increase in energy investment is largely due to investments in the electric power sector, which receives ca 93% of Quebec energy investment. In the petroleum sector, preliminary data indicate that total investment in 1991 and 1992 will be $192.5 million and $203.1 million respectively, mostly for refining and distribution. In the natural gas sector, the historical data show a large peak at 1983 of $424 million, descending to the $50-70 million level starting in 1987. Natural gas investments in 1991 rose to $101.6 million, most of which went towards extending the distribution network. For 1992, $68.5 million is forecast to be invested. In the electric power sector, total 1991 investment was ca $4 billion, a 29% increase over 1990; 1992 investment is forecast at $4.46 billion. In 1991, the investment in electricity production totalled ca $2 billion and investment in power transmission $970 million, the latter mainly dedicated to construction of a 450 kV dc power line and to a network improvement program. Investment in power distribution was $567 million, while other investments such as communications, buildings, and technological activities amounted to $450 million. 4 figs., 5 tabs

  10. International wind energy development. World market update 2000. Forecast 2001-2005

    International Nuclear Information System (INIS)

    2001-03-01

    In the year 2000, the wind power development took another major step forward. Installed capacity set a new record with the additon of 4,495 MW of new generating capacity, this is 574 MW more than the record set in 1999. Growth in new capacity declined from 51% in 1999 to 15% in 2000. Nevertheless, the trend of wind energy being preferred over other technologies for new generating capacity is continuing. Europe remains the major market for wind power. Of the new capacity added in 2000, 86% was installed in Europe. Germany again took the lead as the single most active market with the installation of 1,665 MW in new capacity, 100 MW more than in the previous year. In doing so, Germany extended its reign as the world's leading developer of wind energy. Spain also took a major step towards meeting its renewable energy goals with the installation of some 1,024 MW of new wind generating capacity. Denmark replaced the United States as the world's third largest market. The Danes installed a new record of 603 MW. This was due to a last-minute rush of contracts signed at the end of 1999 in order to utilize attractive payment rates for new installations, which expired at the end of the year. The turbines were then installed in 2000. Market leaders among wind turbine manufacturers changed from previous years. Vestas Wind Systems A/S is now the world's largest manufacturer of wind turbines, followed by Gamesa S.A. of Spain (Vestas owns 40% of the shares in the Spanish company). Enercon GmbH has become the third largest manufacturer of wind turbines. It is interesting to note that Enercon GmbH made steady progress in sales to several emerging markets throughout the world. New on the Top-10 list is an Indian manufacturer. Suzlon Energy Ltd. installed 103 MW of capacity and thereby becomes the number 10 in the list of the worlds largest manufacturer. The cumulative installed capacity of 18,449 MW at the end of 2000 will supply approximately 37 TWh (37 billion kWh) per year. The

  11. Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

    Energy Technology Data Exchange (ETDEWEB)

    Neto, Joao C. do L, E-mail: jcaldas@ufam.edu.br [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Costa Junior, Carlos T. da [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil); Bitar, Sandro D.B. [Group of Optimization and Fuzzy Systems, Federal University of Amazonas, General Rodrigo Octavio Jordao Ramos Avenue, 3000, Academic Campus, 69077-000 Manaus, Amazonas (Brazil); Junior, Walter B. [Postgraduate Program in Electrical Engineering, Institute of Technology, Federal University of Para, Augusto Correa Street, 1, Guama, 66075-900 Belem, Para (Brazil)

    2011-09-15

    Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the {alpha}-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: > A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. > It assists the decision-making process for planning the expansion in isolated thermoelectric systems. > The {alpha}-cut concept facilitated the evaluation of risks for the cost of electricity production. > Provides decisions using various forecasted interval for this cost with different membership values.

  12. Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models

    International Nuclear Information System (INIS)

    Neto, Joao C. do L; Costa Junior, Carlos T. da; Bitar, Sandro D.B.; Junior, Walter B.

    2011-01-01

    Understanding the uncertainty inherent in the analysis of diesel fuel consumption and its impact on the generation of electricity is an important topic for planning the expansion of isolated thermoelectric systems in the state of Amazonas. In light of this, a decision support system has been developed to forecast the cost of electricity production using non-stationary data by integrating the methodology of time series models with fuzzy systems and optimization tools. The method presented herein combines the potential of the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal ARIMA (SARIMA) models, such as the forecasting tool, with the advantages of fuzzy set theory to compensate for the uncertainties and errors encountered in the observed data, which would degrade the validity of forecasted values. The results show that incorporation of the α-cut concept facilitated the evaluation of risks while allowing simultaneous consideration of intervals for the unitary cost of energy production. This provides the analyst with the ability to make decisions using various predicted intervals with different membership values instead of the common practice of simply using the specific costs. - Highlights: → A decision support system has been developed using SARIMA with fuzzy systems and optimizations tools. → It assists the decision-making process for planning the expansion in isolated thermoelectric systems. → The α-cut concept facilitated the evaluation of risks for the cost of electricity production. → Provides decisions using various forecasted interval for this cost with different membership values.

  13. Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

    Science.gov (United States)

    Anghileri, D.; Castelletti, A.; Burlando, P.

    2016-12-01

    European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-09-01

    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

  15. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

    International Nuclear Information System (INIS)

    Wang, Jie; Wang, Jun

    2016-01-01

    In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. - Highlights: • A new forecasting model is developed by a random Elman recurrent neural network. • The forecasting accuracy of crude oil price fluctuations is improved by the model. • The forecasting results of the proposed model are more accurate than compared models. • Two new distance analysis methods are applied to confirm the predicting results.

  16. EnerFuture Energy Scenarios to 2035 'Understanding our Energy Future'. Key graphs and analysis, Enerdata - Global Energy Forecasting - February 2014

    International Nuclear Information System (INIS)

    2014-01-01

    The EnerFuture service provides projections to 2035 of energy supply and demand across the world, powered by the POLES model, to help you with what to expect in the energy industry in the mid-term. Our energy forecasting team have developed three key energy scenarios (Balance, Emergence and Renaissance) to illustrate possible futures. Balance scenario: Balance provides an outlook of the energy system up to 2035 based on current policies and trends. Sustained growth of China and other emerging countries is a powerful driver of global energy demand, but confirmed energy policy commitments in several regions play a key role in controlling the pace of growth. However, non-coordinated policies result in soaring CO_2 emissions across the world and energy prices rise. Emergence scenario: This scenario explores the implications of more stringent climate policies, with more ambitious efforts on energy efficiency, initiatives to phase out fossil fuel subsidies and a real emergence of renewable technologies. Europe goes beyond its -20% targets by 2020, and the OECD and emerging countries meet their Copenhagen objectives. Following this, a new green deal is launched to reduce world emissions by a factor of 2 by 2050. Renaissance scenario: With strong efforts in the exploitation and production of unconventional oil and gas resources, the world encounters a fossil fuels renaissance with the appearance of new key actors and ultimately new geopolitical configurations changing the energy independence of several countries. For climate efforts, this new paradigm leads to progressively weaker policies. Further analysis and key findings are available here: - Increasing economic activity and wealth drives energy consumption, in a balance between energy prices and innovation; - As Non-OECD exceeds OECD oil demand, massive financial flows underlie the shifts in global oil trade; - Optimistic resource assumptions and moderate production costs would lead to an oil production Renaissance

  17. The long-term forecast of Taiwan's energy supply and demand: LEAP model application

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Yophy, E-mail: yohuanghaka@gmail.com [Deptartment of Public Finance and Tax Administration, National Taipei College of Business, Taipei Taiwan, 10051 (China); Bor, Yunchang Jeffrey [Deptartment of Economics, Chinese Culture University, Yang-Ming-Shan, Taipei, 11114, Taiwan (China); Peng, Chieh-Yu [Statistics Department, Taoyuan District Court, No. 1 Fazhi Road, Taoyuan City 33053, Taiwan (China)

    2011-11-15

    The long-term forecasting of energy supply and demand is an extremely important topic of fundamental research in Taiwan due to Taiwan's lack of natural resources, dependence on energy imports, and the nation's pursuit of sustainable development. In this article, we provide an overview of energy supply and demand in Taiwan, and a summary of the historical evolution and current status of its energy policies, as background to a description of the preparation and application of a Long-range Energy Alternatives Planning System (LEAP) model of Taiwan's energy sector. The Taiwan LEAP model is used to compare future energy demand and supply patterns, as well as greenhouse gas emissions, for several alternative scenarios of energy policy and energy sector evolution. Results of scenarios featuring 'business-as-usual' policies, aggressive energy-efficiency improvement policies, and on-schedule retirement of Taiwan's three existing nuclear plants are provided and compared, along with sensitivity cases exploring the impacts of lower economic growth assumptions. A concluding section provides an interpretation of the implications of model results for future energy and climate policies in Taiwan. - Research Highlights: > The LEAP model is useful for international energy policy comparison. > Nuclear power plants have significant, positive impacts on CO{sub 2} emission. > The most effective energy policy is to adopt demand-side management. > Reasonable energy pricing provides incentives for energy efficiency and conservation. > Financial crisis has less impact on energy demand than aggressive energy policy.

  18. Influence of the Scientific and Technical Progress upon the Fuel and Energy Complex of Ukraine until 2050: Methodology of Assessment of Long-term Forecasts

    Directory of Open Access Journals (Sweden)

    Antonenko Leonid A.

    2013-12-01

    Full Text Available The article makes an attempt, using new methodological initial grounds by the works of leading foreign authors, to present an aggregate of qualitatively different conditions of development of the fuel and energy complex of Ukraine until 2050 in the context of forecasts of development of USA until 2100. The authors justify factors that significantly differentiate trajectories of future development of the Ukrainian energy sector and USA energy sector, which envisages development of own methodology and realisation of forecasts officially already developed by Ukrainian scientists. On the basis of this the article presents the authors’ comparative analysis of fuel and energy complexes of Ukraine and China.

  19. State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

    Science.gov (United States)

    Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

    2018-05-01

    Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

  20. Real-time energy resources scheduling considering short-term and very short-term wind forecast

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Marco; Sousa, Tiago; Morais, Hugo; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center

    2012-07-01

    This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process the update of generation and consumption operation and of the storage and electric vehicles storage status are used. Besides the new operation conditions, the most accurate forecast values of wind generation and of consumption using results of short-term and very short-term methods are used. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented. (orig.)

  1. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    Science.gov (United States)

    Anggraeni, Novia Antika

    2015-04-01

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.

  2. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    International Nuclear Information System (INIS)

    Anggraeni, Novia Antika

    2015-01-01

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days

  3. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    Energy Technology Data Exchange (ETDEWEB)

    Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com [Geophysics Sub-department, Physics Department, Faculty of Mathematic and Natural Science, Universitas Gadjah Mada. BLS 21 Yogyakarta 55281 (Indonesia)

    2015-04-24

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.

  4. Verifying Operational and Developmental Air Force Weather Cloud Analysis and Forecast Products Using Lidar Data from Department of Energy Atmospheric Radiation Measurement (ARM) Sites

    Science.gov (United States)

    Hildebrand, E. P.

    2017-12-01

    Air Force Weather has developed various cloud analysis and forecast products designed to support global Department of Defense (DoD) missions. A World-Wide Merged Cloud Analysis (WWMCA) and short term Advected Cloud (ADVCLD) forecast is generated hourly using data from 16 geostationary and polar-orbiting satellites. Additionally, WWMCA and Numerical Weather Prediction (NWP) data are used in a statistical long-term (out to five days) cloud forecast model known as the Diagnostic Cloud Forecast (DCF). The WWMCA and ADVCLD are generated on the same polar stereographic 24 km grid for each hemisphere, whereas the DCF is generated on the same grid as its parent NWP model. When verifying the cloud forecast models, the goal is to understand not only the ability to detect cloud, but also the ability to assign it to the correct vertical layer. ADVCLD and DCF forecasts traditionally have been verified using WWMCA data as truth, but this might over-inflate the performance of those models because WWMCA also is a primary input dataset for those models. Because of this, in recent years, a WWMCA Reanalysis product has been developed, but this too is not a fully independent dataset. This year, work has been done to incorporate data from external, independent sources to verify not only the cloud forecast products, but the WWMCA data itself. One such dataset that has been useful for examining the 3-D performance of the cloud analysis and forecast models is Atmospheric Radiation Measurement (ARM) data from various sites around the globe. This presentation will focus on the use of the Department of Energy (DoE) ARM data to verify Air Force Weather cloud analysis and forecast products. Results will be presented to show relative strengths and weaknesses of the analyses and forecasts.

  5. Power plant site evaluation, electric energy demand forecasts - Douglas Point Site. Volume 3. Final report

    International Nuclear Information System (INIS)

    Wilson, J.W.

    1975-07-01

    This is part of a series of reports containing an evaluation of the proposed Douglas Point nuclear generating station site located on the Potomac River in Maryland 30 miles south of Washington, D.C. This report contains chapters on the Potomac Electric Power Company's market, forecasting future demand, modelling, a residential demand model, a nonresidential demand model, the Southern Maryland Electric Cooperative Model, short term predictive accuracy, and total system requirements

  6. Funding of energy research: BMFT expenditures for energy research and energy technologies, 3rd program, a review and a forecast

    International Nuclear Information System (INIS)

    Jacke, S.

    1990-01-01

    Between the early sixties and late 1989, the German Federal Government spent some DM 23 billion to support research and development of the entire field of nuclear technology (such as fundamental research, industrial applications, medicine, safety technology, advanced energy systems) in the Federal Republic of Germany. Of this amount, approx. DM 11 billion was spent on the technology of nuclear power plants equipped with light water reactors, on safety research, and on the nuclear fuel cycle. Comparing the expenditures of the Federal Government for the conversion of nuclear power into electricity with the savings achieved in electricity generating costs of approx. DM 58 billion by late 1989 (the cost advantage of nuclear power being approx. Pf 5/kWh), one arrives at a cost advantage to the whole economy of approx. DM 47 billion by the date shown above; by the year 2000, this advantage will have risen to some DM 150 billion. (orig.) [de

  7. Forecast of energy demand in Colombia by means of a system of inference diffuse neuronal

    International Nuclear Information System (INIS)

    Medina Hurtado, Santiago; Garcia Aguado, Josefina

    2005-01-01

    This work two artificial intelligence techniques are used lo forecast the monthly demand of electric power in Colombia, the objective is determinate the error of the prediction and they can be compared later with other traditional models of forecast time series, an important decrease in the prediction errors, would bring economic benefits for all the agents that operate in the electric market. The artificial neural networks - RNA and Adaptative Neural Fuzzy Inference Systems - ANFIS are actually broadly used in forecast problems in many fields of the science and the technology with good performance, for our case these models were fed with explanatory variables of the demand. We used a RNA totally interconnected with forward propagation and three hidden layer, two learned algorithms were proved for the net find significantly different results in the prediction error as we as in the time of training. The ANFIS model used was of type Takawi - Sugeno of order zero and it was fed with the main components of the defined entrance variables. The results were compared by means of the function of error Root of the Mean Square Error RMSE and the Percentage of Error Mean Absolute (MAPE) we find a better performance of the RNA

  8. Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-04-01

    Full Text Available For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm, is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product, industrial structure (the proportion of the second industry added value, energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    Highlights: • We develop a building energy forecasting model using support vector regression. • Model is applied to data from a multi-family residential building in New York City. • We extend sensor based energy forecasting to multi-family residential buildings. • We examine the impact temporal and spatial granularity has on model accuracy. • Optimal granularity occurs at the by floor in hourly temporal intervals. - Abstract: Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10 min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for

  10. Methodical Approaches To Analysis And Forecasting Of Development Fuel And Energy Complex And Gas Industry In The Region

    Directory of Open Access Journals (Sweden)

    Vladimir Andreyevich Tsybatov

    2014-12-01

    Full Text Available Fuel and energy complex (FEC is one of the main elements of the economy of any territory over which intertwine the interests of all economic entities. To ensure economic growth of the region should ensure that internal balance of energy resources, which should be developed with account of regional specifics of economic growth and energy security. The study examined the status of this equilibrium, indicating fuel and energy balance of the region (TEB. The aim of the research is the development of the fuel and energy balance, which will allow to determine exactly how many and what resources are not enough to ensure the regional development strategy and what resources need to be brought in. In the energy balances as the focus of displays all issues of regional development, so thermopile is necessary as a mechanism of analysis of current issues, economic development, and in the forward-looking version — as a tool future vision for the fuel and energy complex, energy threats and ways of overcoming them. The variety of relationships in the energy sector with other sectors and aspects of society lead to the fact that the development of the fuel and energy balance of the region have to go beyond the actual energy sector, involving the analysis of other sectors of economy, as well as systems such as banking, budgetary, legislative, tax. Due to the complexity of the discussed problems, the obvious is the need to develop appropriate forecast-analytical system, allowing regional authorities to implement evidence-based predictions of the consequences of management decisions. Multivariant scenario study on development of fuel and energy complex and separately industry, to use the methods of project-based management, harmonized application of state regulation of strategic and market mechanisms on the operational directions of development of fuel and energy complex and separately industry in the economy of the region.

  11. Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error

    International Nuclear Information System (INIS)

    Sanjari, M.J.; Karami, H.; Gooi, H.B.

    2016-01-01

    Highlights: • Combination of day-ahead and hour-ahead optimizations to design online controller. • Investigating the effect of load forecast error on the system operating cost. • Proposing effective method for hour-ahead resource re-dispatch. • Using the HSS algorithm as a powerful and effective optimization method. • Combining long-term and short-term strategies for optimal dispatch of resources. - Abstract: This paper deals with a residential hybrid thermal/electrical grid-connected home energy system incorporating real data for the load demand. A day-ahead scheduling (DAS) algorithm for dispatching different resources has been developed in previous studies to determine the optimal operation scheduling for the distributed energy resources at each time interval so that the operational cost of a smart house is minimized. However, demand of houses may be changed in each hour and cannot be exactly predicted one day ahead. System complexity caused by nonlinear dynamics of the fuel cell, as a combined heat and power device, and battery charging and discharging time make it difficult to find the optimal operating point of the system by using the optimization algorithms quickly in online applications. In this paper, the demand forecast error is studied and a near-optimal dispatch strategy by using artificial neural network (ANN) is proposed for the residential energy system when the demand changes are known one hour ahead with respect to the predicted day-ahead values. The day-ahead and hour-ahead optimizations are combined and ANN training inputs are adjusted according to the problem such that the economic dispatch of different energy resources can be achieved by the proposed method compared with previous studies. Using the model of the fuel cell extracted from experimental measurement and real data for the load demand makes the results more applicable in real residential energy systems.

  12. Towards a low-carbon future in China's building sector-A review of energy and climate models forecast

    International Nuclear Information System (INIS)

    Li Jun

    2008-01-01

    This article investigates the potentials of energy saving and greenhouse gases emission mitigation offered by implementation of building energy efficiency policies in China. An overview of existing literature regarding long-term energy-demand and carbon dioxide (CO 2 ) emission forecast scenarios is presented. Energy consumption in buildings could be reduced by 100-300 million tons of oil equivalent (mtoe) in 2030 compared with the business-as-usual (BAU) scenario, which means that 600-700 million metric tons of CO 2 emissions could be saved by implementing appropriate energy policies within an adapted institutional framework. The main energy-saving potentials in buildings can be achieved by improving a building's thermal performance and district heating system efficiency. The analyses also reveal that the energy interchange systems are effective especially in the early stage of penetration. Our analysis on the reviewed models suggests that more ambitious efficiency improvement policies in both supply- and demand-side as well as the carbon price should be taken into account in the policy scenarios to address drastic reduction of CO 2 emission in the building sector to ensure climate security over the next decades

  13. Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

    DEFF Research Database (Denmark)

    Lunde, Asger; Olesen, Kasper Vinther

    We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized...... variances over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit can be obtained in-sample by utilizing high-frequency data compared to standard models that use only daily observations. Additionally, we show that the intraday sampling...

  14. The 1999 MWV petroleum consumption forecast: Petroleum still in the lead in the German energy market

    International Nuclear Information System (INIS)

    Anon.

    1999-01-01

    Petroleum consumption in Germany has been stagnant over the last few years. Since in 1998 the decline in light heating oil sales was balanced out by an increase in fuel consumption and petroleum sales to the petrochemical industry, total domestic supplies of petroleum products amounted to 127.4 million tonnes and thus remained at last year's level. According to the latest forecast of the Mineraloelwirtschaftsverband e.V., extending to the year 2010, stagnation will continue for some years, but petroleum consumption is expected to then take a clear downward trend and fall off by 11 million tonnes (or 9%) until 2010. (orig./CB) [de

  15. On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

    DEFF Research Database (Denmark)

    Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.

    2017-01-01

    This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...

  16. Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2018-05-01

    Full Text Available With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually.

  17. A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

    International Nuclear Information System (INIS)

    Kıran, Mustafa Servet; Özceylan, Eren; Gündüz, Mesut; Paksoy, Turan

    2012-01-01

    Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

  18. Gas analysis modeling system forecast for the Energy Modeling Forum North American Natural Gas Market Study

    International Nuclear Information System (INIS)

    Mariner-Volpe, B.; Trapmann, W.

    1989-01-01

    The Gas Analysis Modeling System is a large computer-based model for analyzing the complex US natural gas industry, including production, transportation, and consumption activities. The model was developed and first used in 1982 after the passage of the NGPA, which initiated a phased decontrol of most natural gas prices at the wellhead. The categorization of gas under the NGPA and the contractual nature of the natural gas market, which existed at the time, were primary factors in the development of the basic structure of the model. As laws and regulations concerning the natural gas market have changed, the model has evolved accordingly. Recent increases in competition in the wellhead market have also led to changes in the model. GAMS produces forecasts of natural gas production, consumption, and prices annually through 2010. It is an engineering-economic model that incorporates several different mathematical structures in order to represent the interaction of the key groups involved in the natural gas market. GAMS has separate supply and demand components that are equilibrated for each year of the forecast by means of a detailed transaction network

  19. Forecast Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  20. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  1. Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

    Science.gov (United States)

    Li, Xiwang

    Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of

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

    Energy Technology Data Exchange (ETDEWEB)

    Reno Harnish

    2011-08-16

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

  3. Decomposition and forecasting analysis of China's energy efficiency: An application of three-dimensional decomposition and small-sample hybrid models

    International Nuclear Information System (INIS)

    Meng, Ming; Shang, Wei; Zhao, Xiaoli; Niu, Dongxiao; Li, Wei

    2015-01-01

    The coordinated actions of the central and the provincial governments are important in improving China's energy efficiency. This paper uses a three-dimensional decomposition model to measure the contribution of each province in improving the country's energy efficiency and a small-sample hybrid model to forecast this contribution. Empirical analysis draws the following conclusions which are useful for the central government to adjust its provincial energy-related policies. (a) There are two important areas for the Chinese government to improve its energy efficiency: adjusting the provincial economic structure and controlling the number of the small-scale private industrial enterprises; (b) Except for a few outliers, the energy efficiency growth rates of the northern provinces are higher than those of the southern provinces; provinces with high growth rates tend to converge geographically; (c) With regard to the energy sustainable development level, Beijing, Tianjin, Jiangxi, and Shaanxi are the best performers and Heilongjiang, Shanxi, Shanghai, and Guizhou are the worst performers; (d) By 2020, China's energy efficiency may reach 24.75 thousand yuan per ton of standard coal; as well as (e) Three development scenarios are designed to forecast China's energy consumption in 2012–2020. - Highlights: • Decomposition and forecasting models are used to analyze China's energy efficiency. • China should focus on the small industrial enterprises and local protectionism. • The energy sustainable development level of each province is evaluated. • Geographic distribution characteristics of energy efficiency changes are revealed. • Future energy efficiency and energy consumption are forecasted

  4. Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities

    DEFF Research Database (Denmark)

    Zugno, Marco; Jónsson, Tryggvi; Pinson, Pierre

    2013-01-01

    in liberalized electricity markets and to assess its performance. At first, the so-called optimal quantile strategy is revisited. It is proved that without market power, i.e. under the price-taker assumption, this strategy maximizes expected market revenues. Forecasts of wind power production, of day......-ahead and real-time market prices and of the system imbalance are inputs to this strategy. Subsequently, constraining of the bid that maximizes the expected revenues is proposed as a way to overcome the strategy's disregard of practical limitations and, at the same time, of risk. Two constraining techniques......Wind power is not easily predictable and non-dispatchable. Nevertheless, wind power producers are increasingly urged to participate in electricity market auctions in the same manner as conventional power producers. The aim of this paper is to propose an operational strategy for trading wind energy...

  5. Challenges, problems and possible solutions in wind generator systems from the aspect of forecast, planning and delivery of wind energy

    International Nuclear Information System (INIS)

    Giovski, Nikola

    2014-01-01

    The fundamental difficulties of integrating wind energy into the power system arise from its large temporal variability and limited predictability. That's why the integration of wind power presents major challenge for today's operating and planning practices of the power system operators. Accurate predictions of the possible wind power output, in time intervals relevant for creating schedules for production and exchange capacity, allows to system operators and dispatching personnel more efficient power system management. Despite the challenges and problems that arise due to integration of wind power into power systems, which need to be solved or reduced, wind power has its advantages that should be utilized. The effective integration of wind power plants into the transmission grid should allow them to represent the backbone of future energy systems. Modern wind generators represent production units that have the ability to participate in the management of energy systems e.g. in the regulation of frequency, voltage and other network operating requirements. This paper provides a brief overview of global experiences with the challenges, problems and possible solutions that appear in wind generator systems from the aspect of forecasting, planning and delivery of wind energy. (author)

  6. VARIMAX MODEL TO FORECAST THE EMISSION OF CARBON DIOXIDE FROM ENERGY CONSUMPTION IN RUBBER AND PETROLEUM INDUSTRIES SECTORS IN THAILAND

    Directory of Open Access Journals (Sweden)

    Pruethsan Sutthichaimethee

    2017-05-01

    Full Text Available This study aims to analyze the forecasting of CO2 emission from the energy consumption in the Rubber, Chemical and Petroleum Industries sectors in Thailand. The scope of research employed the input-output table of Thailand from the year 2000 to 2015. It was used to create the model of CO2 emission, population, GDP growth and predict ten years and thirty years in advance. The model used was the VARIMAX Model which was divided into two models. The results show that from the first model by using which predicted the duration of ten years (2016-2025 by using VARIMAX Model (2,1,2, On average, Thailand has 17.65% higher quantity of CO2 emission than the energy consumption sector (in 2025. The second model predicted the duration of 30 years (2016-2045 by using VARIMAX Model (2,1,3 shows that Thailand has average 39.68% higher quantity of CO2 emission than the energy consumption sector (in 2025. From the analyses, it shows that Thailand has continuously higher quantity of CO2 emission from the energy consumption. This negatively affects the environmental system and economical system of the country incessantly. This effect can lead to unsustainable development.

  7. Dynamic sizing of energy storage for hedging wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Pinson, Pierre; Papefthymiou, George; Klöckl, Bernd

    2009-01-01

    In market conditions where program responsible parties are penalized for deviations from proposed bids, energy storage can be used for compensating the energy imbalances induced by limited predictability of wind power. The energy storage capacity necessary for performing this task will differ bet...

  8. Solar combisystems with forecast control to increase the solar fraction and lower the auxiliary energy cost

    DEFF Research Database (Denmark)

    Perers, Bengt; Furbo, Simon; Fan, Jianhua

    2011-01-01

    Solar Combi systems still need quite a lot of auxiliary energy especially in small systems without seasonal storage possibilities. The control of the auxiliary energy input both in time and power is important to utilize as much as possible of the solar energy available from the collectors and also...... energy sources. It can be either direct electric heating elements or a heat pump upgrading ambient energy in the air, ground, solar collector or waste heat from the house. The paper describes system modeling and simulation results. Advanced laboratory experiments are also starting now with three...

  9. Energy-saving decomposition and power consumption forecast: The case of liaoning province in China

    Energy Technology Data Exchange (ETDEWEB)

    He, Y.X.; Zhao, Y.S.; Wang, Y.J. [School of Business Administration, North China Electric Power University, Zhu Xin Zhuang, Bei Nong Lu No. 2, Changping District, Beijing (China); Zhang, S.L. [Finance Department, Nanning Power Supply Bureau, Xingguang Street No. 43, Nanning, Guangxi Autonomous Region (China); Li, F.R. [University of Bath, Bath, BA2 7AY (United Kingdom)

    2011-01-15

    To achieve sustainable development of the society, the People's Republic of China (PRC) proposed in its 11th Five-Year Plan for National Economic and Social Development Program a shift in energy-saving target of decreasing energy intensity by 20% in 2010 compared with that of 2005. Liaoning province is one of the oldest industrial bases in China. Policymakers are often confronted with problems relating to adjustment in the development pattern as a means to secure steady economic growth. The power industry is a fundamental energy industry; it plays an important role in realizing of energy-saving targets. Based on the input-output model, this paper sets extensive, planning and 20% energy-saving scenarios in order to analyze energy-saving and power consumption situations for Liaoning by 2010. Through extensive simulations, the levels of energy-saving and power demand under different scenarios are obtained. Results from the analysis show that under the premises of adjusting the ratio of investment and consumption, optimizing products structure, and improving energy use efficiency, it is possible to achieve the proposed energy-saving target. Liaoning's power consumption can maintain a stable growth trend in the future. The percentage of electricity to the total energy use can also increase to 16% in 2010. (author)

  10. Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

    Directory of Open Access Journals (Sweden)

    Bharatiraja Chokkalingam

    2017-03-01

    Full Text Available The enormous growth in the penetration of electric vehicles (EVs, has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate user’s “range anxiety”. The existing charging stations fail to adopt power provision, allocation and scheduling management. To improve the existing charging infrastructure, data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on the road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

  11. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  12. Forecasting of the incorporated energy in the final demand of the brazilian economy in 2005

    International Nuclear Information System (INIS)

    Cunha, Marcelo Pereira da; Pereira, Jose Tomaz Vieira

    2008-01-01

    This work presents the application of a methodology for evaluation of the primary energy incorporated by the productive sectors of a economy at the final demand - using of a income-product mode. A methodology is applied in the evaluation of the energy incorporated to 25 sectors of the brazilian economy, by using the the data available in the national counts (IBGE - 2007), and the National Energy Balance for the year 2005 (EPE - 2007). For each sector, the results are presented in terms of the primary energy incorporated (in petroleum equivalent tons per R$ 1,000), of the participation of renewable energy, and the total primary energy distribution for the offered products by the 25 sectors to be consumption by the final demand. Among some interesting results in terms of final demand, it is highlighted the presence of 96.5% of renewable primary energy for the sector of alcohol, and 5.3% for the sector of petroleum refining products sector. In terms of the total energy distribution,the petroleum refining and coke sector were the most significant contribution to the incorporation of primary energy, presenting 16.1% of the total ahead of foods and beverages which presents 12.1%. Related to the final demand components, families consumption was responsible by the 57.7% of the total, the exports with 25.3%, the gross capital formation (investments and stock variations) with 11.3%, and the govern consumption wit 5.7%

  13. Optimal modeling and forecasting of the energy consumption and production in China

    International Nuclear Information System (INIS)

    Xiong, Ping-ping; Dang, Yao-guo; Yao, Tian-xiang; Wang, Zheng-xin

    2014-01-01

    Energy is of fundamental importance to a nation's economy. Accurate prediction of the energy consumption and production in China can play a guiding role in making the energy consumption plan, and facilitate timely and effective decision making of energy policy. This article proposes a novel GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority. The optimized model and five other GM (1,1) models are applied in the modeling of China's energy consumption and production. Both the simulation and prediction accuracy of the models are compared and analyzed. We obtain the result that the optimized model has higher prediction accuracy than the other five models. Therefore, the presented optimized model is further utilized to predict China's energy consumption and production from 2013 to 2017. The result indicates that China's energy consumption and production will keep increasing and the gap between the energy production and consumption will also be increasing. Finally, we predict Iran's and Argentina's energy consumption to further prove the effectiveness of the proposed model. - Highlights: • We proposed a novel GM (1,1) model based on optimizing initial condition. • The prediction accuracy of the proposed model is better than the other models. • We used the proposed model to predict China's energy consumption and production. • The proposed model can be used to predict other countries' energy consumption

  14. 太阳能预报方法及其应用和问题%A Review on Methods of Solar Energy Forecasting and Its Application

    Institute of Scientific and Technical Information of China (English)

    马金玉; 罗勇; 申彦波; 李世奎

    2011-01-01

    太阳能预报包括预测太阳辐射量和光伏发电功率,对光伏发电系统并网运行有重要意义,是当前太阳能开发利用的一个关键问题.本文对国内外太阳能预报方法进行了扼要的评述,归纳了太阳能预报的机理及其方法在光伏发电中的应用.太阳辐射的预报方法主要有传统统计、神经网络、卫星遥感和数值模拟等方法.文中基于光伏发电应用的需求,分析了不同预报方法的优点和不足,并探讨了若干有待进一步改善的问题,展望了国内太阳能预报技术方法的发展和应用前景.%Solar forecasting, consisting of solar radiation forecasting and photovoltaic solar power forecasting, is important for photovoltaic power generation systems in network operation. In recent years, with the development of the solar industry, the demand for solar energy forecasting is increasing. Solar energy prediction methods have been developed in developed country. Our solar photovoltaic technology research is, however, at a primary stage, with only a few universities and institutes conducting simulation-based research, little of which accounts for meteorological factors.According to predicted solar physical factors, the prediction can be generally divided into two categories. One is to predict solar radiation which requires the calculation of photovoltaic power according to the output photoelectric conversion efficiency. The other is direct prediction of output power of PV systems. As the domestic forecast on solar energy technologies and applications are rarely reported, mechanisms of solar forecasting, methods and applications in photovoltaic power generation were reviewed based on the demand for photovoltaic applications. This review would provide an important basis for domestic solar photovoltaic power generation development. This paper focuses on the situations of solar energy prediction at home and abroad, and summarizes the principles of solar energy

  15. A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

    Science.gov (United States)

    Courdent, Vianney; Grum, Morten; Munk-Nielsen, Thomas; Mikkelsen, Peter S.

    2017-05-01

    Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken

  16. Impact of Renewable Energy Forecast Imperfections on Market-Clearing Outcomes

    DEFF Research Database (Denmark)

    Ordoudis, Christos; Pinson, Pierre

    2016-01-01

    The increased integration of renewable energy sources, in particular wind and solar power, calls for changes in power system operation. Current market designs that are only efficient to accommodate limited uncertainty are highly challenged by the partly predictable renewable energy generation...

  17. Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

    Energy Technology Data Exchange (ETDEWEB)

    Garikapati, Venu M. [Systems Analysis and Integration Section, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401; You, Daehyun [Maricopa Association of Governments, 302 North First Avenue, Suite 300, Phoenix, AZ 85003; Zhang, Wenwen [School of City and Regional Planning, Center for Geographic Information Systems, Georgia Institute of Technology, 760 Spring Street, Suite 230, Atlanta, GA 30308; Pendyala, Ram M. [School of Sustainable Engineering and the Built Environment, Arizona State University, 660 South College Avenue, Tempe, AZ 85281; Guhathakurta, Subhrajit [School of City and Regional Planning, Center for Geographic Information Systems, Georgia Institute of Technology, 760 Spring Street, Suite 230, Atlanta, GA 30308; Brown, Marilyn A. [School of Public Policy, 685 Cherry Street, Georgia Institute of Technology, Atlanta, GA 30332; Dilkina, Bistra [School of Computational Science and Engineering, 266 Ferst Drive, Georgia Institute of Technology, Atlanta, GA 30332

    2017-01-01

    This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

  18. China's economic reform and industry sector energy requirement: A forecast to 2015

    International Nuclear Information System (INIS)

    Gu, A.Y.

    1997-01-01

    With its GDP growing at an average rate of 9.8% for the last seventeen years, China has the world's fastest growing economy. This rapid pace of growth and industrialization has caused economic strain because fuel production cannot keep pace with demand, If China allows this situation to continue, significant oil imports will be necessary. In 1993, the industrial sector contributed 56% to China's GDP and consumed 61% of the total final energy. The industrial sector will remain the largest energy consumer in China well into the next century. According to China's Ninth Five-Year Plan (1996--2000), China will strengthen its ability to develop new products and will use technological advancement to promote industrial development. The Plan calls for special attention in four major areas: microelectronics technology, digital technology, software technology, and network technology. Given China's emphasis on developing light industries and on improving industrial sector energy efficiency, it is important to study the future energy demand of the industrial sector. Two scenarios for future energy requirements are studied through the year 2015: a Business As Usual (BASU) scenario and an Energy Efficient (EE) scenario. The study evaluates China's current economic reform policies and energy efficiency policies. The results of this evaluation are used to assign appropriate growth rates to industrial GDP and the industrial energy intensity for both scenarios. Results from the two scenarios are compared and analyzed

  19. Application and verification of the NMMB/BSC-CTM forecast for solar energy

    Science.gov (United States)

    Soret, Albert; Serradell, Kim; Piot, Matthias; Ortega, Daniel; Obiso, Vincenzo; Jorba, Oriol

    2016-04-01

    In the beginning of April 2014, northern Europe was affected by a mineral dust intrusion. On 4 April 2014, the power prediction for German solar installations was estimated as 21 GW, whereas the measured power production merely reached 11 GW. This strong overestimation significantly affected the hourly price in the wholesale electricity market: prices were firstly assessed at around 27 € /MWh but rapidly reached a level close to 150 € /MWh after recognizing the lack of solar output. It has been found that a large proportion of the uncertainty of existing NWP models can be attributed to the lack of accurate aerosol data used in order to model solar radiation. Despite the advancements in the modelling of aerosol-cloud interactions, current meteorological models use parameterizations made mostly for climate considerations (generally monthly-based). In this contribution, we analyse model results of the direct radiative effect of mineral dust over Germany at the beginning of April 2014. For that, the NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM) is applied on a regional domain at 0.1° horizontal resolution. The NMMB/BSC-CTM is a new on-line chemical weather prediction system coupling atmospheric and chemistry processes. In the radiation module of the model, mineral dust is treated as a radiatively active substance interacting both short and longwave radiation. The impact of the mineral dust outbreaks on meteorology is discussed by comparing model forecasts meteorological observations. The analysis focuses on the performance of the NMMB/BSC-CTM to simulate the radiative effects of a mineral dust intrusion far from source regions. Model results would help to illustrate the added value of on-line models for long term analysis of solar resource. On-going developments: integration of anthropogenic sources and implementation of indirect radiative effects will be also presented.

  20. Trends of energy efficiency in Finnish road freight transport 1995-2009 and forecast to 2016

    International Nuclear Information System (INIS)

    Liimatainen, Heikki; Poellaenen, Markus

    2010-01-01

    A framework for modeling and analyzing the energy efficiency of road freight transport is presented in this paper. This framework is tested by using the data from the Finnish Goods Transport by Road statistics. The data was enhanced by calculating the fuel consumption for each trip in the data. To calculate this, weight-fuel consumption functions were estimated for each Euro-class vehicles and road type. This is a new method for analyzing the energy efficiency of road freight transport and it could be applied also in other countries gathering freight transport data with continuous company surveys. The analysis show that the energy efficiency of road freight transport in Finland improved during 1995-2002, but has declined since. The major drivers in the development have been the changes in the level of empty running and vehicle fuel efficiency. Extrapolating current statistical trends of factors that influence the energy efficiency show that the target set by the Finnish government for improving energy efficiency by 9% until 2016 will not be achieved. However, the target is possible to be achieved by a combination of small changes to some determinants. - Research highlights: →A new method for analyzing energy efficiency by adding fuel data to national freight statistics. →Energy efficiency improved in Finland from 1995 to 2002 but has declined since. →Energy efficiency in Finland is still on a good level internationally. →Target of the Finnish energy efficiency agreement was quantified for the first time in this study. →The target will not be achieved if the past trends continue.

  1. Forecasting for the second half-century of nuclear energy using. Projection and strategies

    International Nuclear Information System (INIS)

    1996-03-01

    This report presents a professional, global and unrestricted overview of the nuclear energy development and using, and the worldwide related research. The document also presents the projection and strategies for the second half-century

  2. International wind energy development. World market update 2006. Forecast 2007-2011

    International Nuclear Information System (INIS)

    2007-03-01

    The report covers development in the international wind power market during 2006 and the new Forecast until 2011. Furthermore a long term Prediction is made up to 2016. With 15,016 MW of new installations, the total installed capacity of wind power grew to around 74,300 MW. This was an increase in cumulative installation of 25%. Looking at the annual installation of 15,016 MW there was an increase of 30%. This is on top of a 2005 growth of 42%. The key figures for development during 2006 were: a) 15,016 MW of newly installed wind power capacity. b)Cumulative installed capacity by the end of 2006 reached 74,306 MW, consisting of around 10,600 wind turbines dispersed in 36 countries. c) Europe maintained its role as the largest wind power continent. 51% of all new installation in 2006 took place in Europe. d) The Americas had a record year thanks to the development in the US, where 2,454 MW of new capacity was added. The reason is the PTC (Production Tax Credit) in the US market in force again and will be so until end of 2008. The Americas accounted for 23.4% of the world's installation in 2006. e) Asia showed significant growth. Including OECD Pacific, Asia doubled its installation, from 7,890 MW in 2005 to 11.601 MW by the end of 2006. India was by far the leading country, with 1,840 MW of new capacity in 2006. China also showed strong progress, with almost 1,334 MW of new installation. The region as a whole accounted for 24.7% of the year's world wide total. f) Among the Top Ten markets USA maintained its position as largest market in 2006. Germany, the world's largest market for a decade, increased its installation from 2005 to installing 2.233 MW, after three year on decline. It is, however, enough to maintain their position as no. 2 market in the world. France and Portugal showed remarkable growth. Spain is still No.2 market in Europe, with 1,587 MW of new installation. g) Penetration of wind power in the world's electricity supply reached 0.82% by the end of

  3. Trends of energy efficiency in Finnish road freight transport 1995-2009 and forecast to 2016

    Energy Technology Data Exchange (ETDEWEB)

    Liimatainen, Heikki; Poellaenen, Markus [Department of Business Information Management and Logistics, Tampere University of Technology, P.O. Box 541, 33101 Tampere (Finland)

    2010-12-15

    A framework for modeling and analyzing the energy efficiency of road freight transport is presented in this paper. This framework is tested by using the data from the Finnish Goods Transport by Road statistics. The data was enhanced by calculating the fuel consumption for each trip in the data. To calculate this, weight-fuel consumption functions were estimated for each Euro-class vehicles and road type. This is a new method for analyzing the energy efficiency of road freight transport and it could be applied also in other countries gathering freight transport data with continuous company surveys. The analysis show that the energy efficiency of road freight transport in Finland improved during 1995-2002, but has declined since. The major drivers in the development have been the changes in the level of empty running and vehicle fuel efficiency. Extrapolating current statistical trends of factors that influence the energy efficiency show that the target set by the Finnish government for improving energy efficiency by 9% until 2016 will not be achieved. However, the target is possible to be achieved by a combination of small changes to some determinants. (author)

  4. Forecast of power generation and heat production from renewable energy sources

    Directory of Open Access Journals (Sweden)

    Pydych Tadeusz

    2017-01-01

    Full Text Available The share of renewable energy sources (RES in the end use of energy in the UE will increase from the present level of about 25% to 50 % in 2030 according to the assumptions of the European Commission. In Poland the RES Act was passed in 2015. The act defines mechanisms and instruments for supporting the production of electricity and heat from renewable energy sources. Statistics (2003–2014 of electricity generation and heat production from RES in Poland were used in the research. Because of amendments to regulations connected with promoting RES and the emissions trading system (ETS as well as the uncertainty associated with further directions of the energy and environmental policy, generation of electricity and heat based on the use of RES must be modelled while taking risk into account. A number of dynamic processes incorporating random events may be modelled by stochastic equations using Ito calculus. By applying Euler’s method to solve stochastic differential equations (SDE, it is possible to simulate the development of the use of renewable energy carriers in electricity generation and heat production in the future.

  5. From potential forecast to foresight of Turkey's renewable energy with Delphi approach

    Energy Technology Data Exchange (ETDEWEB)

    Celiktas, Melih Soner [EBILTEM, Ege University Science and Technology Research Center, Izmir (Turkey); Kocar, Gunnur [Solar Energy Institute of Ege University, Izmir (Turkey)

    2010-05-15

    A Delphi Survey is a series of questionnaires that allow experts or people with specific knowledge to develop ideas about potential future developments around an issue. The Delphi questionnaires were developed throughout the foresight process in relation to the responses given by participants in bibliometric and SWOT analysis conducted prior to the Delphi survey. In this paper, Turkey's renewable energy future is evaluated using the Delphi method. A two-round Delphi research study was undertaken to determine and measure the expectations of the sector representatives regarding the foresight of renewable energies. First and second round of Delphi study were carried out by using online surveys. About 382 participants responded in the first round of the Delphi questionnaire yielding a respond rate of 20.1%, whereas 325 participants responded at the second round yielding a respond rate of 84.9%. About 50% of Turkey's energy demand was foresighted to be met by renewable energies around 2030. The results showed that all types of renewable energies would not only provide economic and environmental benefits but also improve living standards. (author)

  6. Advanced Cloud Forecasting for Solar Energy's Impact on Grid Modernization

    International Nuclear Information System (INIS)

    Werth, D.; Nichols, R.

    2017-01-01

    Solar energy production is subject to variability in the solar resource - clouds and aerosols will reduce the available solar irradiance and inhibit power production. The fact that solar irradiance can vary by large amounts at small timescales and in an unpredictable way means that power utilities are reluctant to assign to their solar plants a large portion of future energy demand - the needed power might be unavailable, forcing the utility to make costly adjustments to its daily portfolio. The availability and predictability of solar radiation therefore represent important research topics for increasing the power produced by renewable sources.

  7. UD-WCMA: An Energy Estimation and Forecast Scheme for Solar Powered Wireless Sensor Networks

    KAUST Repository

    Dehwah, Ahmad H.; Elmetennani, Shahrazed; Claudel, Christian

    2017-01-01

    -WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location

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

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Jørgensen, John Bagterp

    2013-01-01

    decentralized energy storage in the turbines’ inertia combined with a central storage unit or deferrable consumers can be utilized to achieve this goal at a minimum cost. We propose a variation on model predictive control to incorporate predictions of wind speed. Due to the aerodynamics of the turbines...

  9. Nonlinear methods for load and energy forecasting in local energy systems, especially wind power and photovoltaic systems. Final report

    International Nuclear Information System (INIS)

    Kantz, H.; Ragwitz, M.

    2002-05-01

    The investigations were supported by the Center for Nonlinear Dynamics in Austin, Texas, the Department of Energy and Semiconductor Engineering of Oldenburg University, and Boreas Energietechnik GmbH in Dresden, Germany [de

  10. FORECASTING ENERGY CONSUMPTION IN SHORT-TERM AND LONG-TERM PERIOD BY USING ARIMAX MODEL IN THE CONSTRUCTION AND MATERIALS SECTOR IN THAILAND

    Directory of Open Access Journals (Sweden)

    Pruethsan Sutthichaimethee

    2017-07-01

    Full Text Available This study aims to analyze the forecasting of energy consumption in the Construction and Materials sectors. The scope of the study covers the forecasting periods of energy consumption for the next 10 years, 2017-2026, 20 years, 2017-2036, and 30 years, 2017-2046, by using ARIMAX Model. The prediction results show that these models are effective in the forecast measured by RMSE, MAE, and MAPE. The results show that from the first model (2,1,1, which predicted the duration of 10 years, 2017-2026, indicates that Thailand has increased an energy consumption rate with the average of 18.09%, while the second model (2,1,2 with the prediction of 20 years, 2017-2036, Thailand arises its energy consumption up to 37.32%. In addition, the third model (2,1,3 predicted the duration of 30 years from 2017 to 2046, and it has found that Thailand increases its energy consumption up to 49.72%.

  11. The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning

    International Nuclear Information System (INIS)

    Perwez, Usama; Sohail, Ahmed; Hassan, Syed Fahad; Zia, Usman

    2015-01-01

    The long-term forecasting of electricity demand and supply has assumed significant importance in fundamental research to provide sustainable solutions to the electricity issues. In this article, we provide an overview of structure of electric power sector of Pakistan and a summary of historical electricity demand & supply data, current status of divergent set of energy policies as a framework for development and application of a LEAP (Long-range Energy Alternate Planning) model of Pakistan's electric power sector. Pakistan's LEAP model is used to analyze the supply policy selections and demand assumptions for future power generation system on the basis of economics, technicality and implicit environmental implications. Three scenarios are enacted over the study period (2011–2030) which include BAU (Business-As-Usual), NC (New Coal) & GF (Green Future). The results of these scenarios are compared in terms of projected electricity demand & supply, net present cost analysis (discount rate at 4%, 7% and 10%) and GHG (greenhouse gas) emission reductions, along with sensitivity analysis to study the effect of varying parameters on total cost. A concluding section illustrates the policy implications of model for futuristic power generation and environmental policies in Pakistan. - Highlights: • Pakistan-specific electricity demand model is presented. • None of the scenarios exceeded the price of 12 US Cents/kWh. • By 2030, fuel cost is the most dominant factor to influence electricity per unit cost. • By 2030, CO_2 emissions per unit electricity will increase significantly in coal scenario relative to others. • By 2030, the penetration of renewable energy and conservation policies can save 70.6 tWh electricity.

  12. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    OpenAIRE

    Zhaoxuan Li; SM Mahbobur Rahman; Rolando Vega; Bing Dong

    2016-01-01

    We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statisti...

  13. Temperature, Humidity and Energy Consumption Forecasting in the Poultry Hall Using Artificial Neural Networknetwork

    Directory of Open Access Journals (Sweden)

    N Gholamrezaei

    2017-10-01

    Full Text Available Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent methods are applied in a lot of fields such as classification, pattern recognition, prediction and modeling of processes. To detect and classify several agricultural crops, a research was conducted based on texture and color feature. The highest classification accuracy for vegetables, grains and fruits with using artificial neural network were 80%, 86% and 70%. In this research, the ability to Multilayer Perceptron (MLP Neural Network in predicting energy consumption, temperature and humidity in different coordinate placement of electronic control unit sensors in the poultry house environment was examined. Materials and Methods The experiments were conducted in a poultry unit (3000 pieces that is located in Fars province, Marvdasht city, Ramjerd town, with dimensions of 32 meters long, 7 meters wide and 2.2 meters height. To determine the appropriate placement of the sensor, 60 different points in terms of length, width and height in poultry were selected. Initially, the data was divided into two datasets. 80 percent of total data as a training set and 20 percent of total data as a test set. From180 observations, 144 data were used to train network and 36 data were used to test the process. There are several criteria for evaluating predictive models that they are mainly based according to the difference between the predicted outputs and actual outputs. To evaluate the performance of the model, two statistical indexes, mean squared error (MSE and the coefficient of determination (R² were used. Results and Discussions In this study, to train artificial neural network for predicting the temperature, humidity and energy consumption, the

  14. Long-term forecasts of regional, customer and use-specific energy demand

    International Nuclear Information System (INIS)

    Schwarz, Juerg

    1999-11-01

    In the future the Swiss electricity market will have to contend with changes stemming from market liberalization. The need for instruments to analyze and predict market shares of electricity is greater than ever; tools are also greatly needed to help managers and workers prepare for new beginnings and to reorient customers. The development and application of such an instrument are the object of the present thesis. A computer program produced within the context of this work can, based on an adapted bottom-up model, be used to analyze and predict the energy demand in the supply area of a medium-sized electric utility. Elektra Birseck Muenchenstein was included in the investigation as a representative medium-sized electric utility, and it provided the basis for a supply area. Current energy demand was depicted with a bottom-up approach and different scenarios of future development were calculated using a prognosis horizon of 30 years. For the market segmentation all consumer sectors had to be considered in detail. In addition, 'regionality', 'substitution' and 'customer proximity' factors had to be illustrated in the model, i.e. the regional development in the supply area, the substitution of energy sources -above all natural gas -and the detailed view of large, individual customers. The choice of a bottom-up approach created a demand for a large quantity of data, not all of which were available or could be produced. An additional crucial capability of the computer simulation was the comparison of assumptions and results of the prognoses. The users needed to be able to consider multiple future eventualities if they were to play out different scenarios to the end. Fulfilling these partly divergent criteria in the structural definition of the energy demand model was one of the large challenges of this work. The result of the dissertation is a differentiated prognosis instrument for the supply area of an electric utility. The structure of the suggested solution is

  15. Analysis of the energy and environmental effects of green car deployment by an integrating energy system model with a forecasting model

    International Nuclear Information System (INIS)

    Lee, Duk Hee; Park, Sang Yong; Hong, Jong Chul; Choi, Sang Jin; Kim, Jong Wook

    2013-01-01

    Highlights: ► A new methodology for improving energy system analysis models was proposed. ► The MARKAL model was integrated with the diffusion model. ► The new methodology was applied to green car technology. ► The ripple effect of green car technology on the energy system can be analyzed. -- Abstract: By 2020, Korea has set itself the challenging target of reducing nationwide greenhouse gas emissions by 30%, more than the BAU (Business as Usual) scenario, as the implementation goal required to achieve the new national development paradigm of green growth. To achieve such a target, it is necessary to diffuse innovative technologies with the capacity to drastically reduce greenhouse gas emissions. To that end, the ripple effect of diffusing innovative technologies on the energy and environment must be quantitatively analyzed using an energy system analysis model such as the MARKAL (Market Allocation) model. However, energy system analysis models based on an optimization methodology have certain limitations in that a technology with superior cost competitiveness dominates the whole market and non-cost factors cannot be considered. Therefore, this study proposes a new methodology for overcoming problems associated with the use of MARKAL models, by interfacing with a forecasting model based on the discrete-choice model. The new methodology was applied to green car technology to verify its usefulness and to study the ripple effects of green car technology on greenhouse gas reduction. The results of this study can be used as a reference when establishing a strategy for effectively reducing greenhouse gas emissions in the transportation sector, and could be of assistance to future studies using the energy system analysis model.

  16. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  17. Fisher matrix forecast on cosmological parameters from the dark energy survey 2-point angular correlation function

    Energy Technology Data Exchange (ETDEWEB)

    Sobreira, F.; Rosenfeld, R. [Universidade Estadual Paulista Julio de Mesquita Filho (IFT/UNESP), Sao Paulo, SP (Brazil). Inst. Fisica Teorica; Simoni, F. de; Costa, L.A.N. da; Gaia, M.A.G.; Ramos, B.; Ogando, R.; Makler, M. [Laboratorio Interinstitucional de e-Astronomia (LIneA), Rio de Janeiro, RJ (Brazil)

    2011-07-01

    Full text: We study the cosmological constraints expected for the upcoming project Dark Energy Survey (DES) with the full functional form of the 2-point angular correlation function. The angular correlation function model applied in this work includes the effects of linear redshift-space distortion, photometric redshift errors (assumed to be Gaussian) and non-linearities prevenient from gravitational infall. The Fisher information matrix is constructed with the full covariance matrix, which takes the correlation between nearby redshift shells in a proper manner. The survey was sliced into 20 redshift shells in the range 0:4 {<=} z {<=} 1:40 with a variable angular scale in order to search only the scale around the signal from the baryon acoustic oscillation, therefore well within the validity of the non-linear model employed. We found that under those assumptions and with a flat {Lambda}CDM WMAP7 fiducial model, the DES will be able to constrain the dark energy equation of state parameter w with a precision of {approx} 20% and the cold dark matter with {approx} 11% when marginalizing over the other 25 parameters (bias is treated as a free parameter for each shell). When applying WMAP7 priors on {Omega}{sub baryon}, {Omega} c{sub dm}, n{sub s}, and HST priors on the Hubble parameter, w is constrained with {approx} 9% precision. This shows that the full shape of the angular correlation function with DES data will be a powerful probe to constrain cosmological parameters. (author)

  18. Short term wave forecasting, using digital filters, for improved control of Wave Energy Converters

    DEFF Research Database (Denmark)

    Tedd, James; Frigaard, Peter

    2007-01-01

    This paper presents a Digital Filter method for real time prediction of waves incident upon a Wave Energy device. The method transforms waves measured at a point ahead of the device, to expected waves incident on the device. The relationship between these incident waves and power capture is derived...... experimentally. Results are shown form measurements taken on the Wave Dragon prototype device, a floating overtopping device situated in Northern Denmark. In this case the method is able to accurately predict the surface elevation at the device 11.2 seconds before the measurement is made. This is sufficient...... to allow advanced control systems to be developed using this knowledge to significantly improve power capture....

  19. Short term wave forecasting, using digital filters, for improved control of Wave Energy Converters

    Energy Technology Data Exchange (ETDEWEB)

    Tedd, J.; Frigaard, P. [Department of Civil Engineering, Aalborg University, Aalborg (Denmark)

    2007-07-01

    This paper presents a Digital Filter method for real time prediction of waves incident upon a Wave Energy device. The method transforms waves measured at a point ahead of the device, to expected waves incident on the device. The relationship between these incident waves and power capture is derived experimentally. Results are shown form measurements taken on the Wave Dragon prototype device, a floating overtopping device situated in Northern Denmark. In this case the method is able to accurately predict the surface elevation at the device 11.2 seconds before the measurement is made. This is sufficient to allow advanced control systems to be developed using this knowledge to significantly improve power capture.

  20. Energy optimization through probabilistic annual forecast water release technique for major storage hydroelectric reservoir

    International Nuclear Information System (INIS)

    Abdul Bahari Othman; Mohd Zamri Yusoff

    2006-01-01

    One of the important decisions to be made by the management of hydroelectric power plant associated with major storage reservoir is to determine the best turbine water release decision for the next financial year. The water release decision enables firm energy generated estimation for the coming financial year to be done. This task is usually a simple and straightforward task provided that the amount of turbine water release is known. The more challenging task is to determine the best water release decision that is able to resolve the two conflicting operational objectives which are minimizing the drop of turbine gross head and maximizing upper reserve margin of the reservoir. Most techniques from literature emphasize on utilizing the statistical simulations approach. Markovians models, for example, are a class of statistical model that utilizes the past and the present system states as a basis for predicting the future [1]. This paper illustrates that rigorous solution criterion can be mathematically proven to resolve those two conflicting operational objectives. Thus, best water release decision that maximizes potential energy for the prevailing natural inflow is met. It is shown that the annual water release decision shall be made in such a manner that annual return inflow that has return frequency smaller than critical return frequency (f c ) should not be considered. This criterion enables target turbine gross head to be set to the well-defined elevation. In the other words, upper storage margin of the reservoir shall be made available to capture magnitude of future inflow that has return frequency greater than or equal to f c. A case study is shown to demonstrate practical application of the derived mathematical formulas

  1. Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

    Directory of Open Access Journals (Sweden)

    Adeshina Y. Alani

    2017-10-01

    Full Text Available Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

  2. Forecast of the installed capacity for renewable energy installations and its influence on the grid extensions in the State of Brandenburg

    Energy Technology Data Exchange (ETDEWEB)

    Schwarz, Harald; Pfeiffer, Klaus [Brandenburgische Technische Univ. Cottbus (Germany); Zeidler, Jens [MITNETZ Strom, Halle/Saale (Germany); Schulz, Stephan [50Hertz Transmission, Berlin (Germany); Dorendorf, Stefan [E.ON edis, Fuerstenwalde (Germany)

    2012-07-01

    Development of installations for renewable electrical generation forced by the Federal Government of Germany causes an increased expansion of wind, photovoltaic and biomass installations, especially in the plain states, such as Brandenburg in Germany. Therefore, a study on grid integration of renewable energy in the state of Brandenburg was commissioned on behalf of the Brandenburg Ministry of Economics and European Affairs. The work tasks of the study, on the one hand, consisted of comprehensive forecast for the renewable energy sources such as wind, biomass and photovoltaic. On the other hand, grid calculations for the evaluation of plausibility of the existing grid extension concepts of network operators in Brandenburg were conducted based on this forecast. The results of this study are to be presented to general public in this contribution. (orig.)

  3. Long-term forecast 2010; Laangsiktsprognos 2010

    Energy Technology Data Exchange (ETDEWEB)

    2011-07-01

    This report presents the energy forecast to the year 2030, and two different sensitivity scenarios. The forecast is based on existing instruments, which means that the report's findings should not be considered a proper forecast of the future energy use, but as an impact assessment of existing policy instruments, given different circumstances such as economic growth and fuel prices

  4. International wind energy development. World market update 2001. Forecast 2002-2006

    International Nuclear Information System (INIS)

    2002-03-01

    In the year 2001, the wind power development took another major step forward. Installed capacity set a new record with the addition of 6,824 MW of new generating capacity. This is 2,329 MW more than the record set in year 2000. Growth in new capacity is up from 15% in year 2000 to 52% in 2001. This confirmed that the trend of wind energy being the preferred technology over other technologies for new generating capacity is continuing. Europe is the leading region for wind power. Of the new capacity added in 2001, 4,527 MW was installed in Europe. Germany once more shows this country's potential as the single most active market with the installation of 2,627 MW in new capacity, nearly 1,000 MW more than in the previous year. Germany consolidated its position as the world's leading developer of wind energy. The market in the US is once again the second largest wind energy market ahead of Spain. The expiration of the PTC at the end of the year 2001 gave some rush in the installation at the end of the year. The Danish market is on a deroute and lost pace completely in a changed political climate which means that the development of windpower is slowing down. Denmark has a very high penetration of windpower. On the supplier side Vestas Wind Systems A/S maintained its position as being the world's largest manufacturer of wind turbines. Vestas Wind Systems is now followed by Enercon GmbH. Without counting the US market figures Vestas Wind Systems A/S and Enercon has become the very closed in terms of sold MW. In the 3rd place is the Danish company NEG Micon A/S. Newcomers in the Top Ten list are Mitsubishi (JP) and REpower (GE). The most significant technological trend in the market is the continuing upscaling of machines. From year 2001 the average size of WTGs is 915 kW (in 2000: 800kW). In the very near future there will be erected wind turbines mainly dedicated for the upcoming offshore market of 4.5-5.0 MW. On Offshore there is only 10 MW installed during the year 2001

  5. Short-term Forecast of Automatic Frequency Restoration Reserve from a Renewable Energy Based Virtual Power Plant

    OpenAIRE

    Camal , Simon; Michiorri , Andrea; Kariniotakis , Georges; Liebelt , Andreas

    2017-01-01

    International audience; This paper presents the initial findings on a new forecast approach for ancillary services delivered by aggregated renewable power plants. The increasing penetration of distributed variable generators challenges grid reliability. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently impedes their integration into reserve mechanisms. A methodology is developed to forecast the flexibility that a win...

  6. Energy forecast 20090. Trends in the energy markets through 2030; Energieprognose 2009. Die Entwicklung der Energiemaerkte bis 2030

    Energy Technology Data Exchange (ETDEWEB)

    Fahl, Ulrich; Blesl, Markus; Voss, Alfred [Stuttgart Univ. (DE). Inst. fuer Energiewirtschaft und Rationelle Energieanwendung (IER); Frondel, Manuel [Rheinisch-Westfaelisches Institut fuer Wirtschaftsforschung (RWI), Essen (Germany); Loeschel, Andreas; Mennel, Tim [Zentrum fuer Europaeische Wirtschaftsforschung (ZEW), Mannheim (Germany)

    2010-09-15

    The power supply sector in Germany will be faced with many challenges in the near future. On the one hand, there is the dependence on imported energy sources and the impending shortage of fossil fuels with the consequence of dramatic price increases; on the other hand, there are enhanced climate protection efforts both on a national and an international scale. Increasing competition in the power generation sector requires greater efforts as a consequence of increasing deregulation and an increasing need for newly constructed power plants; the latter will depend to a considerable extent on the residual operating time of the existing nuclear power stations. Against this background, the ''Energieprognose 2009'' takes a look at the long-term development of power supply in Germany through 2030. (orig.)

  7. A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

    DEFF Research Database (Denmark)

    Courdent, V.; Grum, M.; Munk-Nielsen, T.

    2017-01-01

    ). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance......Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs......) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar...

  8. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  9. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  10. Analysis and discussion of the most recent forecasts on energy demand for major industrial nations with a view to the avoidance of greenhouse gases

    International Nuclear Information System (INIS)

    Jochem, E.; Herz, H.; Mannsbart, W.

    1993-09-01

    In the future, when individual governments in their negotiations within the framework convention on climate change refer to their national energy demand assessments, any resulting energy forecasts are likely to be criticized in the scientific public because to date most of these energy demand assessments vary greatly in their essential basic assumptions (e.g., the future economic development, the oil price, the development of transportation, and so forth). For this reason the last ''official'' energy demand estimates for each country have been evaluated by a network of competent energy economy institutes in the countries having the highest emissions of climate-relevant gases (USA, Japan, the former Soviet Union, the EC). The work of this network had the following aim: to compare these most recent demand estimates and analyse their differences, to match each other's understanding of the most important basic assumptions and forecasting methods, and to agree on a reference skeleton for demand estimates, by means of which the assessments of different countries could be compared with each other. (orig.) [de

  11. A review of agent-based models for forecasting the deployment of distributed generation in energy systems

    NARCIS (Netherlands)

    Veneman, J.F.; Oey, M.A.; Kortmann, L.J.; Brazier, F.M.; De Vries, L.J.

    2011-01-01

    Agent-based models are seeing increasing use in the study of distributed generation (DG) deployment. Researchers and decision makers involved in the implementation of DG have been lacking a concise overview of why they should consider using agent-based modeling (ABM) for forecasting purposes. Since

  12. Forecasting nuclear power supply with Bayesian autoregression

    International Nuclear Information System (INIS)

    Beck, R.; Solow, J.L.

    1994-01-01

    We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)

  13. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  14. A Tool for Empirical Forecasting of Major Flares, Coronal Mass Ejections, and Solar Particle Events from a Proxy of Active-Region Free Magnetic Energy

    Science.gov (United States)

    Barghouty, A. F.; Falconer, D. A.; Adams, J. H., Jr.

    2010-01-01

    This presentation describes a new forecasting tool developed for and is currently being tested by NASA s Space Radiation Analysis Group (SRAG) at JSC, which is responsible for the monitoring and forecasting of radiation exposure levels of astronauts. The new software tool is designed for the empirical forecasting of M and X-class flares, coronal mass ejections, as well as solar energetic particle events. Its algorithm is based on an empirical relationship between the various types of events rates and a proxy of the active region s free magnetic energy, determined from a data set of approx.40,000 active-region magnetograms from approx.1,300 active regions observed by SOHO/MDI that have known histories of flare, coronal mass ejection, and solar energetic particle event production. The new tool automatically extracts each strong-field magnetic areas from an MDI full-disk magnetogram, identifies each as an NOAA active region, and measures a proxy of the active region s free magnetic energy from the extracted magnetogram. For each active region, the empirical relationship is then used to convert the free magnetic energy proxy into an expected event rate. The expected event rate in turn can be readily converted into the probability that the active region will produce such an event in a given forward time window. Descriptions of the datasets, algorithm, and software in addition to sample applications and a validation test are presented. Further development and transition of the new tool in anticipation of SDO/HMI is briefly discussed.

  15. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  16. Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1 and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-03-01

    Full Text Available Presently, China is the largest CO2 emitting country in the world, which accounts for 28% of the CO2 emissions globally. China’s CO2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO2 emissions in China accurately, considering population, the CO2 emission forecasting model using GM(1,1 (Grey Model and least squares support vector machine (LSSVM optimized by the modified shuffled frog leaping algorithm (MSFLA (MSFLA-LSSVM is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1 model is used to forecast the main influencing factors of CO2 emissions. Finally, taking the forecasting value of the CO2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO2 emissions in China from 2018 to 2025.

  17. Evaluation of Dynamical Downscaling Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Hahmann, Andrea N.; Nielsen, Torben Skov

    accuracy metric evaluated for wind speed data consistently translates to an improvement for wind power. For two time series describing the temporal development of the same variable, though by different means, it is assumed that phase errors account for most of the departure from perfect correlation between...... the two time series. Results on limited-area NWP model performance, with focus on the 12th to 48th forecast hour horizon relevant for Elspot auction bidding on the Nord Pool Spot market [2], are presented....

  18. Measurements in support of wind farm simulations and power forecasts: The Crop/Wind-energy Experiments (CWEX)

    International Nuclear Information System (INIS)

    Takle, E S; Rajewski, D A; Lundquist, J K; Gallus, W A Jr; Sharma, A

    2014-01-01

    The Midwest US currently is experiencing a large build-out of wind turbines in areas where the nocturnal low-level jet (NLLJ) is a prominent and frequently occurring feature. We describe shear characteristics of the NLLJ and their influence on wind power production. Reports of individual turbine power production and concurrent measurements of near-surface thermal stratification are used to turbine wake interactions and turbine interaction with the overlying atmosphere. Progress in forecasting conditions such as wind ramps and shear are discussed. Finally, the pressure perturbation introduced by a line of turbines produces surface flow convergence that may create a vertical velocity and hence a mesoscale influence on cloud formation by a wind farm

  19. Forecasting risks of natural gas consumption in Slovenia

    Energy Technology Data Exchange (ETDEWEB)

    Potocnik, Primoz; Govekar, Edvard; Grabec, Igor [Laboratory of Synergetics, Ljubljana (Slovenia). Faculty of Mechanical Engineering; Thaler, Marko; Poredos, Alojz [Laboratory for Refrigeration, Ljubljana (Slovenia). Faculty of Mechanical Engineering

    2007-08-15

    Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company. (author)

  20. Forecasting risks of natural gas consumption in Slovenia

    International Nuclear Information System (INIS)

    Potocnik, Primoz; Thaler, Marko; Govekar, Edvard; Grabec, Igor; Poredos, Alojz

    2007-01-01

    Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company

  1. Forecasting the demand on solar water heating systems and their energy savings potential during the period 2001-2005 in Jordan

    International Nuclear Information System (INIS)

    Kablan, M.M.

    2003-01-01

    Jordan is an example of a developing country that depends almost exclusively on imported oil. Luckily, Jordan is blessed with good solar energy resources. However, only 24% of Jordanian families are installing solar water heating systems (SWHS). The objective of this research is to forecast the yearly demand on SWHS by the household sector during the period 2001-2005 and to compute the potential energy savings throughout the investigated period due to the use of SWHS. It is found that the net energy collected over the entire investigated period is about 1454.4 million kW h. In addition, the capital savings over the entire investigated period is estimated to be 46.28 million US$ if SWHS are used to heat water instead of the commonly used LPG gas cookers. The results of the research may assist decision makers in the energy sector to implement more comprehensive plans that encourage more families to install SWHS and save on imported oil

  2. Seismic forecast using geostatistics

    International Nuclear Information System (INIS)

    Grecu, Valeriu; Mateiciuc, Doru

    2007-01-01

    The main idea of this research direction consists in the special way of constructing a new type of mathematical function as being a correlation between a computed statistical quantity and another physical quantity. This type of function called 'position function' was taken over by the authors of this study in the field of seismology with the hope of solving - at least partially - the difficult problem of seismic forecast. The geostatistic method of analysis focuses on the process of energy accumulation in a given seismic area, completing this analysis by a so-called loading function. This function - in fact a temporal function - describes the process of energy accumulation during a seismic cycle from a given seismic area. It was possible to discover a law of evolution of the seismic cycles that was materialized in a so-called characteristic function. This special function will help us to forecast the magnitude and the occurrence moment of the largest earthquake in the analysed area. Since 2000, the authors have been evolving to a new stage of testing: real - time analysis, in order to verify the quality of the method. There were five large earthquakes forecasts. (authors)

  3. Wind power forecasting: IEA Wind Task 36 & future research issues

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, J.; Frank, Helmut Paul

    2016-01-01

    the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD...

  4. Short-term natural gas consumption forecasting

    International Nuclear Information System (INIS)

    Potocnik, P.; Govekar, E.; Grabec, I.

    2007-01-01

    Energy forecasting requirements for Slovenia's natural gas market were investigated along with the cycles of natural gas consumption. This paper presented a short-term natural gas forecasting approach where the daily, weekly and yearly gas consumption were analyzed and the information obtained was incorporated into the forecasting model for hourly forecasting for the next day. The natural gas market depends on forecasting in order to optimize the leasing of storage capacities. As such, natural gas distribution companies have an economic incentive to accurately forecast their future gas consumption. The authors proposed a forecasting model with the following properties: two submodels for the winter and summer seasons; input variables including past consumption data, weather data, weather forecasts and basic cycle indexes; and, a hierarchical forecasting structure in which a daily model was used as the basis, with the hourly forecast obtained by modeling the relative daily profile. This proposed method was illustrated by a forecasting example for Slovenia's natural gas market. 11 refs., 11 figs

  5. kosh Terminal Aerodrome Forecast

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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  1. kblf Terminal Aerodrome Forecast

    Data.gov (United States)

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. katy Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kdet Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kbur Terminal Aerodrome Forecast

    Data.gov (United States)

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    Data.gov (United States)

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  1. kfar Terminal Aerodrome Forecast

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  4. klse Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. ksts Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. koth Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kbfl Terminal Aerodrome Forecast

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    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

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    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. krog Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kbjc Terminal Aerodrome Forecast

    Data.gov (United States)

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  2. kphx Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. Review of the study ''Development of the energy markets - energy reference forecast'' from the perspective of brown coal; Bewertung der Studie ''Entwicklung der Energiemaerkte - Energiereferenzprognose'' aus Sicht der Braunkohle

    Energy Technology Data Exchange (ETDEWEB)

    Dyllong, Yvonne [DEBRIV e.V., Koeln (Germany); Maassen, Uwe [Statistik der Kohlenwirtschaft e.V., Koeln (Germany)

    2014-12-15

    In June 2014 the Federal Ministry for Economic Affairs and Energy (BMWi) published an expertise prepared by the EWI, GWS and Prognos institutes entitled ''Development of the energy markets - energy reference forecast''. This study presents probable developments in the energy economy up to the year 2030 (reference forecast) on the assumption of a yet more stringent energy and climate protection policy as well as continuing impediments to its implementation. Complementing the forecast is an outlook up to the year 2050 (scenario of trends). But what do the findings of this expertise say about the future utilization of Germany's greatest domestic energy resource, namely brown coal? This question is addressed in the following article.

  4. Robust forecast comparison

    OpenAIRE

    Jin, Sainan; Corradi, Valentina; Swanson, Norman

    2015-01-01

    Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

  5. The issue of the fourth nuclear power plant and its impact on 3-E problems in Taiwan - empirical evidence from the energy forecasting (EnFore) system

    International Nuclear Information System (INIS)

    Bor, Y.J.; Chou, F.-Y.

    2003-01-01

    Taiwan has placed considerable emphasis on economic, energy and environmental (3-E) problems in recent decades. Following President Chen's inauguration, one particular issue of concern has been the current dispute over the fourth nuclear power plant (FNPP) in northern Taiwan. This dispute has had a serious impact on Taiwan's economy, including its energy structure and general policy towards CO 2 emission controls. It is estimated, for example, that the loss to Taiwan's capital market as a result of the FNPP dispute, reached NT$7 trillion (about US$219 billion) by the end of 2000. If Taiwan Power Company (Taipower) were to replace the nuclear power plant capacity with liquified natural gas generators, average utility prices would go up by around 4.6% over the next 10 years. The alternative would be for low-cost coal-fired power plants to assume the major position in future power generation; however, this could cause significant damage to Taiwan's CO 2 emission control policy. This paper uses an integrated computerized system model of energy forecasting to simulate the complex interrelationship between the various issues. Empirical results reveal that there are no perfect solutions available; thus, this is an important learning process for the government in terms of administration, as well as for other academic studies

  6. Risk-based Operation and Maintenance Approach for Wave Energy Converters Taking Weather Forecast Uncertainties into Account

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kramer, Morten Mejlhede; Sørensen, John Dalsgaard

    2016-01-01

    Inspection and maintenance costs are significant contributors to the cost of energy for wave energy converters. Maintenance can be performed after failure (corrective) or before a breakdown (preventive) occurs. Furthermore, helicopter and boat can be used to transport equipment and personnel to t...

  7. Forecaster Behaviour and Bias in Macroeconomic Forecasts

    OpenAIRE

    Roy Batchelor

    2007-01-01

    This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

  8. Development forecast of renewable energy power generation in China and its influence on the GHG control strategy of the country

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Tong; Xu, Gang; Tian, Longhu; Huang, Qili [National Power Generation Engineering Research Center, National Engineering Laboratory for Biomass Power Generation Equipment, School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206 (China); Cai, Peng [Yantai Longyuan Power Technology Co., LTD, Beijing 100070 (China)

    2011-04-15

    CO{sub 2} emissions of the electricity supply sector in China account for about half of the total volume in the country. Thus, reducing CO{sub 2} emissions in China's electricity supply sector will contribute significantly to the efforts of greenhouse gas (GHG) control in the country and the rest of the world. This paper introduces the development status of renewable energy and other main CO{sub 2} mitigation options in power generation in China and makes a preliminary prediction of the development of renewable energy in the country for future decades. Besides, based on the situation in China, the paper undertakes a comprehensive analysis of CO{sub 2} mitigation costs, mitigation potential, and fossil energy conversation capacity of renewable energy and other mitigation options, through which the influence of renewable energy on the mitigation strategy of China is analyzed. (author)

  9. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  10. Gas demand forecasting by a new artificial intelligent algorithm

    Science.gov (United States)

    Khatibi. B, Vahid; Khatibi, Elham

    2012-01-01

    Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

  11. Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters

    OpenAIRE

    Fusco, Francesco; Ringwood, John

    2010-01-01

    Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if ...

  12. Benefits of up-wave measurements in linear short-term wave forecasting for wave energy applications

    OpenAIRE

    Paparella, Francesco; Monk, Kieran; Winands, Victor; Lopes, Miguel; Conley, Daniel; Ringwood, John

    2014-01-01

    The real-time control of wave energy converters requires the prediction of the wave elevation at the location of the device in order to maximize the power extracted from the waves. One possibility is to predict the future wave elevation by combining its past history with the spatial information coming from a sensor which measures the free surface elevation upwave of the wave energy converter. As an application example, the paper focuses on the prediction of the wave eleva...

  13. How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars. Part 2. Forecasting effects of feebates based on energy-efficiency

    International Nuclear Information System (INIS)

    Haan, Peter de; Mueller, Michel G.; Scholz, Roland W.

    2009-01-01

    In this paper, we simulate the car market in order to forecast the effects of feebate systems based on an energy-labeling scheme using categories A to G. Very fuel-efficient (A) cars receive a cash incentive, highly inefficient (G) cars pay additional fees. Consumers have different price elasticities and behavioral options to react to feebates. They can switch to a smaller sized car, but as energy-efficiency varies widely within size segments, they can also stick to the preferred size class and choose a more efficient (smaller) engine. In addition, previously owned cars influence the next car to be chosen. We use an agent-based microsimulation approach particularly suited to predict environmental and market effects of feebates. Heteorogeneous agents choose from a choice set drawn from a detailed fleet of new cars. Incentives of EUR2000 for A-labeled cars induce an additional rated CO 2 emission decrease of new car registrations between 3.4% and 4.3%, with CO 2 abatement costs between EUR6 and EUR13 per ton, and otherwise little undesired market disturbance. The risk of rebound effects is estimated to be low. After adopting the frequencies of consumer segments to a given country, the model presented is applicable to all European car markets. (author)

  14. National Forecast Charts

    Science.gov (United States)

    code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

  15. Are Forecast Updates Progressive?

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)

    2010-01-01

    textabstractMacro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average,

  16. A methodology for Electric Power Load Forecasting

    Directory of Open Access Journals (Sweden)

    Eisa Almeshaiei

    2011-06-01

    Full Text Available Electricity demand forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting methods were developed, none can be generalized for all demand patterns. Therefore, this paper presents a pragmatic methodology that can be used as a guide to construct Electric Power Load Forecasting models. This methodology is mainly based on decomposition and segmentation of the load time series. Several statistical analyses are involved to study the load features and forecasting precision such as moving average and probability plots of load noise. Real daily load data from Kuwaiti electric network are used as a case study. Some results are reported to guide forecasting future needs of this network.

  17. Energy forecasts for France. A scenario having a particular tendency; Perspectives energetiques pour la France. Un scenario tendanciel

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2000-03-01

    This document presents in a synthetic form a description of the energy tendency ( supplies and demands) for 2010-2020. This description has been requested by the Direction of the Energy and the raw materials (DGEMP), from may to december 1999. The proposed scenario is based on a simulation and hypothesis which don't take into account the future policy and regulations as for example the decisions of the fight program against the climatic change. Tables and graphs are provided. (A.L.B.)

  18. Forecasting freight flows

    DEFF Research Database (Denmark)

    Lyk-Jensen, Stéphanie

    2011-01-01

    Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

  19. Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models

    International Nuclear Information System (INIS)

    Jackson, Jerry

    2010-01-01

    Electric utilities and regulators face difficult challenges evaluating new energy efficiency and smart grid programs prompted, in large part, by recent state and federal mandates and financial incentives. It is increasingly difficult to separate electricity use impacts of individual utility programs from the impacts of increasingly stringent appliance and building efficiency standards, increasing electricity prices, appliance manufacturer efficiency improvements, energy program interactions and other factors. This study reviews traditional approaches used to evaluate electric utility energy efficiency and smart-grid programs and presents an agent-based end-use modeling approach that resolves many of the shortcomings of traditional approaches. Data for a representative sample of utility customers in a Midwestern US utility are used to evaluate energy efficiency and smart grid program targets over a fifteen-year horizon. Model analysis indicates that a combination of the two least stringent efficiency and smart grid program scenarios provides peak hour reductions one-third greater than the most stringent smart grid program suggesting that reductions in peak demand requirements are more feasible when both efficiency and smart grid programs are considered together. Suggestions on transitioning from traditional end-use models to agent-based end-use models are provided.

  20. The new IEA Wind Task 36 on Wind Power Forecasting

    DEFF Research Database (Denmark)

    Giebel, Gregor; Cline, Joel; Frank, Helmut

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind E...... forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions....